issn: 2086-3195 - fis.um.ac.idfis.um.ac.id/wp-content/uploads/2015/11/aceh-proceeding... · fikri...

ISSN: 2086-3195

PROCEEDINGS

5th Annual International Workshop & Expo

on Sumatra Tsunami Disaster & Recovery

(AIWEST-DR 2010)

Hermes Palace Hotel, Banda Aceh, Indonesia

November 23-25, 2010

Editors:

Dr. Agussabti

Dr. Syamsidik

Dr. Nasaruddin

Dr. Eldina Fatimah

Dr. Azmeri

Dr. Fitri Arnia

Preface

Distinguished guests and workshop participants,

It is a great honor to Tsunami and Disaster Research Center (TDMRC) of Syiah Kuala

University to host the 5th

Annual International Workshop and Expo on Sumatera Tsunami

Disaster Recovery (AIWEST-DR). This event would be a forum for all disaster practitioners

and scientists to deliver their ideas and experiences to save lives and environment from any

disaster. Learning from five consecutive events of AIWEST-DR, the TDMRC, once again,

invites all parties to sit together in this event, bringing positive measures and ideas to reduce

the risk of disaster.

The world has encountered serious changes in terms of disaster frequency trends and number

of casualties at every disaster event. The reflection is a stark proof found in recent

Indonesia’s problems with Merapi volcano, Mentawai Tsunami, and recent Wasior flood

disasters. Numbers of losses are mounted and the time is extremely limited. It is duty of all of

us to be immediately and to be properly offer solutions for reducing the losses. Therefore, the

AIWEST-DR is expected deliberately to discuss the problems and the solutions.

It is hoped that this workshop will stimulate further efforts that result in better disaster

management strategies and will yield initiatives toward sustaining the learning and

knowledge sharing processes, integrating the international lessons, and providing a sound

platform for a sustainable international collaboration in Disaster Risk Reduction (DRR), with

a particular reference to tsunami-related issues, all of which will hopefully help establish

disaster-resilient communities.

We are also pleased to inform this forum that more than 73 technical papers from 13

countries are going to be disseminated in this year workshop. In addition, various important

and interesting issues related to disaster risk reduction (DRR) and research on Tsunami will

be discussed in two panel sessions.

On behalf of the TDMRC, I would like to express our sincere appreciation to the Government

of Aceh, Multi Donor Thrust Funds (MDTF), and UNDP, for the support and good

cooperation afforded to us leading to the proper organization of this workshop. Special thanks

are also extended to our fellow national and international counterparts for their collaborations

and continuous support in this effort.

Please enjoy and have a fruitful workshop!

Thank you.

Dr. Nasaruddin

Organizing Committee

Chairman

Committees

Steering Committees

- Prof. Yasuo Tanaka (Kobe University, Japan)

- Dr. M. Dirhamsyah (TDMRC-Syiah Kuala University, Indonesia)

- Dr. Idwan Suhardi (Ministry of Research And Technology, Indonesia)

- Prof. Ahmad Khairy (Universiti Teknologi Malaysia, Malaysia)

- Prof. Friedemann Wenzel (CEDIM, Karlsruhe University, Germany)

- Prof. Koh Hock Lye (Universiti Sains Malaysia, Malaysia)

- Prof. Louise Comfort (University of Pittsburgh, USA)

- Prof. Philip F.Liu (Cornell University, USA)

- Prof. Frank Thomalla (MacQuarie University, Australia)

- Mr. Tony Elliott (ICG/IOTWS, UNESCO, Australia)

- Dr. Stephen Rice (New Mexico State University, USA)

- Prof. Kerry Sieh (NUS, Singapore )

- Prof. Samsul Rizal (Syiah Kuala University, Indonesia)

- Prof. Syamsul Rizal (Syiah Kuala University, Indonesia)

- Prof. Yuwaldi Away (Syiah Kuala University, Indonesia)

- Dr. Musri (Syiah Kuala University, Indonesia)

- Dr. Danny Hilman (LIPI, Indonesia)

- Dr. I Wayan Sengara (Bandung Institute of Technology, Indonesia)

- Dr. M. Syahril Badri (Bandung Institute of Technology, Indonesia)

- Teuku Faisal Fathani, Ph.D (Gadjah Mada University, Indonesia)

- Ir. Hira Laksmiwati, M. Sc (Bandung Institute of Technology, Indonesia)

Organizing Committees

Chairman : Dr. Nasaruddin

Secretary : Dr. Syamsidik

Treasurer : Erwin, S.Sos

Program Advisor : Dr. M. Ridha

Liaison Officer : T. Alvisyahrin, Ph.D

Program and Schedule

• Didik Sugiyanto (Head) • Rina Suryani Oktari, SKep

• Fahmi Zubir, M.Pd

• Razali Amna, S.Si

• Irma Setyawati, S.T

• Cut Marlina, ST

• Veri Yanti, ST

Logistics

• Dr. Khairuddin (Head) • Wan Akmal Indrawan, S.T

• Imam Munandar, S.T

• Diky Juliandi, S.T

• Khairul Anwar, ST

• T. Mahlil, ST

• Faisal Ilyas, SE

Special Event and Promotion

• Dr. Eldina Fatimah (Head) • Zulchaidir, S.Si

• T. Ikmal, S.T

• Fauziah, S.T

• Eka Putra, SP

• Suhendra, SH

• Uswatun Ukhra, SP

• Syarifah Fadilla, ST

Publication and Documentation

• Dr. Khairul Munadi (Head) • Hendra Syahputra, MM, M.Sc

• Nurul Isl, S.T

• Fachrul Fikri, S.T

• Azhari, S.Si

• Nasliati

• Juniawan, S.Si

Secretariat

• Rudi Kurniawan, M.Sc. (Head) • Drs. Ridwan Mahmud

• Nani Eliza, S.Si

• Nida Silmina, S.Pdi.

• Rini Ermiana, S.P

• Miranda Sari, SE. Ak

• Erisman Hidayat, SE

Editorial Board

• Dr. Agussabti (Head) • Dr. Abdullah

• Dr. Azmeri

• Dr. Fitri Arnia

Cooperating Organizations :

• Ministry of research and Technology, Indonesia

• National Agency for Disaster Management, Indonesia

• Indonesian Institute of Sciences

• Agency for Meteorology, Climatology, and Geophysics Indonesia

• Aceh Agency for Disaster Management

• Bappeda Aceh, Indonesia

• Bandung Institue of Technology (ITB), Indonesia

• Earthquakes and Megacities Initiatives (EMI)

• Center for Disaster Management and Risk Reduction Technology (CEDIM),

Karlsruhe-Germany

• Faculty of Engineering and Human Ecology, University Putra Malaysia, Malaysia

• National Tsunami Warning Center, Malaysia

• Pacific Tsunami Museum of Hawaii

• Tsunami Research Center, University of New South Wales

• United Nations International Strategy for Disaster Reduction (UNISDR) Asia

Region, Bangkok- Thailand

• International Centre for Water Hazard and Risk Management (ICHARM), Japan

Principal Organizers :

• Ministry of Home Affairs, Indonesia

• Aceh Government

• Multi Donor Funds (MDF)

• DRR-A UNDP

• Research Centre for Urban Safety and Security (RCUSS)-Kobe University, Japan

List Content

Invited Papers

Long-Term Post-Tsunami Recovery in Aceh: Survey of Community Perspective

on Recovery Priorities

Krishna S. Pribadi (Center for Disaster Mitigation, Bandung Institute of Technology, Bandung,

