Quantification of Uncertainty in Mineral Prospectivity Prediction Using Neural Network Ensembles and Interval Neutrosophic Sets

Pawalai Krai peerapun, Kok Wai Wong,

Abstract-Qnantification of unscrtalnty in mineral prospee tivity predictinn is an important prncess tn support decisinfi making In mineral exploration, D e p oF uncertainty can identify lcvel of quality in thc prcdiction. This papcr proposes an approach to predict d w e s of hvourability for gold deposits together with quantification of uncertainty in the prediction. Geographic lnfurmatiun Systems (GIS) data is applied to the integratinn of ensemble neural networks and interval neutm- sophip w k Three difTewnt neural network architectufis we uscd in this paper. The prcdiction and its unccrtainty arc represented in the form of truth-membership, indeterminacy- membership. and false-memkrship values, Two networks are created for sash network architecturn to predict degrees of favr)urahility for deposit and nnn depmit, which are represented by truth add false memhership v.sloes wspectiuely. Uncertainty or indeterminacy-mcmbcrship valucs am ~stimatd from both trnth and false membership values. The results obtained using different neural network ensemhle techniques are diwussed in this paper.

Uncertainty estimation in mineral prospectivity prediction is an important task in order to support decision making in regional-scale mineral exploration. I11 this pnper. we focus on uncertainty of type vagueness in which it refers to boundaries t h a t cannot be defined precisely. In [I]. vague objects are separated into vague point, vague Tine, and vague region. Dilo et al. [ I ] defined vague point as a finite set of disjoint sites with known location. but the existence of the sites may be uncertain.

This study involves gridded map layers in a GIs database, each grid cell represents n site with a known location. hut uncertain exiqtence of favourability for deposit. Hence. this study deals with vague point. Some locations have one hvndred percent of favourability for deposits. Some locations have zero percent of favourability for mineral deposits. Such cells are referred to as non-deposit or barren cell?. Most locations have degrees of favourability between these two extremes. Therefnre, each cell contains uncertain information about the degree of favourability for deposits, degree of favourability for hamns. and degree of indeter- minable information or uncertainty. In order to store these three types of information for each cell, we apply interval

htwalai Kraipccmpun is with the School of Information Tcchnolngy. Murdoch University. Australia {emnil: p.kmipeenpun@rnurdoch.edu.~u)

Kok Wai Wong 1% with the School of Inforn~ation Technology. Murdoch University. Australia {elnail. k.wonp@murdoch.edu.su)

Chun Che Fung is with the Ccntrc for Enterprisc Collabm- lion In Innovative Systems, Murdoch University, AusmIia (cmail: I.fung@murdoch.cdu.;tul

Warick Brown is w~th the Centre for Exploration Targeting. The Vniver- siry of Western Australia, Australia (email: wbmwn@cyllenc.uwa.edu.au)

Chun Che Fung, and Warick Brown

neutrosophic sets [2] to keep these information in the form of truth-membership, falsemembership, and indeterminacy- membership values, respectively.

In recent years, neural network methods were found to give ktler mineraI prospectivity prediction results than the conventional empirical statistically-based methods 131. There are various types of neural network used to predict degree of favourability for mineral deposits. For example, Brown et al. [3 ] , [4] applied backpropagation neural network for mineral prospectivity prediction. Skabar [5] used a reed- forward neural network to produce mineral potential maps. Iyer et al. [6], [7] applied a general regression neural network and a polynomial neural network to predict the favourability for gold deposits. Fung ct al. [XI applied neural network ensembles to the prediction of mineral prospectivity.

