Comparing the Performance of Machine Learning Algorithms for Groundwater Mapping in Delhi

The problem of groundwater depletion has arisen as havoc in countries like India due to expanding intensive agriculture, growing population, and burgeoning urban centres. Delhi is one of the greatest urban agglomerations in the country facing severe groundwater depletion, but the robust methods for modelling the groundwater have not yet been adopted for examining the conditions of the groundwater. In such scenarios, accurate modelling of groundwater resources using appropriate techniques and tools is essential. The present study aimed to investigate groundwater level using GIS tools and machine learning algorithms and find the best models for application. The previous studies conducted are purely based on GIS methods without the possibility of accuracy determination of the results. Thus, in this study, boosted regression tree, generalized linear model (GLM), and neural net multi-layer perceptron (NNET-MLP) were applied for modelling the groundwater table in the capital city of India (i.e. Delhi). Anthropogenic, physiographic, meteorological, and hydrological factors like LULC, geology, elevation, slope, aspect, curvature, soil permeability, LST, precipitation, stream power index, and topographic wetness index are supplied as conditioning factors. The performances of the models were compared using area under curve (AUC) plot and correlation (COR). The AUC plot appears well above the diagonal line, showing acceptable results for all the models. The COR is maximum for the NNET-MLP, i.e. 0.93, while minimum value is for GLM, i.e. 0.60. The modelled rasters represented variable groundwater depths, and the mean of each district of Delhi is calculated. This is one of the first studies where GIS and machine learning are integrated to model the groundwater level of Delhi and hence open new prospects for research focussing on the capital of the country.