Indonesia)

Muhammad Dirhamsyah (Tsunami and Disaster Mitigation Research Center, Syiah Kuala

University, Banda Aceh, Indonesia)

Bayu Novianto (Center for Disaster Mitigation, Bandung Institute of Technology, Bandung,

Indonesia)

The Determination of Warning Criteria for Rainfall-induced Shallow Landslide by

Deploying a Real-time Monitoring System

Teuku Faisal Fathani (Faculty of Engineering, Gadjah Mada University, Indonesia)

Dwikorita Karnawati (Faculty of Engineering, Gadjah Mada University, Indonesia)

Fikri Faris (Faculty of Engineering, Gadjah Mada University, Indonesia)

1

6

Disaster Recovery Activities

Rehabilitation and Conservation of Coastal Environtments after Tsnami Recovery trough

Microfinance and Revolving Fund (Study Case in Pidie Jaya, Aceh)

Basuki Rahmad (The Indonesian Biodiversity Foundation, Indonesia)

Diah Rahayuningsih Sulistiowati (The Indonesian Biodiversity Foundation, Indonesia)

Ali Hasan Safari (The Indonesian Biodiversity Foundation, Indonesia)

14

Pre and Post-Tsunami Energy Loss Analysis of Banda Aceh’s Electricity Distribution

Network System

17

Syukri Yadin (Department of Electrical Engineering – Syiah Kuala University, Indonesia)

Ramon Zamora (Dept. of Electrical & Comp. Engineering – Mississippi State University, USA)

Rizki Muttaqin (Department of Electrical Engineering – Syiah Kuala University, Indonesia)

Corrosion Risk Assessment of Existing and New Reinforced Concrete Buildings after Six

Years Tsunami Aceh 2004

M. Ridha (Tsunami & Disaster Mitigation Research Center, Syiah Kuala University,Indonesia)

Syarizal Fonna (Department of Mechanical Engineering, Syiah Kuala University, Indonesia)

Syifaul Huzni (Department of Mechanical Engineering, Syiah Kuala University, Indonesia)

Israr (Department of Mechanical Engineering, Syiah Kuala University, Indonesia)

Zulfan Fauzi (Department of Mechanical Engineering, Syiah Kuala University, Indonesia)

A.K. Arifin (Dept. of Mechanical and Materials Engineering, UKM, Malaysia)

25

Gender Relations in Acehnese Economic Activities (Case Study on Fishery Economic

Activities in Meunasah Keudee Village Mesjid Raya Subdistrict Aceh Besar District)

Evi Lisna (Syiah Kuala University, Indonesia)

Agussabti (Tsunami & Disaster Mitigation Research Center, Syiah Kuala University)

Safrida (Syiah Kuala University, Indonesia)

31

Disaster Preparedness and Risk Management

Implementing Youth-Based Disaster Preparedness Programs: A Case Study of IDDI

Program in Santo Domingo, Dominican Republic

Erin Joakim (University of Waterloo, Canada)

35

Disaster Preparedness for Young Children

Syifa Andina (Plan Internasional, Indonesia)

41

Identification of Post Tsunami Disaster Training and Its Impact to School Community’s

Preparedness of Disaster Risk Reduction in Aceh Province

Khairuddin (Tsunami & Disaster Mitigation Research Center, Syiah Kuala University,

Indonesia)

Ngadimin (Faculty of Teacher Training & Education, Syiah Kuala University, Indonesia)

Sri Adelila Sari (Tsunami & Disaster Mitigation Research Center, Syiah Kuala University,

Indonesia)

Melvina(Faculty of Teacher Training & Education, Syiah Kuala University, Indonesia)

Tati Fauziah (Faculty of Teacher Training & Education, Syiah Kuala University, Indonesia)

46

Animation Viewer Development as a Method of Disaster Preparedness and Education

Dr. Yozo GOTO (Project Researcher, Earthquake Research Institute, University of Tokyo,

Tokyo-Japan)

Muzailin AFFAN (Tsunami Disaster and Mitigation Research Center, Banda Aceh-Indonesia)

Yudha NURDIN (Tsunami Disaster and Mitigation Research Center, Banda Aceh-Indonesia)

Diyah K. YULIANA (Researcher, Agency for The Assessment and Application of Technology

(BPPT), Jakarta-Indonesia)

50

Risk Assessment of Long-Term Climate Variability on the Production of Maize in Porac,

Pampanga

Karmella Eunice Espana (De La Salle Araneta University, Philippines)

Mary Lou Tinonas (De La Salle Araneta University, Philippines)

Angelique Manalo (De La Salle Araneta University, Philippines)

Elbert Barayoga (De La Salle Araneta University, Philippines)

Glenn Banaguas (Manila Observatory/Ateneo De Manila University, Philippines)

56

Community-Based Disaster Risk Management (CBDRM): An Elucidation to the Climate

Variability on Coastal Communities

Abigail Salva (De La Salle Araneta University, Philippines)


61

Measuring Progress Towards Implementation of Key aspects of the Hyogo Framework

Priorities for Action at local level

Hepi rahmawati (YAKKUM Emergency Unit, Indonesia)

70

Disaster Evacuation Simulation with Multi-Agent System Approach Using NetMAS for

Contigency Planning

Yudha Nurdin (Tsunami & Disaster Mitigation Research Center, Syiah Kuala University,

Indonesia)

76

Diyah Yuliana (Agency for The Assessment and Application of Technology (BPPT), Indonesia)

Itsuki Noda (Advanced Industrial Science and Technology (AIST), Tsukuba-Japan)

Shunsuke Soeda (Advanced Industrial Science and Technology (AIST), Tsukuba-Japan)

Tomohisa Yamashita (Advanced Industrial Science and Technology (AIST), Tsukuba-Japan)

Institutional And Legislative System For Disaster Management In Aceh Province: Past,

Present And The Future

Asmadi Syam (Aceh Disaster Management Agency (BPBA), Indonesia)

M.Dirhamsyah (Tsunami & Disaster Mitigation Research Center,Syiah Kuala University,

Indonesia)

Juniawan Priyono (Tsunami & Disaster Mitigation Research Center, Syiah

Kuala University, Indonesia)

Mapping the Needs of the Governments in City District for Disaster Reduction(Studies in

Regional Implementation Unit in Eight Cities District of Aceh Province)

Faisal Ilyas, (Advocacy, Education and Training Division, Tsunami and Disaster Mitigation

Research Center- Syiah Kuala University, Indonesia)

Sri Adelila Sari (Advocacy, Education and Training Division,Tsunami and Disaster Mitigation

Research Center- Syiah Kuala University, Indonesia)

From Aceh To The World Lesson Learnt from the Establishment of Tsunami and Disaster

Mitigation Research Center (TDMRC) Syiah Kuala University

Hendra Syahputra, (Tsunami and Disaster Mitigation Risearch Center- Syiah Kuala University,

Indonesia)

80

84

87

Optimal Logistic Distribution for Mitigation and Disaster Management in Banda Aceh

Taufiq Abdul Gani (Research Center for Computational Engineering, Department of Electrical

Engineering, Syiah Kuala University, Indonesia)

92

Role of Local Wisdom in Acceleration of Disaster Risk Reduction in Aceh (Kabupaten

Simeulue Case)

Hendra Syahputra (Tsunami & Disaster Mitigation Research Center, Syiah Kuala University,

Indonesia)

99

Community preparedness planning in Meuraxa Sub District-Banda Aceh

Variadi (Gajah Mada University, Indonesia)

