application of neural network of multi layers perceptron (mlp) in site selection of waste disposal (case ‎study: fereydoonshahr city)‎

نویسندگان

علیرضا عرب عامری

دانشگاه تربیت مدرس کورش شیرانی

مرکز تحقیقات اصفهان جلال کرمی

دانشگاه تربیت مدرس عبدالله کلوراژان

دانشگاه تربیت مدرس

چکیده

introductapplication of neural network of multi layers perceptron (mlp) in site selection of municipal solid ‎waste landfilling with emphasis on hydrogeomorphic characteristics (case study: fereydoonshahr city)‎introduction‏:‏cities are at the nexus of a further threat to the environment, namely the production of an increasing ‎quantity and complexity of wastes. the estimated quantity of municipal solid waste (msw) generated ‎worldwide is 1.7 – 1.9 billion metric tons.‎‎ in many cases, municipal wastes are not well managed in low-income countries, more than 50 per cent of ‎the collected waste is often disposed of through uncontrolled landfilling and about 15 per cent is ‎processed through unsafe and informal recycling. municipal solid waste (msw) is the natural result of ‎human activities. if an appropriate management system is not used for this problem, it may lead to ‎environmental pollution and jeopardize the mankind’s health. ‎the ann models are basically based on the perceived work of the human brain‏.‏‎ anns can be trained to ‎model any relationship between a series of independent and dependent variables (inputs and outputs to ‎the network respectively). for this reason, anns have been usefully applied to a wide variety of ‎problems that are difficult to understand, define, and quantify. it should be pointed out that similar to any ‎other statistical and mathematical model, ann models have also some disadvantages, too. having a large ‎number of input variables is one of the most common problems for their development because they are ‎not engineered to eliminate superfluous inputs.‎‎ literature survey demonstrates that artificial neural network (ann) models are proper tools for prediction ‎of solid waste generation predicting. noori et al (2008) investigated the prediction of municipal solid ‎waste generation with combination of support vector machine and principal component analysis in ‎mashhad and in authors’ opinion, the model presented in this article is a potential tool for predicting wg ‎and has advantages over the traditional svm model. jalili and noori (2008) investigated the prediction of ‎municipal solid waste generation by use of artificial neural network and results point that artificial ‎neural network model has more advantages in comparison with traditional methods in predicting the ‎municipal solid waste generation.noori et al (2010) investigated the evaluttion of pca and gamma test ‎techniques on ann operation for weekly solid waste prediction and findings indicated that the pca-ann ‎and gt-ann models have more effective results than the ann model. these two models decrease the ‎number of input variables from 13 to 7 and 5, respectively.‎the accurate prediction of waste disposal zonation plays an important role in the solid waste management ‎system. for this reason, ann is used and different models are created and tested.