A Framework for Optimizing Rooftop Rainwater Harvesting Using Advanced Geostatistics and Hybrid Machine Learning

The present research aims to identify scientifically suitable locations for artificial recharge pits to minimize unnecessary construction and prioritize regions experiencing rapid groundwater depletion. As precise pit-level identification at the local scale remains challenging, this study proposes a robust methodology for delineating favorable recharge zones at broader spatial scales, using advanced geostatistical techniques.
Artificial rooftop rainwater-harvesting recharge pits are a promising solution for aquifer recharge. However, they depend heavily on rainfall and rooftop catchment, both of which are influenced by settlement patterns and the underlying geology. These systems work best in areas that receive sufficient rainfall and have a high concentration of multi-story buildings that can effectively collect rooftop runoff and pipe it into the underlying aquifers. Subsurface lithology largely determines infiltration, and hence, scientifically informed site selection is critically important for the sustainable planning of recharge.
The suggested analysis integrates several open-access data sources, including gridded rainfall data, groundwater level depth data (CGWB), high-resolution (10 m) land-use/land-cover data, and geological data. Groundwater level data are used to identify zones of severe, moderate, and improving groundwater conditions. In contrast, rainfall data sets are used to identify spatial patterns of rainfall concentration, and geological information ensures the correct hydraulic connectivity between the surface recharge structure and the aquifers.
Based on the findings, the proposed framework incorporates geostatistical modelling, simulation, and hybrid machine-learning methods to develop an optimization-based hydro-informatics framework for rooftop rainwater harvesting.