Abdillah imron Nasution (Karst Aceh, Indonesia)

101

Geotechnical and Hydro-dynamical Modelling

Landslide hazard assessment using analytic hierarchy processing (AHP) and geographic

information system in Kaligesing mountain area of Central Java Province Indonesia

Syamsul Bachri (State University of Malang, Indonesia)

Rajendra P Shresta (School of Environment, Resources and Development, Asian Institute of

Technology, Thailand)

107

The utilization of ultrasonic wave modulated by Gamma ray (γ) for detecting victims

through carbon-14 (C-14) from inside as navigational aids in evacuation of Disaster victims

Umar Sidik (Research Center for Noise/Vibration Control-USU Medan, Indonesia)

Ikhwansyah Isranuri (Research Center for Noise/Vibration Control-USU Medan, Indonesia)

113

Study of Use Structure Light Systems for 3D Scanning Rehabilitation Assistive Device

Irwansyah (Mechanical Engineering Dept. Syiah Kuala University, Indonesia)

Udink Aulia (Mechanical Engineering Dept. Syiah Kuala University, Indonesia)

M. Hafidz Mubarrak (Mechanical Engineering Dept. Syiah Kuala University, Indonesia)

118

Preliminary Laser Induced Plasma Spectroscopy (LIBS) Study Towards Quick Inspection

of Building Quality in Disaster Vulnerable Regions

Nasrullah Idris (Department of Physics, Faculty of Mathematics and Natural Sciences, Syiah


121

Disaster Medicine, Nursing/Mental Health

Challenge of Establishing Hospital Disaster Plan

Sari Mutia Timur (YAKKUM Emergency Unit, Indonesia)

125

Puskesmas Disaster Preparedness After the Earthquake in Padang Pariaman,

West Sumatra, Indonesia

Ahmad Fuady (Faculty of Medicine, University of Indonesia)

Nuri Purwito Adi (Faculty of Medicine, University of Indonesia)

Trevino Pakasi (Faculty of Medicine, University of Indonesia)

Muchtaruddin Mansyur (Faculty of Medicine, University of Indonesia)

132

Risk Affected Prevention of Osteoporosis Due To Depression Among The Elderly in

Post-Tsunami Aceh

Ari Istiany (Jakarta State University, Indonesia)

135

Detection of Developmental Disorder, Behavioral Disorder, and Depression in

Post-Earthquake Children

Yogi Prawira (Department of Public Health, Faculty of Medicine, University of Indonesia Cipto

Mangunkusumo Hospital Jakarta, Indonesia)

Intan AP Tumbelaka (Department of Public Health, Faculty of Medicine, University of

Indonesia Cipto Mangunkusumo Hospital Jakarta, Indonesia)

Ali K Alhadar (Department of Public Health, Faculty of Medicine, University of Indonesia Cipto


Erwin L Hendrata (Department of Public Health, Faculty of Medicine, University of Indonesia

Cipto Mangunkusumo Hospital Jakarta, Indonesia)

Renno Hidayat (Department of Public Health, Faculty of Medicine, University of Indonesia


Dave Anderson (Department of Public Health, Faculty of Medicine, University of Indonesia


Trevino Pakasi (Department of Public Health, Faculty of Medicine, University of Indonesia


Bernie Endyarni (Department of Public Health, Faculty of Medicine, University of Indonesia


Rini Sekartini (Department of Public Health, Faculty of Medicine, University of Indonesia Cipto


138

Performance and Trauma Recovery in Aceh

Kimberly Twarog (University of California, Los Angeles (UCLA), United States)

143

Knowledge, Self-Preparedness and Perceived Skills Regarding Tsunami Disaster

Nursing Among Public Health Nurses Working in Tsunami Affected Area of Aceh

Province, Indonesia

Maulidar (Faculty of Nursing, Prince of Songkla University, Thailand)

Urai Hatthakit (Faculty of Nursing, Prince of Songkla University, Thailand)

Aranya Chaowalit (Faculty of Nursing, Prince of Songkla University, Thailand)

146

Environmental Management and Disaster Resilience

The Risk Analysis of Climate Variability on the Rice Production in Cabanatuan,

Nueva Ecija

Adrianne Angelica Reyes (De La Salle Araneta University,Manila Observatory/Ateneo De

Manila University Philippines)

Zarina Aiesa Garcia (De La Salle Araneta University,Manila Observatory/Ateneo De Manila

University Philippines)

Toni Rose Poso (De La Salle Araneta University,Manila Observatory/Ateneo De Manila


Maricar Alcazar (De La Salle Araneta University,Manila Observatory/Ateneo De Manila


Glenn Banaguas (De La Salle Araneta University,Manila Observatory/Ateneo De Manila


152

Assessing the Resilience of Agricultural Fields Affected by the 2004 Tsunami

Disaster in Tamil Nadu, India

Takashi Kume (Research Institute for Humanity and Nature, Japan)

Chieko Umetsu (Research Institute for Humanity and Nature , Japan)

K. Palanisami (International Water Management Institute, India)

157

Determination of Arsenic, Iron, and Manganese in Water Spinach

Sri Adelila Sari (Tsunami & Disaster Mitigation Research Center, Syiah Kuala University)

Nur Suraiya (Department of Chemical Education, Faculty of Teacher Training & Education,

Syiah Kuala University, Indonesia)

Erlida Wati (Department of Chemical Education, Faculty of Teacher Training & Education,

Syiah Kuala University, Indonesia)

162

Community Resilience to Natural Hazards and Climate Change Impacts: A Review of

Definitions and Operational Frameworks

Riyanti Djalante (Department of Environment and Geography, Macquarie University, Sydney,

Australia)

Frank Thomalla (Department of Environment and Geography, Macquarie University, Sydney,

Australia)

164

Resilience of Tsunami Affected Farm Households in Coastal Region of Tamil Nadu, India

Chieko Umetsu (Research Institute for Humanity and Nature, Japan)

Thamana Lekprichakul (Research Institute for Humanity and Nature , Japan)

K. Palanisami (International Water Management Institute, India)

179

M. Shanthasheela (Tamil Nadu Agricultural University, India)

Takashi Kume (Research Institute for Humanity and Nature, Japan)

Reef Resilience Status Of Northern Acehnese Reef

Edi Rudi (Department of Biology, Syiah Kuala University, Indonesia)

Nur Fadli (Dept. of Marine Science, Syiah Kuala University, Indonesia)

183

A Scenario Study of Tsunami Induced Inundation for Pariaman, Indonesia

Zhenhua Huang (School of Civil and Environmental Engineering, Nanyang Technological

University, Singapore, Singapore)

Qiang Qiu (Earth Observatory of Singapore, Singapore)

Ksnowidjaja Megawati (Nanyang Technological University, Singapore, Singapore)

Kerry E.Sieh (Earth Observatory of Singapore, Singapore)

Danny H. (Laboratory for Earth Hazards, LIPI, Indonesia)

186

Poverty: A Critical Impact of Climate Change in the Philippine Coastal Sector

Dane Agatha Lorica (De La Salle Araneta University, Philippines)


191

Modeling Climate Change Impact on Water Balance in the Krueng Jreu Sub Watershed of

Aceh Besar

Teuku Ferijal (Department of Agricultural Engineering, Syiah Kuala University, Indonesia)

197

Analysis of Well-Water Quality Based on Physical Parameters in Kutaraja Sub-District,

