Use of neural networks and spatial interpolation to predict groundwater quality

Abstract

The artificial neural networks share its working analogous with the human brain; and by using these artificial neural models, various complex nonlinear relationships can be modeled which cannot be described easily using mathematical equations. In this study, groundwater quality at a sanitary landfill site used for solid waste disposal was modeled using artificial neural networks. The groundwater quality was assessed for two consecutive years 2016 and 2017 at ten locations near the site, and the data were used for modeling. Total hardness was predicted using neural networks by using three learning algorithms, and the best one was used in the final model for prediction. The interpolation maps were drawn for both the years to understand the total hardness concentrations at unsampled locations using ArcGIS Geostatistical Analyst Extension, and Inverse Distance Weighing method was used. The percentage effect of spatial and temporal changes on total hardness was calculated by doing the sensitivity analysis and thus finding the relative importance of each input parameter on total hardness. Different algorithms were tested to select the best-performing algorithm with optimal neural architecture.