Hansen and Salarnon [91 suggested that ensembles of neu- wl networks gives better results and less emr than a single neural network. Ensembles of neural networks consist of two steps: training of individual components in the ensembles and combing the output from the component networks [lo]. This study aims to apply neural network ensembles to predict the degrees of favourability for gold deposits and also the degrees of favwrability for barrens. These two degrees are then used to estimate the degree of uncertainty in the prediction for each grid cell on a mineral prospectivity map. Each component of neural network ensembles applied in this study consists of a pair of neural networks trained to predict degree of Favourabi lity for deposits and degree of favourability for barrens, respectively. We use three components in the ensemble of neural networks. These component architecrures are feed-forward backpmpagation neural network, general regression neural network, and polynomial neural network. These three are selected mainly because they have successful application in the field.

A multilayer feed-forward neural network with backprop- agation learning is applied in this study since it is suitable for a large variety of applications. A general regression neural network is a memory-based supervised feed-forward network based on nonlinear regression theory. This network is not necessary to define the number of hidden layers in advance and has fast training time comparing to backpropagation neural network [I I]. A polynomial neural network is based on Group Method of Data Handling (GMDH) [I21 which identifies thc nonlinear relatiuns bctwecn input and output variables. Similar to general regression neural network, a topology of this network is not predetermined but developed through learning [TI.

In order to combine the outputs obtained from components

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of ensemble neural networks, we propose and compare six aggregation techniques which are based on majority vote, averaging, and dynamic averaging techniques. Our proposed techniques have applied the three membership values in the aggregation instead of the truth-membership only as in most conventional approaches.

The rest of this paper is organized as follows. Section I1 presents interval neutrosophic sets used in this study. Sec- tion III explains the proposed model for the quantification of uncertainty in the prediction of favourability for gold deposits using interval neutrosophic sets and ensemble of neural networks. Section IV explains the GIs data set used in this paper. Experimental methodologies and results are also presented in this section. Conclusions are explained in section V.

11. INTERVAL NEUTROSOPHIC SETS

An interval neutrosophic set (INS) is an instance of neutrosophic set [13] which is generalized from the con- cept of a classical set, fuzzy set, interval-valued fuzzy set, intuitionistic fuzzy set, interval-valued intuitionistic fuzzy set, paraconsistent set, dialetheist set, paradoxist set, and tautological set [2]. The membership of an element to the interval neutrosophic set is expressed by three values: t , i, and f , which represent truth-membership, indetenninacy- membership, and false-membership, respectively. These three memberships are independent and can be any real sub-unitary subsets. In some special cases, they can be dependent. In this paper, the indeterminacy-membership value depends on both truth-membership and false membership values. The interval neutrosophic set can represent several kinds of imperfection such as imprecise, incomplete, inconsistent, and uncertain information [14]. In this paper, we express imperfection in the form of uncertainty of type vagueness. This research follows the definition of an interval neutrosophic set that is defined in [2]. This definition is described below.

Let X be a space of points (objects). An interval neutro- sophic set in X is

where TA is the truth-membership function, IA is the indeterminacy-membership function, and FA is the false-membership function.

The operations of interval neutrosophic sets are also ap- plied in this paper. Details of the operations can be found in [14].

111. UNCERTAINTY ESTIMATION USING INTERVAL NEUTROSOPHIC SETS AND ENSEMBLE NEURAL

NETWORKS

This paper applies GIs input data to ensemble neural net- works for the prediction of favourability for gold deposits and

Indeterminacy

Falsity BPNN

GRNN Output

Falsity GRNN GiS input data layers

Output

Falsity PNN

Fig. I. Uncertainty model based on the integration of interval neutrosophic sets and ensemble neural network

utilizes the interval neutrosophic set to express uncertainties in the prediction. Fig. 1 shows our proposed model. The input feature vectors of the proposed model represent values from co-registered cells derived from GIs data layers which are collected and preprocessed from the Kalgoorlie region of Western Australia. The same input data set is used in every neural network created in this paper.