Banda Aceh

Nazli Ismail (Physics Department, Faculty of Mathematics and Natural Sciences, Syiah Kuala

University, Indonesia)

Nurjannah (Physics Department, Faculty of Mathematics and Natural Sciences, Syiah Kuala


Kurnia Lahna (Physics Department, Faculty of Mathematics and Natural Sciences, Syiah Kuala


203

Mercury Contamination in the Atmosphere and River Water by Small-Scale Gold

Mining Plants in Aceh Province, Indonesia

Syamsidik (Tsunami & Disaster Mitigation Research Center, Syiah Kuala University, Indonesia)

208

Geohazard, Database, and Risk Analysis

Utilization of SeaWIFS Remote Sensing Data for Identifying Fish Capture Area in Aceh

Sea Waters

Khairul Munadi (Tsunami & Disaster Mitigation Research Center, Syiah Kuala University,

Indonesia)

Fardhi Adria (Tsunami & Disaster Mitigation Research Center, Syiah Kuala University,

Indonesia)

212

Banda Aceh City - Building Damage Information System (Bdis) After Sumatera

(Indonesia) Earthquake 2004

Edy Irwansyah (Department of Computer Science, Faculty of Computer Studies, BINUS

University) Shahnawaz (Zentrum für GeoInformatik (Z_GIS), Paris-Lodron-Universität

Salzburg)

218

Design and Implementation of Earthquake Information and Tsunami Early Warning

Dissemination System for Aceh Province

Muhammad Dirhamsyah (Tsunami & Disaster Mitigation Research Center, Syiah Kuala

University, Indonesia) Khairul Munadi (Tsunami & Disaster Mitigation Research Center, Syiah


Nasaruddin (Tsunami & Disaster Mitigation Research Center, Syiah Kuala University,

Indonesia)

Yudha Nurdin (Electrical Engineering Departement of Syiah Kuala University, Indonesia)

Aulia Perkasa (Electrical Engineering Departement of Syiah Kuala University, Indonesia)

223

Design of Disaster Risk Management Information System in Aceh Province

Nasaruddin (Tsunami & Disaster Mitigation Research Center, Syiah Kuala University,

Indonesia)


Indonesia)

Muhammad Dirhamsyah (Tsunami & Disaster Mitigation Research Center, Syiah Kuala


229

Developing Drr Knowledge Resource Center: A Post- Tsunami Initative in Aceh

Hendra Syahputra (The division of Knowledge Management TDMRC Unsyiah,Indonesia)

235

Aceh Tsunami Digital Repository: Sustainable Information in the Development after

Rehabilitation-Reconstruction of Aceh

Nurul Islami (Tsunami & Disaster Mitigation Research Center, Syiah Kuala University,

Indonesia)

Hendra S (Tsunami & Disaster Mitigation Research Center, Syiah Kuala University, Indonesia)

241

The Use of Web-Based Visualization Models Information In Disaster Risk Reduction

(Sample: The Data and Information of Aceh Disaster)

Fachrul Fikri (Tsunami & Disaster Mitigation Research Center, Syiah Kuala University,

Indonesia)

Irma Setyawati (Tsunami & Disaster Mitigation Research Center,Syiah Kuala University,

Indonesia)


244

The Implementation Concept of Integrated Knowledge Management :Managementof Data,

Information, and knowledge for Disaster Risk Reduction (DRR)



Indonesia)

247

Mapping Soil Moisture in Darkhan’s Agricultural Land, Mongolia

Orn-uma Polpanich (Stockholm Environment Institute-Asia,Bangkok Thailand)

250

Knowledge Management in Disaster Risk Reduction: An Implementation Framework


Indonesia)

255

Analysis of Aceh Disaster Using DIBA Data

Irma Setyawati (Tsunami & Disaster Mitigation Research Center, Syiah Kuala University,

Indonesia)

Alleviation Of Heavy Metal Toxicity (Mn) By Organic Matter In Tsunami Area

Dj. Rosmaidar (Agrotechnology Department, Faculty of Agriculture Universitas Syiah Kuala,

Banda Aceh, Indonesia)

J.Shamshuddin (Land Managemant Department, Faculty of Agriculture, Universiti Putra

Malaysia, Serdang, Selangor, Malaysia)

Tj. Khamzurni ( Balai Penyuluhan Pertanian, Montasik, Aceh Besar, Indonesia)

M.A. Mawardah (Balai Penyuluhan Pertanian, Montasik, Aceh Besar, Indonesia)

263

267

5th Annual International Workshop & Expo on Sumatra Tsunami Disaster & Recovery 2010

107

Landslide hazard assessment

using analytic hierarchy processing (AHP) and

geographic information system

in Kaligesing mountain area of Central Java

Province Indonesia

1Syamsul Bachri and

2Rajendra P Shresta

1Departement of Geography, Faculty of Social Science, State University of Malang

Jalan Semarang 5, Malang 65145, Indonesia 2School of Environment, Resources and Development, Asian Institute of Technology

58 Moo 9, Km. 42, Paholyothin Highway, Klong Luang, Pathumthani 12120 Thailand

Email: [email protected],

[email protected]

Abstract - Landslide is type of natural disaster which may

cause huge losses of live and properties. Many landslides

triggering factors found in Kaligesing make this zone as

landslide prone area in Indonesia. Zoning hazard area was a

solution to assess landslide disaster since there is still a great

danger of further landslides in the region and also it is strongly

linked with spatial issues. The combination of GIS and

Analytic Hierarchy Process (AHP) are used to create landslide

hazard zone in this study. Factors, such as landform, land

utilization, slope steepness, soil texture and lithology are

considered for use in AHP through pair-wise matrix. The

output of calculation was validated with present landslide

location. Based on the judgment matrix and calculation, the

result showed ë max = 5.406256, the feature vector of

normalization: F= 0.4042，，，，0.2746，，，，0.2018，，，，0.0845，，，，0.0349).

In this calculation, RI =1.12. According to relational formula,

CI=0.101564. A consistency ratio (CR) was computed to verify

that the consistency of matrix. CR value of 0.090682, meant

that the pair-wise matrix is consistent (threshold CR<0.10) and

can be used for assigning the criteria weight. Spatial

distribution of the susceptibility classes of landslide in

Kaligesing showed that more than 40% of the study area was

categorized as landslide prone area with moderate

susceptibility and 30.05% falling on high susceptibility class,

while the rest 20.78% was categorized as less susceptibility

class.

Key word: Landslide, AHP, GIS, Kaligesing, Java.

I. INTRODUCTION

Currently around 70 percent the world’s population lives

are affected by natural disaster, such as earthquake, flood

and landslide [1]. During 1991-2005, EMDAT reported

total amount of economic lost due to natural disaster as

379.15 US $ billion in developing countries [2]. Comparing

with other part of continent, Asia is the world’s most

disaster prone region. Geological disasters like landslide

hold second position in number of occurrences after Hydro-

meteorological disaster. Several studies showed that

disasters are likely to occur due to their environmental,

climatic and geographical condition. In particular, landslide

will occur in more intensive scale in countries which has

mountain geographic characteristic area.

Landslide is a natural disaster which may cause huge

losses of lives and property. The occurrence of landslide is

influenced by many factors, naturally occurring or by

human activities. Natural landslide may occur by collapses.

It is triggered by physical condition such as topography,

climate, vegetation, land use and earthquake. It is also

commonly occurred due to over human exploitation, such as

forest logging, over grazing, etc. Moreover, landslide often

occurs in mountainous area which has low stability slope.

