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This study examines the intricate relationship between Delhi's urban water system and environmental factors, with a specific emphasis on the challenges posed by urbanization. It investigates the present conditions of the water resources, infrastructure, and governance in the National Capital Territory (NCT) of Delhi and identifies significant difficulties, such as depletion of groundwater and inadequate access to water. Additionally, it explores innovative approaches to strengthen supply internalization, such as rainfall harvesting, wastewater recycling, and demand management strategies. Therefore, this document establishes a path for future research endeavors and policy suggestions that aim to promote a fair, effective, and environmentally sustainable future for water 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.