‎materials‏ & ‏‎ methods‏:‏fereydunshahr city is located from 49° 36ʹ to 50° 19ʹ longitude and from 32° 37ʹ to 33° 05ʹ latitude ‎geographic coordinate system. the extent of the area is 77646 hectar. fereydunshahr city with an average ‎altitude of 2500 m above sea level is a mountainous region and is located in the province of isfahan. ‎according to hydrological, geological, and geomorphological characteristics of study area and the goals ‎outlined, it can be said that the parameters used to municipal solid waste landfilling are different. in this ‎research the most important factors are used for this purpose are 12 primary factors influencing ‎municipal solid waste landfilling in the study area, including lithology, level of groundwater, soil ‎texture, distance to habitate, land use, slope, aspect, elevation, rainfall, distance to fault, distance to road, ‎and drainage density were identified by interpretation of satellite imagery, aerial photography, and field ‎studies. the used base map in this work including geological map at a scale of 1: 100,000, aerial ‎photographs on a scale of 1: 40,000, topographical maps with a scale of 1: 50,000, etm +satellite images ‎and precipitation (rain-gauge stations) were prepared by arcgis10.2 software. ‎the digital elevation model (dem) with 30 meter multiplied by 30 meter pixel size was prepared by using ‎topographic map 1:50000. the distance to drainage and road was extracted by drainage and road ‎networks from study area topographic map. the land use map was provided by including unsupervised ‎classification etm+ image satellite, field survey, and accuracy control. also geologic map was prepared ‎by digitizing and polygonize of rock units of geologic map 1:100000 and using arcgis10.2. artificial ‎neural networks, originally developed to mimic basic biological neural systems‏.‏‎ a network can perform a ‎surprising number of tasks quite efficiently (reilly and cooper,1990). this information processing ‎characteristic makes anns a powerful computational device and able to learn from examples and then to ‎generalize to examples never before seen. recent research activities in artificial neural networks (anns) ‎have shown that anns have powerful pattern classification and pattern recognition capabilities.the most ‎popular architecture for a neural network is a multilayer perceptron (bishop, 1995; jain, et al., 2006). in ‎this study, we used was the feed forward, multilayer perceptron (mlp), which is consideredable to ‎approximate every measurable function (gardner and dorling, 1998). the main issue in training mlp for ‎prediction is the generalization performance. mlp, like other flexible nonlinear estimation methods such ‎as kernel regression, smoothing splines, can suffer from either underfitting or overfitting (coulibaly, et al., ‎‎2000). in this situation error between training and testing results start to increase. for solving this problem, ‎stop training approach (sta) has been used. data are divided into 3 parts in this method. first part is ‎related to network training, second part for stopping calculations when error of integrity start to increase ‎and the third part that is used for integrity of network. in order to evaluate the performance of the ann ‎model 3 statistical indices are used: t mean squared normalized error (mse)‎, root mean square error ‎‎(rmse) and correlation coefficient (r2) values that are derived in statistical calculation of observation in ‎model output predictions, defined as:‎mse=(∑_(i= 0)^n▒〖 (d_(i )- y_i ) 〗)/n ‎rmse=√(∑_(i=1)^n▒((obs-pre)/n) ) ‎‏ ‏r^2=(∑_(i=1)^n▒〖(obs-obs) (pre-pre) 〗)/(√(∑_(i=1)^n▒(obs-obs)^2 ) ∑_(i=‎‎1)^n▒(pre-pre)^2 )‎‏ ‏‎ ‎discussion of results‏ ‏& conclusions:‎accurate prediction of landfilling