Installation of Rainwater Harvesting (RWH) System for Combating the Problem of Water Scarcity Using Machine Learning and GIS

Global water scarcity has emerged as one of the most critical environmental challenges of the 21st century, affecting over 2.3 billion people worldwide. Rainwater Harvesting (RWH) represents a sustainable, cost-effective strategy for alleviating freshwater stress, particularly in semi-arid and drought-prone regions. This study presents an integrated framework that combines Machine Learning (ML) algorithms with Geographic Information System (GIS) spatial analysis to optimize the siting, design, and operational parameters of RWH systems in the Bangalore Urban and Rural Districts of Karnataka, India. Annual average rainfall in the study area ranges from 800 mm (Doddaballapura Taluka) to 1009 mm (Bangalore North Taluka), providing substantial harvestable potential. Using multi-criteria decision analysis (MCDA) integrated with Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN) models, we evaluated 14 spatial parameters including slope, soil texture, land use/land cover (LULC), drainage density, lineament density, and rainfall distribution across 5,824 km² of study area. The ML ensemble achieved an overall accuracy of 91.3% (AUC = 0.94) in delineating RWH suitability zones. GIS-based analysis identified 38.6% of the study region as highly suitable for RWH installation. Economic analysis using Benefit-Cost Ratio (BCR = 2.67), Net Present Worth (NPW = Rs. 8,240 per household), and Payback Period (PBP = 4.2 years) confirmed strong financial viability. The integrated ML-GIS model reduced site selection time by 73% compared to conventional methods and demonstrated that strategic RWH deployment could supplement 42% of annual household water demand in the region. This framework provides a scalable, replicable methodology applicable to data-scarce regions globally.