In order to predict degrees of favourability for deposits, we apply three types of neural network architecture: feed- forward backpropagation neural network (BPNN), general regression neural network (GRNN), and polynomial neural neural network (PNN) for training individual network in the ensembles. We create two neural networks for each neural network architecture. The first network is used to predict the degree of favourability for deposits (truth-membership values) and another network is used to predict the degree of favourability for barrens (false-membership values). Both networks have the same architecture and are applied with the same input feature data. The difference between these two networks is that the second network trained to predict degrees of favourability for barrens uses the complement of target outputs used in the first network which is trained to predict degrees of favourability for deposits. For example, if the target output used to train the first network is 0.1, its complement is 0.9. The results from these two networks are used to analyze uncertainty in the prediction. If a cell has high truth-membership value then this cell should have low false-membership value and vise versa. Otherwise, this cell contains high uncertainty. Hence, the degrees of uncertainty in the prediction or indeterminacy-membership values can be calculated as the difference between truth-membership and false-membership values. If the difference between truth-

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membership and false-membership is high then the nncer- tainty is low. In conmst, if the difference between both values is Inw then the uncertainty is high.

In Fig. 1 , the proposed neural network ensembles contain t h ~ e components which each consists of a pair of neural networks. The first pair is feed-forward backpropagation neural networks (truth BPNN and falsity BPNN). The second pair is general regression neural networks (truth GRNN and falsity GRNN). The third pair is polynomial neural networks ( n t h PNN and faIsity PNN3. Each pair of neural networks is trained to predict degrees of favourability for de- posits (truth-membership values) and degrees of favourability for barrens (false-membership values). The indeterminacy- membership values are calculated from the different between truth-m~mbership and fzlse-memhrship values. Therefore, we have three interval neutrosophic sets which are outputs from those three pairs of neural netwarks. We can define these outputs as the following.

Let X j be the set of outputs from the neural netrvork. In our case, we have three sets of outputs, i.e. XI, X2 and &. representitag output sets from BPNN, GRNN and PNN respectively. Each set Xj contains the outputs from each pair of the neural networks. The output set for BPNN is therefore represented as Xr = {xlr, 2 1 2 , .-.,. ~ l i , --., xln} where s r f is a cell in the output from the BPNN at Iwation i.

Let AT be an interval neutrosophic set of X j . Aj can be defined as

where TAj is the truth (deposit) mernkrship function. IAj is the indeterminacy membership function. and FAJ is the false (barren) membership function. After !he individual neural network is trained and the three interval neutto5ophic sets A, are created, the next step is to combine these three sets. Instead of using only truth membership values to predict the favourability for gold deposits. the f~llowings are our propused aggregation techniques using truth-membership, false-membership, and indeterminacy-membership values,

1) Majority vote using T&F For each internal neutrosophic set. A,, if a ccIl x has truth-membership value TA~(X$ greater than a threshold value then this cell is classified as deposit, otherwise it is classified as barren. In this paper, we use threshold values ranging from 0.1 to 0,9 in steps of 0.1. If a cell has false-membership value FA,(x) Iess than a threshold value then this cell is classified as deposit, otherwise it is classified as barren. The results calculated from the best threshold for truth- membership values and the results calculated fmm the best threshold for false-membership values are then calculated using the logical operator and to provide

the prediction results for each cell x i n each output Xj. The degree of uncefiainty for each cell, is expressed by the indeterminacy-membership value, IAj (x). After the three outputs ate classified, the next step is to combine these outputs. The majority vote is then applied in order to aggregate the three outputs. For each cell, if two or more outputs are classified as deposits then the cell is deposit. Othewise, the cell is classified as barren. The uncertainty value for each "deposit" cell is estimated from the average indeterminacy- membership value for all the neural nerwork pairs in the ensemble that classified the input pattern as a depsit . Likewise, uncertainty values for "barren" celIs are calculated as the average of indeterminacy- membership values from the members of the network pairs that gave a classification of barren.

2) Majoriry vote using T > F This technique is more flexible than the first technique. The threshold value is not required for the prediction. For every ccll in each interval neutrosophic set A j , if the truth-membership value is greater than the false- membership value (TA$(x) > FA~(X)) then the cell is classified as deposit. Otherwise it is classified as barren. The degree of uncertainty for each cell is represented by the indeterminacy-membership value, IA,(x). Similar to the first technique, the majority vote is then used to combine the three outputs and the indeteminacy-memkrship values are calculated according to the predicted cell type for each individual output.

