Analysis of Heavy Metal Concentration in Soils of a Waste Disposal Site in Khulna using Artificial Intelligence Techniques

Abstract

The collection of soil samples is labored and time consuming as well as the determination of heavy metal concentrations in laboratory was expensive. To these attempts, artificial intelligence techniques (AI) such as adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and artificial neural networks (ANN) were implemented for the analysis of heavy metal concentrations in soils of a selected waste disposal site at old Rajbandh, Khulna. The aim of this study was to fix the functions, algorithms, optimization methods for AI techniques based on their best performance and then select a best technique for the analysis of heavy metal concentrations in soils. In this study, soil samples were collected from eighty-five locations at a depth 0-30 cm from the existing ground surface from the selected disposal site. In the laboratory, the concentrations of heavy metals of Pb, Cu, Ni, Zn, Co, Cd, As, Sc, Hg, Mn, Cr, Ti, Sb, Sr, V and Ba in soils were measured. Result reveals the model with SCP, gaussmf, linear and hybrid was the best-fitted model of ANFIS for the prediction of heavy metal concentrations in soils. In addition, in SVM analysis, the model SVM-RBF with 15 folds was selected for the prediction of heavy metal concentrations in soils. In ANN, the model LT (Levenberg-marquardt and Tansig functions) with neuron structure 2-10-1 was selected. The accuracy of the predicted results were checked based on the acceptable limits of prediction parameters like R value, RMSE, MAPE, GRI and percentage recovery. Among all heavy metals analysis in ANFIS, the maximum R-value 0.999 was found with the minimum RMSE 0.12 for Sc indicating the best correlation in prediction of Sc in soils. The others value of prediction parameters (MAPE= 36.00, GRI=1.50, percentage recovery=123.43%) for Sc were found within the acceptable limits. In addition, in SVM analysis, maximum R-value 0.73 with RMSE 2.03 was found for Cu; while, maximum R-value 0.88 with the minimum RMSE 1.01 for As was found in ANN. The results demonstrated that ANFIS model was a reliable technique than that of other counterparts of SVM and ANN to analyse the heavy metal concentrations in soils with the acceptable degree of robustness and accuracy. Therefore, the performance of AI techniques may be expressed by the sequence of ANFIS > SVM > ANN. Here it can be noted that one can easily be computed the concentration of a particular heavy metal in soils by inserting GPS values (latitude and longitude) only in the developed rule viewer of ANFIS. Therefore, this newly developed model will further be helpful for other researchers in this line to analysis heavy metal concentration in soils of selected waste disposal sites.