Many landslide triggering factors are found in Indonesia

that makes Indonesia as the second highest number of

landslide in the world [3]. According to [4] there are 20

regions in Daerah Istimewa Yogyakarta (DIY) province -

Central Java province and 29 regions in East Java province

which are categorized as susceptible to landslide hazards.

All of the areas were witnessed mass movement and rock

movement. As it is mentioned by [5], most of the hilly areas

in Java are susceptible to landslide. The landslides in Java

Island increase over time [6]. The occurrences of landslide

during 1990 and 2005 caused the death of 1,000 people. The

highest loss was incurred in 2005 that victimized 118

individuals. Furthermore, data showed by [7] the extreme

event was in January 2006 causing death of 142 people and

damaging 182 houses; and landslide in September 2000 in

Purworejo caused death of 44 people and 20 people were

injured as well as 77 houses were damaged [8]

Kaligesing, sub-district in Central Java Province,

Indonesia, is known as one of the landslide prone area [9].

Most part area of Kaligesing sub-district is upland area

which varies in lithology, geomorphology and hydrology.

Due to these factors, this area is susceptible to landslide

hazards. More than 10 occasion of landsli

[10]. Generally, three cases of landslide occur in each year

[11]. The degree of damage was different at different areas

on houses and road network. The average indemnification

was about 1,000,000-100,000,000 IDR for eac

Management of land resources is very important due to

the susceptibility of this area. In order to curb and reduce

the impacts of landslide also to be more effective to

exploitation the land, landslide hazard zoning can be one of

the solutions. Combination of GIS and AHP are the

effective method for hazard assessment, GIS has powerful

for spatial analysis while AHP has certain advantages in

multi-index integrated evaluation.

II. METHODOLOGY

A. Description of study area

The research area is located in Kaligesing sub

Purworejo Regency, Central Java Province Indonesia

(Fig.1). It lies between South Latitude 7º 50’ 34”

45” and East Longitude 110º 07’ 46”

Kaligesing sub-district has 21 villages, namely: Village

Somongari, Jati Rejo, Dono Rejo, Hulosobo, Kali Harjo,

Tlogoguwo, Kali Gono, Jelok, Kedung Gubah, Purbo

Wono, Pandan Rejo, Ngaran, Tawang Sari, Gunung Wangi,

Tlogo Rejo, Sudogoro, Tlogo Bulu, Hardi Mulyo,

Sumowono, Pucung Roto, and Ngardi Rejo. The total a

of the study area is approximately 7,472.89 ha with total

population 35,895 citizens. Kaligesing sub

between Purworejo City and Yogyakarta Province. Due to

improvement of roads network, the development process in

this area has increased particularly along the roads.

Kaligesing sub district is an important region for socio

economic development particularly fruit production in

Purworejo district Central Java province Indonesia.

Unfortunately, Kaligesing is a landslide prone area. This

study area represents characteristic feature of most of

upland area in Central Java, Indonesia.

The climate of the study area is mostly sub

condition. Based on Oldeman classification, Kaligesing sub

district is categorized as C3 class climate. I

rainfall intensity, 200 mm in wet month and 100 mm in dry

month. The average amount of rainfall in study area is about

2000 to 5000 mm in a year. Based on Oldeman

classification, wet condition prevails in Kaligesing

months and there is dry condition for 2

So, 5-6 months are prone for occurrence of landslides.

condition represent of high perception during the year.

Based on geology map sheet Yogyakarta scale 1:100,000,

the study area lies in three major rock fo

Andesit Van bemmelen formation, Alluvium formation and

Jonggrangan formation. It was dominated

Van bemmelen formation which formed during Oligocene

up to first Miocene. This formation

andesitic, andicitist lava flow and tuff. The rock material are

5th Annual International Workshop & Expo on Sumatra Tsunami Disaster & Recovery

Due to these factors, this area is susceptible to landslide

ccasion of landslide occurs in 2005

. Generally, three cases of landslide occur in each year

. The degree of damage was different at different areas

on houses and road network. The average indemnification

100,000,000 IDR for each damage.

Management of land resources is very important due to

the susceptibility of this area. In order to curb and reduce

the impacts of landslide also to be more effective to

exploitation the land, landslide hazard zoning can be one of

Combination of GIS and AHP are the

effective method for hazard assessment, GIS has powerful

for spatial analysis while AHP has certain advantages in

II. METHODOLOGY

located in Kaligesing sub-districts

Purworejo Regency, Central Java Province Indonesia

ig.1). It lies between South Latitude 7º 50’ 34” – 7º 51’

45” and East Longitude 110º 07’ 46” – 110º 08’ 20”.

district has 21 villages, namely: Village

Somongari, Jati Rejo, Dono Rejo, Hulosobo, Kali Harjo,

Tlogoguwo, Kali Gono, Jelok, Kedung Gubah, Purbo

Wono, Pandan Rejo, Ngaran, Tawang Sari, Gunung Wangi,

Tlogo Rejo, Sudogoro, Tlogo Bulu, Hardi Mulyo,

Sumowono, Pucung Roto, and Ngardi Rejo. The total area

of the study area is approximately 7,472.89 ha with total

population 35,895 citizens. Kaligesing sub-district is located

between Purworejo City and Yogyakarta Province. Due to

improvement of roads network, the development process in

ased particularly along the roads.

Kaligesing sub district is an important region for socio-

economic development particularly fruit production in

Purworejo district Central Java province Indonesia.

Unfortunately, Kaligesing is a landslide prone area. This

study area represents characteristic feature of most of

The climate of the study area is mostly sub-tropical

condition. Based on Oldeman classification, Kaligesing sub

district is categorized as C3 class climate. It has minimal

rainfall intensity, 200 mm in wet month and 100 mm in dry

month. The average amount of rainfall in study area is about

2000 to 5000 mm in a year. Based on Oldeman

classification, wet condition prevails in Kaligesing for 5–6

s dry condition for 2-4 months in a year.

6 months are prone for occurrence of landslides. This

condition represent of high perception during the year.

Based on geology map sheet Yogyakarta scale 1:100,000,

the study area lies in three major rock formations: Old

Andesit Van bemmelen formation, Alluvium formation and

dominated by Old andesit

an bemmelen formation which formed during Oligocene

formation is composed of

andesitic, andicitist lava flow and tuff. The rock material are

constitute of andesit, dasit, conglomerate, breksi andesit,

gravel and sand. In addition, a mechanic weathering process

Fig. 1. Location of study area

in this region is occurring frequently due to such formation

condition as well as effective rainfall and steep slope.

Soil and slope map in this study area were presented in

Fig. 2 and 3. Soil type in study area

controlled by geomorphic pr

USDA classification such as vertisol, inceptisol and enstisol

are found within Kaligesing sub district. Most of upper

areas in Kaligesing develop by vertisol soil types which

have high content of expansive clays and active erosi

mass movement processes. This area covers as a part of

Kaligono village, Hulusobo, Ngaran, Sudorgo and

Tlogoguwo. Furthermore, entisols was found in the

deposition and alluvium plain which lies in part of Kaligono

and Kaliharjo village. Inceptisols

slope which usually disturbed by sedimentation and erosion

process. In great group based on FAO classification we also

found detailed soil types in

hapludalfs, typic eutrudepts, typic

eutrudepts, lithc udhorthents, lithic udhorthents, typic

hapludalfs, haplic udarent, lithic calciustepts, haplic

ustarents, vertic hapludalfs, typic eutrodepts. T

domination of texture was occupied by clay texture.

