Application of neural network of Multi Layers Perceptron (MLP) in site selection of waste disposal (Case ‎study: fereydoonshahr city)‎

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 unsuitable‎

Keyword: Multi Layers Perceptron, Site Selection, Waste disposal, Fereydunshahr City.‎


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