site selection of municipal solid waste is crucial for programming ‎municipal solid waste management system. in this research with application of feed forward artificial ‎neural network, an appropriate model for predicting of landfilling site selection of municipal solid waste ‎in fereydunshahr city, was proposed. for this purpose, in this paper, neural network is trained and tested ‎using matlab 7.2.. for this purpose, 12 primary factors influencing municipal solid waste landfilling in ‎the study area, including lithology, level of groundwater, soil texture, distance to habitate, land use, ‎slope, aspect, elevation, rainfall, distance to fault, distance to road, and drainage density were chosen for ‎imput layers. also, for recognizing the effect of each input data sensitive analysis was performed.finally, ‎different structures of artificial network were investigated and then the best model for predicting ‎landfilling site selection of municipal solid waste was chosen based on ‎mean squared normalized error ‎‎(mse)‎, root mean square error (rmse) and correlation coefficient (r2) indexes. after performing of the ‎mentioned models, mean squared normalized error (mse)‎, root mean square error (rmse) and ‎correlation coefficient (r2)in neural network for test have been achieved equal to 0.0081 , 0.11 and ‎‎0.999% respectively. results indicate that trainlm model has more advantages in comparison with trainbp ‎and trainbpx methods in landfilling site selection of municipal solid waste. after determining the best ‎network structure, zonation map of the best site for landfilling of municipal solid waste using 12 imput-‎layer was prepared in 5 classes. the results showed that 37.2% (28884/31ha) of the total area is very ‎suitable for waste landfilling, 7.2% (5590/51 ha) suitable, 12.6% (9783/39 ha) is fairly suitable, 38% ‎‎(29505/48 ha) unsuitable and 5% (3882/3 ha) is very unsuitable‎keyword: multi layers perceptron, site selection, waste disposal, fereydunshahr city.‎‏‎

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عنوان ژورنال:
محیط شناسی

جلد ۴۲، شماره ۲، صفحات ۳۲۹-۳۴۱

کلمات کلیدی
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' i n c o m e c o u n t r i e s ' , ' m o r e t h a n 5 0 p e r c e n t o f u 2 0 0 e t h e c o l l e c t e d w a s t e i s o f t e n d i s p o s e d o f t h r o u g h u n c o n t r o l l e d l a n d f i l l i n g a n d a b o u t 1 5 p e r c e n t i s u 2 0 0 e p r o c e s s e d t h r o u g h u n s a f e a n d i n f o r m a l r e c y c l i n g . m u n i c i p a l s o l i d w a s t e ( m s w ) i s t h e n a t u r a l r e s u l t o f u 2 0 0 e h u m a n a c t i v i t i e s . i f a n a p p r o p r i a t e m a n a g e m e n t s y s t e m i s n o t u s e d f o r t h i s p r o b l e m ' , ' i t m a y l e a d t o u 2 0 0 e e n v i r o n m e n t a l p o l l u t i o n a n d j e o p a r d i z e t h e m a n k i n d ’ s h e a l t h . u 2 0 0 e t h e a n n m o d e l s a r e b a s i c a l l y b a s e d o n t h e p e r c e i v e d w o r k o f t h e h u m a n b r a i n u 2 0 0 f . u 2 0 0 f u 2 0 0 e a n n s c a n b e t r a i n e d t o u 2 0 0 e m o d e l a n y r e l a t i o n s h i p b e t w e e n a s e r i e s o f i n d e p e n d e n t a n d d e p e n d e n t v a r i a b l e s ( i n p u t s a n d o u t p u t s t o u 2 0 0 e t h e n e t w o r k r e s p e c t i v e l y ) . f o r t h i s r e a s o n ' , ' a n n s h a v e b e e n u s e f u l l y a p p l i e d t o a w i d e v a r i e t y o f