31 Averaging using T&F In this technique, the three interval neutrtlsophic sets AJ , j = 1 ,2 ,3 are averaged. Let O be an averaged output m p . O = {or, 0 2 , ..., on) where q is a cell ~f the averaged output map at location i. Let Avg be an interval neutrosophic set of the averaged output map 0. Avg can be calculated as follow

If a cell has averaged truth-memhrship value TA,,(o) greater than a threshold value then this cell is classified as deposit, otherwise the cell is classified as barren. If a cell has averaped false-membership value F A ~ J O ) less than a threshold value then this cell is classified as de- posit, otherwise this cell is classified as barsen. Similar to the first technique, the logical operator and is used to calculate the prediction from the results obtained from the best threshold for buth truth-membership and false-membership values. The degree of unceflainty is expmswd by the averaged indeterminacy-memkrship value I A ~ ~ (0).

4) Averaging using T > F In this technique. the three interval neutrosophic sets are also averaged and the results are stored in Avg. if the averaged truth-membership value is greater than the

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averaged false-~nernbership value .T.;-T;l,?,(o) :r F.;-T;l,,,(o) then the cell is clnssified as deposit. Otherwise the cell is classified as barr.en. The degree of iincertninty for eac I1 cell is repr.esented by the averaged i~~rletesminacy -

iilerribersllip value T.,A,,,(O). 5 ) Dynamic nver.nging using 'I'k 1.'

Illstead of using equal weight averaginp. tliis tecli- niqiie uscs dynan~ic weight averaging in which the weight is the co~riple~nent of the uncertainty value or indeterminacy-~nernbership value for eacli cell. Irnccr- tainty is integrated into truth-rnernbership and false- inernbership values to supporq the co~lfiderice of the prediction. Lct Y he a d y ~ a m i c averaged outpi~t Innp. Y = {gL?U2 ,.,,, gIL) where 3~ is a cell of dynamic averaged outpi~t at locntio~i E . Lct D he an interval neutrosophic set of the dyrlari~ic averaged outpi~t Y. II call be defined as follow

u~ here

I f o ccll has truth-mcmhcrship value 'I?> (9) grcalcr than 3 ~I~i-csl~wld ~ a l u c I ~ C I I his cvll is ~Iassificd as dcpusir, ottler~lise the cell ix cli~ssifiet-l as hiil.en. On (he olher hand. i f a cell has r;~l\e-rnen~bership value F D ( y ) Irsx than a threshold value then thir; cell i q classified ax depocit, otherwise the cell is clahsified as harretl. The results obtained frorn the best threshold tor both truth- membership and false-me~nhershl valuec are the11 combined usliig tlic logical opcratol. irnrl to providc lhc prediction rcsults. Thu dcgrcc of uncertainty is cx- prcsscd by the i i idc t~minacy-n~anba .sh ip value ID(y) which is caluulatcd as the dii'i'crcnt bct~vccn truth- incn~bcrship ilnd L~lsc-mcmbcrship l,alues.

6) nynarr~ic averaging using T > F In this technique, an interval neutrosnphic xet I3 is created using the same previous technique. In order to predict the favourability for depocits. if the ttuth- membership value is greater than the false-membership ualuc I >)(?I) > &A(!!) thcn the cell is classified aa duposit. Othc~.wisc thc ccll is ulassificd as barren. Thc dcg~.uc ol' unccrtaiiiry fur each ccll is rcprcscntcd by thc indc~crminacq.-ii~c~l~bc~-ship valuc In (y).

