Remote sensing and machine learning approach for zoning of wastewater drainage system

Abstract

This study aimed to optimize drainage in Tiruchirappalli city using Geographic Information Systems (GIS), machine learning, and remote sensing. The integration of these methods involved multiple steps: remote sensing data was used to gather up-to-date information on land use and land cover (LULC), GIS provided a spatial framework for data integration and visualization, and machine learning, specifically a Random Forest (RF) model, was utilized for detailed LULC classification. Hydrological analysis using a Digital Elevation Model (DEM) helped delineate the watershed and drainage network, determining suitable zones for wastewater systems. Key factors such as ground slope, land use, and proximity to water bodies were considered. Buffer analysis was employed to transform and overlay these data layers. For the site suitability analysis, the Analytic Hierarchy Process (AHP) method was employed within the GIS to integrate various weighted factors, enabling a comprehensive assessment of potential locations for wastewater treatment facilities. Wastewater drainage zones were determined to be appropriate for zones 1, 8, 9, 11, 15, and 20, which correspond to areas of 40.40, 1.11, 1.00, 2.04, 1.69, and 2.46 Sq.km, respectively. This comprehensive approach enabled the identification of optimal wastewater drainage zones and suitable locations for treatment facilities, enhancing urban wastewater management.