Fig. 2. Soil map of study area

Expo on Sumatra Tsunami Disaster & Recovery 2010

108

constitute of andesit, dasit, conglomerate, breksi andesit,

gravel and sand. In addition, a mechanic weathering process

Location of study area

in this region is occurring frequently due to such formation

condition as well as effective rainfall and steep slope.

and slope map in this study area were presented in

Fig. 2 and 3. Soil type in study area was formed and

controlled by geomorphic process. Soil type’s based on

USDA classification such as vertisol, inceptisol and enstisol

are found within Kaligesing sub district. Most of upper

areas in Kaligesing develop by vertisol soil types which

have high content of expansive clays and active erosion and

mass movement processes. This area covers as a part of

Kaligono village, Hulusobo, Ngaran, Sudorgo and

Tlogoguwo. Furthermore, entisols was found in the

deposition and alluvium plain which lies in part of Kaligono

and Kaliharjo village. Inceptisols was placed in the hill foot

slope which usually disturbed by sedimentation and erosion

process. In great group based on FAO classification we also

this study area such as lithic

hapludalfs, typic eutrudepts, typic hapludalfs, oxyaquic

eutrudepts, lithc udhorthents, lithic udhorthents, typic

hapludalfs, haplic udarent, lithic calciustepts, haplic

ustarents, vertic hapludalfs, typic eutrodepts. The

domination of texture was occupied by clay texture.

ig. 2. Soil map of study area

Fig. 3. Slope map of study area

The elevation within Kaligesing sub district varies from

250 m to 800 m [12]. It is considered as

slope map which construct from contour map, 7.42 Ha of

area has falls on level slope (3-8%), of 10.83 Ha areas lies

on level slope 8-15 % slope class, 185.65 Ha falls on level

slope (15-30%) and 368.31 Ha falls on level slope (30

45%). Similarly, 134.99 Ha falls on level slope (45

This study area was dominated by mountain areas which lie

in more than 15 % of slope level.

B. Methods of mapping landslide hazard

There are many landslide mapping approach has

been used in several study, including on

remote sensing data, geomorphologic approach, factors

overlay, statistic models, and geotechnical process model

[13]. Factors overlay method were the technique which this

study uses. Landslide location and landslide related factors

such as slope, soil texture, lithology, landform and land use

were used for analyzing landslide susceptibility. A

probability method was used for calculating the rating of the

relative importance of each factor class to landslide

occurrence. In this study, the score of each factor can be

dispensed as the same or different value

judgment. The degree of impotance of value in each

parameter adapts from [14] (Table I).

For calculating the weight of the relative importance of

each factor to landslide occurrence is using analytical

hierarchy processes (AHP). The

TABLE I DEGREE OF IMPORTANCE

Intensity of Importance Numerical scale

Equal importance 1

Weak importance of one over another 3

Essential or strong importance 5

Demonstrated importance 7

Absolute importance 9

Intermediate values between two

adjacent judgments

2, 4, 6, 8

If activity I has one of the above

numbers assigned to it when compared

with activity j, then j has the reciprocal

value when compared with i

Reciprocal of above number


Fig. 3. Slope map of study area

The elevation within Kaligesing sub district varies from

. It is considered as hilly area. Based on

which construct from contour map, 7.42 Ha of

8%), of 10.83 Ha areas lies

15 % slope class, 185.65 Ha falls on level

30%) and 368.31 Ha falls on level slope (30-

level slope (45-65%).

This study area was dominated by mountain areas which lie

Methods of mapping landslide hazard

There are many landslide mapping approach has

been used in several study, including on-ground monitoring,

remote sensing data, geomorphologic approach, factors

overlay, statistic models, and geotechnical process model

. Factors overlay method were the technique which this

study uses. Landslide location and landslide related factors

e, soil texture, lithology, landform and land use

were used for analyzing landslide susceptibility. A

probability method was used for calculating the rating of the

relative importance of each factor class to landslide

f each factor can be

dispensed as the same or different value depends on expert

judgment. The degree of impotance of value in each

For calculating the weight of the relative importance of

occurrence is using analytical

analysis hierarchy

DEGREE OF IMPORTANCE

Numerical scale

2, 4, 6, 8

Reciprocal of above number

processing (AHP) weighted

processing data landslide related factors. The AHP system is

worked for ranking in a set of alternatives.

AHP is create the AHP hierarchy. Then the second steep is

use Pair Wise to compare each of factors. Third procedure is

conducted all the priority into degree of susceptibility of

landslide.

Furthermore, analytical hierarchy m

the pair wise comparisons and produces the relative weights

as output. The procedure consists of three major steps:

generation of the pair wise comparison matrix, computation

of the criterion weights, and estimation of the consistency

ratio. Following is the detailed step of mapping landsl

hazard based on [15] analysis hierarchy processing (AHP)

procedure:

1. Generation of the pair wise comparison matrix:

The method employs scale with values from 1 to 9 to

rate the relative preferences of two cr

(Table 2).

2. Computation of the criterion weights:

This step involves the following operations:

a. Sum of the values in each column of the pair wise

comparison matrix.

b. Divide each element in the matrix by its column total

(the resulting matrix is referred to as the normalized

pair wise comparison matrix)

c. Compute the average of the elements in each row of

the normalized matrix, that is, divide the sum of

normalized scores for each row by the number of

criteria. These averages provide an estimate of the

relative weights of the criteria being compared. The

higher the weight is the more important the criteria.

3. Estimation of the consistency ratio:

This step is to determine whether the comparisons are

consistent. It involves the following operations:

a. Determine the weighted sum vector

the original pair wise comparison matrix to sum of

normalized scores for each row matrix.

b. Determine the consisten

weighted sum vector by the sum of normalized scores

for each row matrix.

c. Calculate lambda (λ), consistency index (CI), and

Consistency Ratio (CR)

a) λ = average value of consistency vector

b) ��= 1−

−

n

nλ ,n is the number of criteria.

c) CR=RI

CI , RI is the random index, the

consistency index of a randomly generated pair

wise comparison matrix. The RI depends on the

number of elements being compared

from [16] (Table 3).


109

weighted is tools that can be use in

processing data landslide related factors. The AHP system is

worked for ranking in a set of alternatives. First of steps in

AHP is create the AHP hierarchy. Then the second steep is

use Pair Wise to compare each of factors. Third procedure is

conducted all the priority into degree of susceptibility of

Furthermore, analytical hierarchy model takes as an input

the pair wise comparisons and produces the relative weights

as output. The procedure consists of three major steps:

generation of the pair wise comparison matrix, computation

of the criterion weights, and estimation of the consistency

g is the detailed step of mapping landslide

analysis hierarchy processing (AHP)

1. Generation of the pair wise comparison matrix:

The method employs scale with values from 1 to 9 to

rate the relative preferences of two criteria based on [16]

2. Computation of the criterion weights:

This step involves the following operations:

a. Sum of the values in each column of the pair wise

Divide each element in the matrix by its column total

(the resulting matrix is referred to as the normalized

pair wise comparison matrix)

Compute the average of the elements in each row of

the normalized matrix, that is, divide the sum of

cores for each row by the number of

criteria. These averages provide an estimate of the

of the criteria being compared. The

higher the weight is the more important the criteria.

3. Estimation of the consistency ratio:

ermine whether the comparisons are

consistent. It involves the following operations:

Determine the weighted sum vector by multiplying

the original pair wise comparison matrix to sum of

normalized scores for each row matrix.

b. Determine the consistency vector by dividing the

weighted sum vector by the sum of normalized scores

λ), consistency index (CI), and

Consistency Ratio (CR)

= average value of consistency vector

,n is the number of criteria.