A. GIS rlrrru sat

The data set used in tliis study was obtained froin a11 approxiinately 100 x 100 k111 arca of the Arcllaen~i Yilgarn Block, ncnr Kalgoorlie. Wcstcni Australia. This data set W C ~ C preprocessecl and compiled into GIS layers fr.oi11 a variety of soutrcs such as geology. geochernistsy, and geophysics. Lt'r:

used ten layers in raster-format to create input feature vecto~.s for wr nod el. These layers reprwent different ~ ~ r i a b l e s sucli as favourability of host rnckc, distance to the nearest regional-scale fault, and distarice to the nearest irlagnetic noomnly. Encli layer- is divided illto a gsicl of sqilnrc cells of 100 111 side. IIcncc, the ]nap arca contnios 1.254.00Q cells. Each ccll stores a single attribute value which is scaled to the rangc [0, I]. For exarnple. a ccll in a layer- representing the distarice to the nearest fault contaios a value of distarice scaled to the raLlgc [O, 11. Each single grid cell is also classified into deposit or' barren cell. The cells containing greatel- than 1.000 kg total contained gold arc, labeled as deposits. All other cells atc classified as non-deposits or barren cells. In this pnpcr. wc list: 268 cells which atc separated into 120 deposit cells and 148 barren cells. These cells arc divided into tsainitig and test dnta sets. L k iisc 85 deposit cells and 102 barren cells fortsninitig data. Fur testing data, w~ usc: 35 deposit cells and 16 barren cells.

In tliis pnpcr. two pnirs of neural networks trained using feed-forwal-d backpropagatio~~ neural ~ieiwork and penenl regrecsion neural tietwork are created using Matlah. .4 pair ut polynomial ncural networks I S traincd using P K N onlinc- soft\vnrc dcvclupcd by Tctko ct al [15]. Each pair of ncurrtl networks is rrnincd to predict dugrccs or hvoui-;rbility for depo\i(s arid degrees of favou~abilily for barrens wllich are truth-tiie~nhership T., (.c) and falw-tne~rthership F d , (;c), reqpectively. Thew two value3 a1.r then u5ed tn calculate the indetrrmi nacy-mt.mbr3rchi1,ibrclip vnluec I , , , (.I:). The three outputs obtained from thcsc t111.c~ iictivork aruliitccturcs arc comhincd using llic proposed cnsc~nblc tccliniqucs. All rcsults shown in this paper arc caluulatcd I'rom llic tcsr data set.

Table I 2nd Tahle 11 show the percentage of total cnrtect cell5 obtained from individual tieurn1 network nrchitectures using a range ot threshold valuec to the truth-membershi p and to thu falsc-mcmbcrship vrtlucs. rcspcutivcly. Thc hcst thrcsli- ulds to rhc truth-mcmhcrship for l3PNN. G K N S . and PKN mc 0.5. 0.b. i~nd 0.5. rcspcctivcly. 'l'hc bcst thresholds to thc rillst-m~i11b~i.ship Tor BPNN. CRNN. i~nd PNN a1.c 0.4. 0.4, and 0.5. rer;pectively. Table 111 shoivs the pel-centage of total col-reci cells obtained ~ I ~ I I I the compari\on bet \v eel1 iruih- membrr~hip and falce-ttie~nbrrship values T,, , (z) > F,,; ( , r ) , and obtained using the logical operator rrrrd to the prediction rcsults using tlic hcst thrcahold for truth-mcmbcrship and tlic hcst threshold for talrc-~tici i lcrshi valucs. Table IV shows tllc pcrucnragc of loral uurrcct cells ublaincd fro111 ctlunl wcight avcriiging and d!niimic wvighr iivvritging using a range of thrcshold valucs lo ttlc 11xth-nicnlbcrship \ : ~ I I U C S (T :-. threshold val i~es) and to the false-~nernbership values ( F < threshold values).