u 2 0 0 e p r o b l e m s t h a t a r e d i f f i c u l t t o u n d e r s t a n d ' , ' d e f i n e ' , ' a n d q u a n t i f y . i t s h o u l d b e p o i n t e d o u t t h a t s i m i l a r t o a n y u 2 0 0 e o t h e r s t a t i s t i c a l a n d m a t h e m a t i c a l m o d e l ' , ' a n n m o d e l s h a v e a l s o s o m e d i s a d v a n t a g e s ' , ' t o o . h a v i n g a l a r g e u 2 0 0 e n u m b e r o f i n p u t v a r i a b l e s i s o n e o f t h e m o s t c o m m o n p r o b l e m s f o r t h e i r d e v e l o p m e n t b e c a u s e t h e y a r e u 2 0 0 e n o t e n g i n e e r e d t o e l i m i n a t e s u p e r f l u o u s i n p u t s . u 2 0 0 e u 2 0 0 e l i t e r a t u r e s u r v e y d e m o n s t r a t e s t h a t a r t i f i c i a l n e u r a l n e t w o r k ( a n n ) m o d e l s a r e p r o p e r t o o l s f o r p r e d i c t i o n u 2 0 0 e o f s o l i d w a s t e g e n e r a t i o n p r e d i c t i n g . n o o r i e t a l ( 2 0 0 8 ) i n v e s t i g a t e d t h e p r e d i c t i o n o f m u n i c i p a l s o l i d u 2 0 0 e w a s t e g e n e r a t i o n w i t h c o m b i n a t i o n o f s u p p o r t v e c t o r m a c h i n e a n d p r i n c i p a l c o m p o n e n t a n a l y s i s i n u 2 0 0 e m a s h h a d a n d i n a u t h o r s ’ o p i n i o n ' , ' t h e m o d e l p r e s e n t e d i n t h i s a r t i c l e i s a p o t e n t i a l t o o l f o r p r e d i c t i n g w g u 2 0 0 e a n d h a s a d v a n t a g e s o v e r t h e t r a d i t i o n a l s v m m o d e l . j a l i l i a n d n o o r i ( 2 0 0 8 ) i n v e s t i g a t e d t h e p r e d i c t i o n o f u 2 0 0 e m u n i c i p a l s o l i d w a s t e g e n e r a t i o n b y u s e o f a r t i f i c i a l n e u r a l n e t w o r k a n d r e s u l t s p o i n t t h a t a r t i f i c i a l u 2 0 0 e n e u r a l n e t w o r k m o d e l h a s m o r e a d v a n t a g e s i n c o m p a r i s o n w i t h t r a d i t i o n a l m e t h o d s i n p r e d i c t i n g t h e u 2 0 0 e m u n i c i p a l s o l i d w a s t e g e n e r a t i o n . n o o r i e t a l ( 2 0 1 0 ) i n v e s t i g a t e d t h e e v a l u t t i o n o f p c a a n d g a m m a t e s t u 2 0 0 e t e c h n i q u e s o n a n n o p e r a t i o n f o r w e e k l y s o l i d w a s t e p r e d i c t i o n a n d f i n d i n g s i n d i c a t e d t h a t t h e p c a ' , ' a n n u 2 0 0 e a n d g t ' , ' a n n m o d e l s h a v e m o r e e f f e c t i v e r e s u l t s t h a n t h e a n n m o d e l . t h e s e t w o m o d e l s d e c r e a s e t h e u 2 0 0 e n u m b e r o f i n p u t v a r i a b l e s f r o m 1 3 t o 7 a n d 5 ' , ' r e s p e c t i v e l y . u 2 0 0 e t h e a c c u r a t e p r e d i c t i o n o f w a s t e d i s p o s a l z o n a t i o n p l a y s a n i m p o r t a n t r o l e i n t h e s o l i d w a s t e m a n a g e m e n t u 2 0 0 e s y s t e m . f o r t h i s r e a s o n ' , ' a n n i s u s e d a n d d i f f e r e n t m o d e l s a r e c r e a t e d a n d t e s t e d . u 2 0 0 e m a t e r i a l s u 2 0 0 f & u 2 0 0 f u 2 0 0 e m e t h o d s u 2 0 0 f : u 2 0 0 f f e r e y d u n s h a h r c i t y i s l o c a t e d f r o m 4 9 ° 3 6 ʹ t o 5 0 ° 1 9 ʹ l o n g i t u d e a n d f r o m 3 2 ° 3 7 ʹ t o 3 3 ° 0 5 ʹ l a t i t u d e u 2 0 0 e g e o g r a p h i c c o o r d i n a t e s y s t e m . t h e e x t e n t o f t h e a r e a i s 7 7 6 4 6 h e c t a r . f e r e y d u n s h a h r c i t y w i t h a n a v e r a g e u 2 0 0 e a l t i t u d e o f 2 5 0 0 m a b o