Addressing water scarcity challenges through rainwater harvesting: A comprehensive analysis of potential zones and model performance in arid and semi-arid regions–A case study on Purulia, India

Abstract

Water scarcity in arid and semi-arid regions is a critical global concern, necessitating innovative solutions to address increasing water demands in these vulnerable areas. This study focuses on tackling this challenge by identifying and classifying rainwater harvesting zones based on their potentiality and comparing the performance of two machine learning models, Artificial Neural Network (ANN) and Random Forest (RF), for optimizing rainwater harvesting strategies. The study area is Purulia, a district in India. Extensive literature review was conducted to identify key factors influencing rainwater harvesting. Open-source remotely sensed data were employed to pinpoint rainwater harvesting potential zones. A multi-criteria decision-making technique was applied to assess the importance of various factors. Results indicated that rainfall, slope, runoff potential, soil, land cover, and drainage density are the six crucial factors for selecting suitable rainwater harvesting locations. Approximately 2% of the area is unsuitable, 8% is poorly suitable, 33% is moderately suitable, 45% is highly suitable, and the remaining 12% is extremely suitable in Purulia. Two predictive models were developed, with the RF algorithm demonstrating nearly 99% accuracy. Finally, remedial techniques for mitigating water scarcity through rainwater harvesting are discussed separately for urban and rural areas. This research article embraces a comprehensive approach to address water-related concerns, offering a replicable framework applicable globally, with a specific focus on arid and semi-arid regions.