'lablc V shuws thv classification accuracy for the test dnta set usirig our. proposed cnse~tible techniques including the accu racy obtained f1.oi11 the existirig techniques that apply only truth-member-sl~ip values and the accuracy obtained by applying only false-~nernbership values. The comparison of accuracy among these techniques shows that the accuracy

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ohlailled Tl.nrn nur p~,nposed tech~liques i~sirig holh tl.iltll- rnemhet.ship and false-mernhersliip values is sinlilnr to the oucuriicy ublaincd froin thc cxisting tcuhniqucs using unly truth-mcmburship valucs and alsu similar lo ~ h c i i ccu l~ i l~~ obtained fr.0111 the tecli~iiques ~isirig o ~ l l y false-111e111bership values. In dynamic weight averaging technique, the iinccr- tnility or indetzrminacy-11ie1111)erqhip valuer are integrated illto Ilic rrurh-mcmbcrsliip and L'i~lsc-mcmbcrship valucs lu suppol~ the confidence of the prediction. This proposed technique p~.nvides a xlightly better accuracy than the nther cnscmblc tcchniqucs shown in this pnper. l:u~thcrmorc. iill

our proposecl techniques call represent ~incertairlty in the prediction for each cell location.

Tablt: VI shows sample outpi~ts fro111 e n ~ e m b l e of ncu- lal 11etnn1-ks using dyrialnic weight, ave~.ngirig by consid- ering the cornparison between tr.utli-membership and false- n~cinbaship valucs (Y'U(g) > k h ( g ) ) . Vuantification of uncei-hi n ~ y can suppor! thu dcci si un making. For ex;jmplc, the third row ~f tliih table contain the uncertainty value 0.2949 i n which the decision ~naker call accept this result with innre corifidence. Sometirrles, ~~ncei-tairlty for 3 cell is high. ]:or cxomplc. rhc rourth row and rhc scvcnth row of this table contain ucry high uncertainty values which arc 0.8475 and 0.97 1 6, respectively. The t r n t l ~ - ~ ~ ~ e ~ ~ i l e s s l ~ i p and false- n~cmbzrship Tor each uT these cells are very clo\e ingether. Tlte cell at the fnurlh rnw is predicted tn 1,e 3 depnsit which 1s correct. The cell at the seventh row is also predicted to be ;I dcposit but il is inuorrccr. In lliis casc, the dccision ~ n ~ i k c r can rcmakc dccision fur thc cells thal contain high dcgrcc of uncei~ai nly.

In th i h paper, interval rle~ltrnsophic bet+ are integrated ink cnscmblu oT ncul.nl nctworks to plrdicr dcprccs of favnulakility for depn+it> and hanenh. They are also used to quantify uncc~tointy in the prediction. Three pairs ul" ncuriil networks arc trained iisirig t h r w differ.ent neural nctwcrrk archi tttctureh i t ) nrder to provide thlw interval neutrnmphic scr s which arc then combincd using our propuscd aggregation techniques. T h e three neural network aschitectures used in this paper are feed-forward hackl,~.npagatior~ 1le~1ra1 rietwo~.!i, genuriil rcgrcssion ncural nctwork. and pulynomiiil ncuriil

Tlu.c.;tlold KPNS GKNS IPNS valhc ( k CO~TCI T k C O ~ T C I ( k C O ~ Y C I 0. I 5h.79 50.79 58.02. 0.2 59.26 62.46 59.26 0.3 71.60 72.84 65.43 V,4 75,31 81,48 67.41) 0 . 5 72.84 $0.25 69. I4 0 . b 70..37 76.54 b4.10 U,7 64.20 4 . 4 6?3b 4,8 .%.O'L W.2V 59.26 U,9 39,38 43.2 1 50.62

tlelwork. The erperimelltal rewltr show [hat our proposed . - erlcerrthle techniques pm\;ide silnilnr accuracy to other exist- in:, et~sernble techniques applied in this paper: Results from thc cspcrimcnts havc not idcntificd any approach which is

- -

oblc tu providc a signiliciint iinprovcmcnt vvcr the othcrs. Ilowcvcr. thc clynaiuic iivcrapiilg appruilch h;js 2 sligh~lq. better- perforlnance. T h e key c ~ n t r i b u t i v ~ i i n this study is that nll the proposed tecliriiques arc capable of representing uncertainty in the prediction of favourability for encli cell locaticln. While this pnpw focuses only on the uncertainty i n the prediction outpi~t. research is cuntinued on the assehslnznt of uncertainty in the CIS itlpi~t data pr-iot- to npplyitig ro the prcdicriui~ s)stcm.

