Plastic Waste Leakage in Urban River Basins of Sri Lanka: A Geospatial and Machine Learning Perspective

Abstract

Plastic waste leakage into the urban river basins has emerged as a critical environmental challenge in Sri Lanka, threatening aquatic ecosystems, public health, and urban resilience. Rapid urbanization, inadequate waste management, and poor drainage infrastructure have accelerated the accumulation and transport of plastic debris into waterways. This study presents a geospatial and machine learning approach to identify and model the key drivers of plastic waste leakage in selected urban river basins. High-resolution geospatial datasets, including land use, population density, slope, rainfall patterns, and proximity to waste disposal sites, were integrated with field-based leakage observations. A Random Forest classifier was employed to predict leakage hotspots, achieving a moderate level of accuracy. Feature importance analysis highlighted waste site proximity, urban density, and hydrological factors as the dominant predictors. The confusion matrix further illustrates the model's strengths in identifying high-risk zones; however, misclassification in low-risk areas suggests potential improvement through the inclusion of additional environmental and socio-economic variables. The findings offer actionable insights for urban planners, waste management authorities, and policymakers to develop targeted interventions for mitigating plastic waste in riverine environments.