, RI is the random index, the

consistency index of a randomly generated pair

wise comparison matrix. The RI depends on the

number of elements being compared that taken

(Table 3).


110

TABLE 2 PAIR WISE METHOD

TABLE 3

RANDOM INDEX (RI)

n 1 2 3 4 5 6 7 8

RI 0.00 0.00 0.58 0.90 1.12 1.24 1.32 1.41

n 9 10 11 12 13 14 15

RI 1.45 1.49 1.51 1.48 1.56 1.57 1.59

III. RESULTS AND DISCUSSIONS

Due to of several impacts of landslide, this study develop

landslide hazard map for learning about potential local

landslide hazard and taking step to reduce those hazard.

The basic assumption which used in this study is the geo-

morphological concept that said “the past and present are

the keys to the future [17]. This principle can simply explain

through basic concept of landslide. Landslide is typically

occurred periodically lies on specific physical condition in

certain area like geologic, slope and soil condition which

categorizes as indelible factors. Landslide that occurred in

the past can be occurring in the future under similar

condition. Thus, take similar factor as consideration

composing landslide hazard susceptibility is an idea in this

research. Further, to achieve appropriate landslide hazard

map, this research also combining to other factor like land

use. Factor overlay method and analytic hierarchy

processing (AHP) were used to develop landslide

susceptibility map. Slope class, soil texture, land use,

lithology, and landform are factor that considered

composing the map. Reference [18] conducted a study

based on a research report from [19] and found five classes

of landslide hazard. This research modified the finding and

divided land slide hazard parameters of the respective study

area into three classes based on the findings of [18]; high

susceptible, moderate susceptible and less susceptible. The

selected factors and its score are revealed in Table 4.

AHP was used to determine degree of important for each

factor related to the landslide occurrence. The relative

importance of criteria among one and another was assigned using pair wise method. The pair wise comparison for each

Variable was justified in discussion with an expert and data

from local people perception as well as site visit. As result

of discussion with expert, rainfall factor that mentioned by

local people as factor causing landslide disaster in study

area cannot used to be considering factor due to rainfall data

coverage area. The coverage area is too small and the

pattern of rainfall is homogenous. In addition, in case of

Indonesia, the lowest level of rainfall station is placed

insub-district level. Hence, it should be improve with

TABLE 4 RANKING FACTORS USED TO EACH PARAMETER IN HAZARD ASSESSMENT

Criteria Sub criteria Factor (F)

Slope 3-15%

15-30%

>30 %

1

2

3

Soil texture Loamy sandy, sandy loam

Sandy clay loam

Loam, clay loam, silty clay, silty clay

loam

1

2

3

Land use Rain fed paddy field, rice paddy Field,

grass land

Shrubs, perennial crop

Mix perennial crop, settlement

1

2

3

Rock

material

Andesit

Breksi andesit, dasit

Limestone, sediment breksi andesitic

1

2

3

Landform Alluvial plain

Colluviums-alluvial, foot slope, foot

slope of structural hills , foot slope of

denudation hills

Structural hills, denudation hills, karts

1

2

3

Source: Adopted from Hadmoko, 2009

develop own rainfall station by researcher to get appropriate

data. Further, main idea for composing landslide hazard

mapping is to reduce hazard its self. Thus, this research tried

to considered most factor that can be managed or control by

human being. According to degree of importance slope was

the most importance factor comparing with landform,

lithology, land use and soil texture. Table 5, 6 and 7 from

analysis are revealed the processes of AHP justification.

Based on the judgment matrix and to calculate, λ max =

5.406256, the feature vector of normalization: F=

(0.4042，0.2746，0.2018，0.0845，0.0349). In this

calculation, RI = 1.12. According to relational formula,

CI=0.101564. A consistency ratio (CR) was computed to

verify that the matrix is consistent. CR value is 0.090682,

meaning that the pair-wise matrix is consistent (threshold

CR<0.10) and can be used for assigning the criteria weight.

TABLE 5 ORIGINAL COMPARISON MATRIX

Slope Landform Lithology Land use

Soil

Texture

Slope 1 2 3 5 7

Landform ½ 1 2 5 7

Lithology 1/3 ½ 1 5 7

Land use 1/5 1/5 1/5 1 5

Soil

Texture 1/7 1/7 1/7 1/5 1

A1 A2 A3 … An

A1 W1/W1 W1/W2 W1/W3 … W1/Wn

A2 W2/W1 W2/W2 W2/W3 … W2/Wn

A3 W3/W1 W3/W2 W3/W3 … W3/Wn

. . . . .

. . . . .

. . . . .

TABLE 6 NORMALIZED COMPARISON MATRIX

Slope Landform Lithology

Slope 0.459 0.520 0.472

Landform 0.229 0.260 0.315

Lithology 0.153 0.130 0.157

Land use 0.091 0.052 0.031

Soil Texture 0.065 0.0371 0.022

TABLE 7

RELATIVE WEIGHT OF CRITERIA

Sum Weight

Slope 2.020

Landform 1.373

Lithology 1.008

Land use 0.422

Soil Texture 0.174

According to the above factors evaluation and its

weighted, the formula for landslide hazard index is given

below:

∑=

5

1

1.1 FWLHI

In the formula: LHI— landslide hazard index; W1

each index; F1—factor of each index. Below

representing the above formula:

Landslide hazard index (LHI) = 0.4042*Slope + 0.2746*L

+ 0.2018*LG + 0.0845*LU + 0.0349*ST

Where landslide hazard index is the total susceptibility

score, while the factors (F) are respectively susceptibility

score for Slope: score of slope; L: score of landform; LG:

score of litology; LU: score of land use; and ST: score of

soil texture. This equation is applied in map ca

function of the Spatial Analysis extension tool in the

ArcView. Furthermore, in order to excuse and validate the

final landslide hazard map, final overlay processing between

landslide hazard map and landslide distribution map was

released.

After analyzing and calculated data related landslide

occurrences in this study area, we came to the result.

landslide hazard index value then was classified into three

different classes to identify the susceptibility level. The

result of class of hazard based on

minimum value of the total score (Table 8). Following

formula is applied to the value.

Spatial distribution of the susceptibility classes of

landslide in Kaligesing is showed in Fig. 4.