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4 ~ c r a ~ i i i g !.I'/F) KI.iih 78 , lh 80.23 I ~ Y I I I , \ L I C , ! ~ ~ I I . ~ on Sxr f r*~~~ . r . Mcrr~ r111r1 I-'rht.~-rzr,!~r,r. ~(11.1, pp. 1h.3-378.

Dy i i an~ i c :~vcr;lging (T) 110 00 1;l 01 X 1.11; 1971.

80.00 78.26 79.0 1 [ 131 F, S i ~ ~ a i ~ i > [ i ~ I i ~ ~ t ~ c . /I L!rzi{li~~,y i,-i<,/ri ill L<y i< .i , ~ Y < , ~ I ~ I ~ ? , T O J J I I ~ L - Loyii,. I ) y i ~a i~uc IIL.CI.~~:~[~:: ir )

nyi i : j i i i ic nvc~,ngirl; ( T k F ) YO.00 82.61 8 1.4s t : ~ i 5 , C I I ~ I O I ~ Pmhtrhilif~ orrtl S / t r f i .>~ic .~ . 1111t,d cd i l~o t l . Xiclun~i. Phoct~ i r . 2003. 1lyii;jnilc ovcrngltly (T,Irl Y2.86 78.36 80.25

[ 1-11 1 I, Wat~;. 1. Sninrai>dnct~c. Y.-V. %hat>;. nnd K, Sundcrr;~n~on. rrrfcr-rcri : i 1 r ~ ~ / ~ ~ ~ . i ~ ~ j j l f ~ t . SL,~), < I I I ~ hl<i~-: T J ~ C O I ; ~ < I I I ~ ~ \ l ) I ~ / l c ~ ( i i i ~ ? ~ i ~ if1 C(IIII />II I -

TABLF. VI i111: : i l ~ f t / ! ~ ~ . i o / ~ / f ~ t , &ooA Sct,ics :Vo.J. ~ I I I ~ ~ ~ / / ~ I ~ ~ ~ ~ . ~ ~ ~ ~ / ~ I ~ ~ ~ / ~ ~ / O S O S O I ~ ~ s,it%il'[,l: OLl '~t,L l'5 f :KO\I t , . l Y l \ i H [ , t : (!I Nt:L I<,\l, \l.:l!AOKKX LlYIN(i :lccc~.;cd Junc 2M5.

[ I S ] I, V. TCI~W, T, 1, i ik\ciitn;l. i', V, Vvlkovictl. i ~ r l i l I>, Y2 F i l i pw , r)unl t j r r t : ~ Y I ~ I ~ , I I I AVI K A ( , I X ( ~ ( ' I ' 2 1 , ) I.(II< 1 1 1 1 r1.s I I).II.\ st:-l \'i~-tuifI C O I I I ~ Y I ~ I ( I ~ ~ ( > I I L I / L-h<,ri~i,>tr:r Li!h~~-lr!o~>-. l011-Ii11h.cl ! lv; l l l~ lbl~~:

th11p;/il4h. 107.2 17.1 7t;/l;~t~1]~1111fu1~1~~.t1~i11l. i ~ c ~ c > a r , d Y r ? \ c ~ l l t x r 2005. ,4cruul 1'1-c<licrr.d Trut11- Fillhc- I ~ i ~ I ~ ~ ~ i - ~ ~ i i r ~ i i c y