v e s e a l e v e l i s a m o u n t a i n o u s r e g i o n a n d i s l o c a t e d i n t h e p r o v i n c e o f i s f a h a n . u 2 0 0 e a c c o r d i n g t o h y d r o l o g i c a l ' , ' g e o l o g i c a l ' , ' a n d g e o m o r p h o l o g i c a l c h a r a c t e r i s t i c s o f s t u d y a r e a a n d t h e g o a l s u 2 0 0 e o u t l i n e d ' , ' i t c a n b e s a i d t h a t t h e p a r a m e t e r s u s e d t o m u n i c i p a l s o l i d w a s t e l a n d f i l l i n g a r e d i f f e r e n t . i n t h i s u 2 0 0 e r e s e a r c h t h e m o s t i m p o r t a n t f a c t o r s a r e u s e d f o r t h i s p u r p o s e a r e 1 2 p r i m a r y f a c t o r s i n f l u e n c i n g u 2 0 0 e m u n i c i p a l s o l i d w a s t e l a n d f i l l i n g i n t h e s t u d y a r e a ' , ' i n c l u d i n g l i t h o l o g y ' , ' l e v e l o f g r o u n d w a t e r ' , ' s o i l u 2 0 0 e t e x t u r e ' , ' d i s t a n c e t o h a b i t a t e ' , ' l a n d u s e ' , ' s l o p e ' , ' a s p e c t ' , ' e l e v a t i o n ' , ' r a i n f a l l ' , ' d i s t a n c e t o f a u l t ' , ' d i s t a n c e t o r o a d ' , ' u 2 0 0 e a n d d r a i n a g e d e n s i t y w e r e i d e n t i f i e d b y i n t e r p r e t a t i o n o f s a t e l l i t e i m a g e r y ' , ' a e r i a l p h o t o g r a p h y ' , ' a n d f i e l d u 2 0 0 e s t u d i e s . t h e u s e d b a s e m a p i n t h i s w o r k i n c l u d i n g g e o l o g i c a l m a p a t a s c a l e o f 1 : 1 0 0 ' , 0 , ' a e r i a l u 2 0 0 e p h o t o g r a p h s o n a s c a l e o f 1 : 4 0 ' , 0 , ' t o p o g r a p h i c a l m a p s w i t h a s c a l e o f 1 : 5 0 ' , 0 , ' e t m + s a t e l l i t e i m a g e s u 2 0 0 e a n d p r e c i p i t a t i o n ( r a i n ' , ' g a u g e s t a t i o n s ) w e r e p r e p a r e d b y a r c g i s 1 0 . 2 s o f t w a r e . u 2 0 0 e t h e d i g i t a l e l e v a t i o n m o d e l ( d e m ) w i t h 3 0 m e t e r m u l t i p l i e d b y 3 0 m e t e r p i x e l s i z e w a s p r e p a r e d b y u s i n g u 2 0 0 e t o p o g r a p h i c m a p 1 : 5 0 0 0 0 . t h e d i s t a n c e t o d r a i n a g e a n d r o a d w a s e x t r a c t e d b y d r a i n a g e a n d r o a d u 2 0 0 e n e t w o r k s f r o m s t u d y a r e a t o p o g r a p h i c m a p . t h e l a n d u s e m a p w a s p r o v i d e d b y i n c l u d i n g u n s u p e r v i s e d u 2 0 0 e c l a s s i f i c a t i o n e t m + i m a g e s a t e l l i t e ' , ' f i e l d s u r v e y ' , ' a n d a c c u r a c y c o n t r o l . a l s o g e o l o g i c m a p w a s p r e p a r e d u 2 0 0 e b y d i g i t i z i n g a n d p o l y g o n i z e o f r o c k u n i t s o f g e o l o g i c m a p 1 : 1 0 0 0 0 0 a n d u s i n g a r c g i s 1 0 . 2 . a r t i f i c i a l u 2 0 0 e n e u r a l n e t w o r k s ' , ' o r i g i n a l l y d e v e l o p e d t o m i m i c b a s i c b i o l o g i c a l n e u r a l s y s t e m s u 2 0 0 f . u 2 0 0 f u 2 0 0 e a n e t w o r k c a n p e r f o r m a u 2 0 0 e s u r p r i s i n g n u m b e r o f t a s k s q u i t e e f f i c i e n t l y ( r e i l l y a n d c o o p e r ' , ' 1 9 9 0 ) . t h i s i n f o r m a t i o n p r o c e s s i n g u 2 0 0 e c h a r a c t e r i s t i c m a k e s a n n s a p o w e r f u l c o m p u t a t i o n a l d e v i c e a n d a b l e t o l e a r n f r o m e x a m p l e s a n d t h e n t o u 2 0 0 e g e n e r a l i z e t o e x a m p l e s n e v e r b e f o r e s e e n . r e c e n t r e s e a r c h a c t i v i t