more than 40 % of the study area was categorized

landslide prone area with moderate susceptibility class and


NORMALIZED COMPARISON MATRIX

Lithology Land use

Soil

Texture

0.472 0.30 0.259

0.315 0.308 0.259

0.157 0.308 0.259

0.031 0.061 0.185

0.022 0.0123 0.037

RELATIVE WEIGHT OF CRITERIA

Weight

0.4042

0.2746

0.2018

0.0845

0.0349

According to the above factors evaluation and its

weighted, the formula for landslide hazard index is given

1

landslide hazard index; W1-weight of

factor of each index. Below calculation is

= 0.4042*Slope + 0.2746*L

+ 0.2018*LG + 0.0845*LU + 0.0349*ST

is the total susceptibility

score, while the factors (F) are respectively susceptibility

score for Slope: score of slope; L: score of landform; LG:

score of litology; LU: score of land use; and ST: score of

soil texture. This equation is applied in map calculator

function of the Spatial Analysis extension tool in the

ArcView. Furthermore, in order to excuse and validate the

final landslide hazard map, final overlay processing between

landslide hazard map and landslide distribution map was

er analyzing and calculated data related landslide

occurrences in this study area, we came to the result. The

value then was classified into three

different classes to identify the susceptibility level. The

based on the maximum and

minimum value of the total score (Table 8). Following

Spatial distribution of the susceptibility classes of

landslide in Kaligesing is showed in Fig. 4. Table 9 shows

ea was categorized as

moderate susceptibility class and

TABLESUSCEPTIBILITY CLASSES

No. Interval value Susceptibility class

1 1-1.588 Less susceptible

2 1.589-2.175 Moderate

3 2.176-2.763 High susceptible

Fig. 4. Map of landslide hazard

TABLE 9

DISTRIBUTION OF LANDSLIDE HAZARD AREA

Landslide hazard

High susceptible

Moderate susceptible

Less susceptible

Total

30.05% falls on high susceptibility class, while the rest

20.78% is categorized as less susceptibility class. Less

susceptibility zone was characteristic with less mass

movement processes and located on

obliqueness. This area lies on alluvial plain landform. While

the moderate landslide susceptibility areas was found to be

characterized by moderately steep to steep

showed incidences of historical mass movement. The

regions of high landslide susceptibility are the areas where

steep slope (>30%) were located and mass movement

occurs frequently. Both old mass movement and new mass

movement were observed to be still active due to high rain

intensity and topography condition.

IV. CONCLUSIO

In summary, mapping landslide hazard through AHP

analysis showed that most of the area Kaligesing is prone to

landslide hazards. In this study, landslide hazard were

divided into three category; high susceptible, moderate and

less susceptible. The proportion of the area mostly falls in

moderate susceptible landslide hazard with 49.15 %.

Effective rainfall, physiographic condition (slope, lithology,

and landform) and improper land utilization were the causes

of landslide occurrences. Even the technical already develop


111

TABLE 8 SUSCEPTIBILITY CLASSES

Susceptibility class Frequency of

landslide point

Less susceptible 2

Moderate susceptible 8

High susceptible 16

Fig. 4. Map of landslide hazard

TABLE 9 DISTRIBUTION OF LANDSLIDE HAZARD AREA

Area (Ha) Percentage

2245.60 30.05

3672.92 49.15

1552.86 20.78

7472.89 100

susceptibility class, while the rest

20.78% is categorized as less susceptibility class. Less

susceptibility zone was characteristic with less mass

movement processes and located on 3–8% slope

obliqueness. This area lies on alluvial plain landform. While

he moderate landslide susceptibility areas was found to be

moderately steep to steep slope and

showed incidences of historical mass movement. The

dslide susceptibility are the areas where

steep slope (>30%) were located and mass movement

occurs frequently. Both old mass movement and new mass

movement were observed to be still active due to high rain

intensity and topography condition.

IV. CONCLUSIONS

In summary, mapping landslide hazard through AHP

analysis showed that most of the area Kaligesing is prone to

landslide hazards. In this study, landslide hazard were

divided into three category; high susceptible, moderate and

less susceptible. The proportion of the area mostly falls in

le landslide hazard with 49.15 %.

Effective rainfall, physiographic condition (slope, lithology,

and landform) and improper land utilization were the causes

of landslide occurrences. Even the technical already develop


112

landslide hazard zone, it is not enough to reduce the risk.

Collaboration management on landslide risk reduction

between regions, departments concerned, universities,

research centers, non-governmental organizations and local

peoples in landslide-prone play important role to better risk

management.

ACKNOWLEDGMENT

The authors thankful to DIKTI scholarship, who supported

this research activity through master scholarship, as well as

Asian Institute of Technology, State University of Malang

and Gadjah Mada University for all services during research

activity.

REFERENCES

[1] UNDP, “Vulnerability and risk assessment,” New

York, United Nation Development. 2nd

ed, 2004

[2] ISRD, “Disaster impact reported. Asia, International

Strategy for Disaster Reduction,” Retrieved April 1

2010, from http://www.unisdr.org/disaster-

statistics/impact-economic.htm, 2005

[3] ILC (International Landslide Center), University of

Durham. Retrieved March 29 2009. from

http://www.landslidecenter.org/database.htm, 2004

[4] Korita, “49 Daerah di Pulau Jawa Rawan Longsor,

Yogyakarta, Indonesia,” Retrieved July 8 2009, from

http://www.ugm.ac.id/index.php?page=rilis&artikel=1

109, 2009

[5] Marfai, M.A and Widiyanto, W, ”Landslide hazard

assessment and mitigation,” Proceeding research year

book. Geography Faculty Gadjah Mada University.

ISBN 979-8786-19-x, pp 36-42, 17 April 2002 (in

Indonesian)

[6] Hadmoko, D.S., “Toward GIS-based integrated

landslide hazard assessment: a critical overview,”

Indonesian Journal of Geography, vol. 34 (1), pp 55-

77, 2007

[7] DGHM, “Landslides database in Java Indonesia,”

Unpublished

[8] Marfai, M.A., King, L., Singh, P.L., Mardiatno, D.,

Sartohadi, J., Hadmoko, D.S., Dewi, A., “Natural

hazards in Central Java Province, Indonesia: an

overview,” Environmental Geology, DOI

10.1007/s00254-007-1169-9, 2007

[9] KR., “Longsor di kecamatan kaligesing Purworejo,”

Kedaulatan rakyat, Purworejo.

RetrievedMarch302009,from

http://www.kr.co.id/web/detail.php?sid=147425&actm

enu=35, 2007

[10] Bachri, S., “Kajian kemampuan lahan dan distribusi

titik longsor untuk penentuan prioritas penanganan

bencana longsor di kecamatan Kaligesing propinsi

Jawa Tengah Indonesia,” Bachelor thesis study,

Gadjah Mada University, Yogyakarta: Gadjah Mada

University, 2005

[11] Office of National Unity and Community Protection.

2009,

[12] Department of Forestry, 2007

[13] Zink, J.A., Lopez, J., Metternicht, G., Shrestha, D.P.,

Selem, L.V., ” Mapping and modeling mass movement

and gullies in mountains areas using remote sensing

and GIS technique, Journal of Applied Geology, vol

3(1), 2001

[14] Saaty, T.L., Alexander, J.M, Thinking with Models:

Mathematical Models in the Physical, Biological and

Social Sciences, Pergamon Press, London, 1981

[15] Saaty, How to make decision: The Analytical

Hierarchy Process. European Journal of Operational

Research 48, 9-26, 1990

[16] Tripathi, N.K., Spatial analysis method in GIS

(Lecturer notes, Course No AT76.9013, School of

Engineering and Technology). Bangkok: Asian

Institute of Technology, 2009

[17] Huabin, W., Gangjun, W., Weiya, X., Gonghui, W.,

“GIS-based Landslide Hazard Assessment: an

Overview,” Progress in Physical Geography 29, Vol

4, pp 548-567, 2005

[18] Hadmoko, D.S., Lavigne, F., Sartohadi, S., Hadi, P.,

Winaryo, “Landslide hazard and risk assessment and

their application in risk management and land use

planning in eastern flank of Menoreh Mountains,

Yogyakarta Province, Indonesia,’’ Natural Hazard,

DOI 10.1007/s11069-009-9490-0, 2009

[19] PSBA-UGM, Research Center for Disaster, 2001

issn: 2086-3195 - fis.um.ac.idfis.um.ac.id/wp-content/uploads/2015/11/aceh-proceeding... · fikri...

Documents