C c l l Typ t Cc l l T y p t \ fc [>~ l>c [ , \ l~ ip \ fc[>~lhc[, \ l~ ip \ fc r~~t>c [ ,> I~ ip r ) t ] ><+ \ i ~ I:)t]>~i\i~ O.tlk26 +539 - 7 0 1

r')t11n\i1 H;uirii 0.41 48 U.676? 0.7136 t 1 i I 1 i 0.8342 0 .1198 0,2449 r')t110\i1 l ; ) t ] ~ ~ i \ i ~ 0.Sh75 U.5149 0.9475 H;uicij H ~ u i c i i 0.1 h29 U.8hY6 11.2943 HAIICI~ I:)?]\o\il 0 .6105 U.4336 0 . 8 4 3 2 H;uicij I : ) t ]w+ i~ 0,5 I 8 I 0.4X9t; 0 .g716 HAIICI~ HL~II~II 0.3079 U,bS.% U,b5J1

Pawalai Krnipeet.npun is supported by postgraduate rc- search scholarship fsoln the Royal Thai Govcrnlncnt.

300f 151 A. -5Li1t>iii-. \4i1icrd l> t ) tc i~~ i i l I r~i i lp~>ir~; ~lyiri: l ' ~ ~ d - I ' o l - ~ a ~ d [lcural IIL-I-

w<~rk \ . P I ~ J L , . ~ 1 7 ~ !rz#i,~ I I ~ I I I O I I ( I / JNII! C ~ ~ ~ r ~ i ~ r ( ~ r z < ~ ~ ~ ( ? I ) : ~ ' < J ~ I I td! :~'<CIII i11-A,+.

3003. 1813 1819. [ h ] \i. 1)c.l. C , C. Fung. W. V.i(lr,wn, i l l id T. C c d c r ~ ~ i . A gt i ic[ , :~l ~,c;rc\\ioii

[ I C L I ~ ~ [ l c ~ ~ ( p [ k h a d [~ i t )dc I I'UT [ i i i i i c rd l ' i ~ v t u ~ h i l i ~ y prh.ct!i~li<>~n. PIYIL,. rlrc Fijtt~ ~ ? s I ~ I - M ~ I [ ( I / L , L!<~c/~- ic~ir E t ~ g i ~ i t ~ t ~ r i t ~ p ( I I I ~ C O I I I ~ W I ~ I I ~ S J , I J I ~ J I - A I I I ) ) I tI'l:l<t-.7 ?OO/). I%i-~li. W-rstvrii ,411~1~1l i :~. 3004. 207 209>

[ 7 ] V. I y. C. C, rung. W. Hi<,wn, iliiil T. C c d c r ~ ~ i . Lhr+*d ~ ) ( ~ l y i i ~ ~ i i ~ i ill 11c11ril1 r~clv,r?rk I u r ~ ~ t ~ i i i ~ r i l l ~ > r ' o u ] ~ c c l l \ ~ ~ y . ~ i ~ ~ l l > \ i \ In aGIS cr~riioii1ilcii1. P111i: rlrc lEEf1 Ht'pir)~~ 10 C-vrdi,rt,rzc,c, i7r.ffro11 2004). Cliiilrl; Mai. Tliai lalirl. 3004. 4 1 1-41 4.

[U] C. C. Fung. V. Iy. W. Riown. i l l id K. W. \i'oiip. S ~ u r i l Sc1w11rk E I I ~ ~ I I I ~ I L ~ I~~ISL.CI .A]I~I[~~ICII lur L l i ~ ~ c r i l l ' r o \ l w ~ l ~ + I I ~ I ' i - c ~ I i c l l ~ ~ ~ ~ . P f ~ ) i , . 1j3c I E H ,4t,Fir>11 10 c ~ ~ r ~ i ~ r ~ ~ r z < ~ ( ~ ( T P I I ~ J J I I 200.7). \ ~ c l l ~ r ~ ~ ~ r ~ i c . , 4 1 1 s ~ ~ i l i :j. 2005 (TI>-KOY. 5 pil;o\l.

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