i e s i n a r t i f i c i a l n e u r a l n e t w o r k s ( a n n s ) u 2 0 0 e h a v e s h o w n t h a t a n n s h a v e p o w e r f u l p a t t e r n c l a s s i f i c a t i o n a n d p a t t e r n r e c o g n i t i o n c a p a b i l i t i e s . t h e m o s t u 2 0 0 e p o p u l a r a r c h i t e c t u r e f o r a n e u r a l n e t w o r k i s a m u l t i l a y e r p e r c e p t r o n ( b i s h o p ' , ' 1 9 9 5 ; j a i n ' , ' e t a l . ' , ' 2 0 0 6 ) . i n u 2 0 0 e t h i s s t u d y ' , ' w e u s e d w a s t h e f e e d f o r w a r d ' , ' m u l t i l a y e r p e r c e p t r o n ( m l p ) ' , ' w h i c h i s c o n s i d e r e d a b l e t o u 2 0 0 e a p p r o x i m a t e e v e r y m e a s u r a b l e f u n c t i o n ( g a r d n e r a n d d o r l i n g ' , ' 1 9 9 8 ) . t h e m a i n i s s u e i n t r a i n i n g m l p f o r u 2 0 0 e p r e d i c t i o n i s t h e g e n e r a l i z a t i o n p e r f o r m a n c e . m l p ' , ' l i k e o t h e r f l e x i b l e n o n l i n e a r e s t i m a t i o n m e t h o d s s u c h u 2 0 0 e a s k e r n e l r e g r e s s i o n ' , ' s m o o t h i n g s p l i n e s ' , ' c a n s u f f e r f r o m e i t h e r u n d e r f i t t i n g o r o v e r f i t t i n g ( c o u l i b a l y ' , ' e t a l . ' , ' u 2 0 0 e u 2 0 0 e 2 0 0 0 ) . i n t h i s s i t u a t i o n e r r o r b e t w e e n t r a i n i n g a n d t e s t i n g r e s u l t s s t a r t t o i n c r e a s e . f o r s o l v i n g t h i s p r o b l e m ' , ' u 2 0 0 e s t o p t r a i n i n g a p p r o a c h ( s t a ) h a s b e e n u s e d . d a t a a r e d i v i d e d i n t o 3 p a r t s i n t h i s m e t h o d . f i r s t p a r t i s u 2 0 0 e r e l a t e d t o n e t w o r k t r a i n i n g ' , ' s e c o n d p a r t f o r s t o p p i n g c a l c u l a t i o n s w h e n e r r o r o f i n t e g r i t y s t a r t t o i n c r e a s e u 2 0 0 e a n d t h e t h i r d p a r t t h a t i s u s e d f o r i n t e g r i t y o f n e t w o r k . i n o r d e r t o e v a l u a t e t h e p e r f o r m a n c e o f t h e a n n u 2 0 0 e m o d e l 3 s t a t i s t i c a l i n d i c e s a r e u s e d : t m e a n s q u a r e d n o r m a l i z e d e r r o r ( m s e ) u 2 0 0 e ' , ' r o o t m e a n s q u a r e e r r o r u 2 0 0 e u 2 0 0 e ( r m s e ) a n d c o r r e l a t i o n c o e f f i c i e n t ( r 2 ) v a l u e s t h a t a r e d e r i v e d i n s t a t i s t i c a l c a l c u l a t i o n o f o b s e r v a t i o n i n u 2 0 0 e m o d e l o u t p u t p r e d i c t i o n s ' , ' d e f i n e d a s : u 2 0 0 e m s e = ( ∑ _ ( i = 0 ) ^ n ▒ 〖 ( d _ ( i ) ' , ' y _ i ) 〗 ) / n u 2 0 0 e r m s e = √ ( ∑ _ ( i = 1 ) ^ n ▒ ( ( o b s ' , ' p r e ) / n ) ) u 2 0 0 e u 2 0 0 f u 2 0 0 f r ^ 2 = ( ∑ _ ( i = 1 ) ^ n ▒ 〖 ( o b s ' , ' o b s ) ( p r e ' , ' p r e ) 〗 ) / ( √ ( ∑ _ ( i = 1 ) ^ n ▒ ( o b s ' , ' o b s ) ^ 2 ) ∑ _ ( i = u 2 0 0 e u 2 0 0 e 1 ) ^ n ▒ ( p r e ' , ' p r e ) ^ 2 ) u 2 0 0 e u 2 0 0 f u 2 0 0 f u 2 0 0 e u 2 0 0 e d i s c u s s i o n o f r e s u l t s u 2 0 0 f u 2 0 0 f & c o n c l u s i o n s : u 2 0 0 e a c c u r a t e p r e d i c t i o n o f l a n d f i l l i n g s i t e s e l e c t i o n o f m u n i c i p a l s o l i d w a s t e i s c r u c i a l f o r p r o g r a m m i n g u 2 0 0 e m u n i c i p a l s o l i d w a s t e m a n a g e m e n t s y s t e m . i n t h i s r e s e a r c h w i t h a p p l i c a t i o n o f f e e d f o r w a r d a r t i f i c i a l u 2 0 0 e n e u r a l n e t w o r k ' , ' a n a p p r o p r i a t e m o d e l f o r p r e d i c t i n g o f l a n d f i l l i n g s i t e s e l e c t i o n o f m u n i c i p a l s o l i d w a s t e u 2 0 0 e i n f e r e y d u n s h a h r c i t y ' , ' w a s p r o p o s e d . f o r t h i s p u r p o s e ' , ' i n t h i s p a p e r ' , ' n e u r a l n e t w o r k i s t r a i n e d a n d t e s t e d u 2 0 0 e u s i n g m a t l a b 7 . 