modeling of groundwater resources heavy metals concentration using soft computing methods: application of different types of artificial neural networks

nowadays, groundwater resources play a vital role as a source of drinking water in arid and semiarid regions and forecasting of pollutants content in these resources is very important. therefore, this study aimed to compare two soft computing methods for modeling cd, pb and zn concentration in groundwater resources of asadabad plain, western iran. the relative accuracy of several soft computing models, namely multi-layer perceptron (mlp) and radial basis function (rbf) for forecasting of heavy metals concentration have been investigated. in addition, levenberg-marquardt, gradient descent and conjugate gradient training algorithms were utilized for the mlp models. the ann models for this study were developed using matlab r 2014 software program. the mlp performs better than the other models for heavy metals concentration estimation. the simulation results revealed that mlp model was able to model heavy metals concentration in groundwater resources favorably. it generally is effectively utilized in environmental applications and in the water quality estimations. in addition, out of three algorithms, levenberg-marquardt was better than the others were. this study proposed soft computing modeling techniques for the prediction and estimation of heavy metals concentration in groundwater resources of asadabad plain. based on collected data from the plain, mlp and rbf models were developed for each heavy metal. mlp can be utilized effectively in applications of prediction of heavy metals concentration in groundwater resources of asadabad plain.