2 . . f o r t h i s p u r p o s e ' , ' 1 2 p r i m a r y f a c t o r s i n f l u e n c i n g m u n i c i p a l s o l i d w a s t e l a n d f i l l i n g i n u 2 0 0 e t h e s t u d y a r e a ' , ' i n c l u d i n g l i t h o l o g y ' , ' l e v e l o f g r o u n d w a t e r ' , ' s o i l t e x t u r e ' , ' d i s t a n c e t o h a b i t a t e ' , ' l a n d u s e ' , ' u 2 0 0 e s l o p e ' , ' a s p e c t ' , ' e l e v a t i o n ' , ' r a i n f a l l ' , ' d i s t a n c e t o f a u l t ' , ' d i s t a n c e t o r o a d ' , ' a n d d r a i n a g e d e n s i t y w e r e c h o s e n f o r u 2 0 0 e i m p u t l a y e r s . a l s o ' , ' f o r r e c o g n i z i n g t h e e f f e c t o f e a c h i n p u t d a t a s e n s i t i v e a n a l y s i s w a s p e r f o r m e d . f i n a l l y ' , ' u 2 0 0 e d i f f e r e n t s t r u c t u r e s o f a r t i f i c i a l n e t w o r k w e r e i n v e s t i g a t e d a n d t h e n t h e b e s t m o d e l f o r p r e d i c t i n g u 2 0 0 e l a n d f i l l i n g s i t e s e l e c t i o n o f m u n i c i p a l s o l i d w a s t e w a s c h o s e n b a s e d o n u 2 0 0 e m e a n s q u a r e d n o r m a l i z e d e r r o r u 2 0 0 e u 2 0 0 e ( m s e ) u 2 0 0 e ' , ' r o o t m e a n s q u a r e e r r o r ( r m s e ) a n d c o r r e l a t i o n c o e f f i c i e n t ( r 2 ) i n d e x e s . a f t e r p e r f o r m i n g o f t h e u 2 0 0 e m e n t i o n e d m o d e l s ' , ' m e a n s q u a r e d n o r m a l i z e d e r r o r ( m s e ) u 2 0 0 e ' , ' r o o t m e a n s q u a r e e r r o r ( r m s e ) a n d u 2 0 0 e c o r r e l a t i o n c o e f f i c i e n t ( r 2 ) i n n e u r a l n e t w o r k f o r t e s t h a v e b e e n a c h i e v e d e q u a l t o 0 . 0 0 8 1 ' , ' 0 . 1 1 a n d u 2 0 0 e u 2 0 0 e 0 . 9 9 9 % r e s p e c t i v e l y . r e s u l t s i n d i c a t e t h a t t r a i n l m m o d e l h a s m o r e a d v a n t a g e s i n c o m p a r i s o n w i t h t r a i n b p u 2 0 0 e a n d t r a i n b p x m e t h o d s i n l a n d f i l l i n g s i t e s e l e c t i o n o f m u n i c i p a l s o l i d w a s t e . a f t e r d e t e r m i n i n g t h e b e s t u 2 0 0 e n e t w o r k s t r u c t u r e ' , ' z o n a t i o n m a p o f t h e b e s t s i t e f o r l a n d f i l l i n g o f m u n i c i p a l s o l i d w a s t e u s i n g 1 2 i m p u t ' , ' u 2 0 0 e l a y e r w a s p r e p a r e d i n 5 c l a s s e s . t h e r e s u l t s s h o w e d t h a t 3 7 . 2 % ( 2 8 8 8 4 / 3 1 h a ) o f t h e t o t a l a r e a i s v e r y u 2 0 0 e s u i t a b l e f o r w a s t e l a n d f i l l i n g ' , ' 7 . 2 % ( 5 5 9 0 / 5 1 h a ) s u i t a b l e ' , ' 1 2 . 6 % ( 9 7 8 3 / 3 9 h a ) i s f a i r l y s u i t a b l e ' , ' 3 8 % u 2 0 0 e u 2 0 0 e ( 2 9 5 0 5 / 4 8 h a ) u n s u i t a b l e a n d 5 % ( 3 8 8 2 / 3 h a ) i s v e r y u n s u i t a b l e u 2 0 0 e k e y w o r d : m u l t i l a y e r s p e r c e p t r o n ' , ' s i t e s e l e c t i o n ' , ' w a s t e d i s p o s a l ' , ' f e r e y d u n s h a h r c i t y . u 2 0 0 e u 2 0 0 f u 2 0 0 e ' ]

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