Monitoring, classification and analysis of waste disposal sites using Machine Learning

Abstract

Lack of planning and regulations around the landfills has resulted and continues to result in severe environmental damage to the immediate environment around the landfills. Our study systematically reviews the literature to understand different processes of monitoring and analysis of a waste disposal site. It further analyses a satellite footprint from Google Earth Engine, of the western part of India around the urban area of Bombay. For the satellite footprint, we compare different algorithms and satellites for detecting landfills using machine learning. We conduct a supervised classification for satellite images for Land Satellite Applied to Remote Sensing (LANDSAT) (2013 to 2023) and SENTINEL (2018 to 2023) using three different classification algorithms: CART (Classification and Regression Tree), Naive Bayes and SVM (Support Vector Machine). The LANDSAT SVM model was generally the most stable and consistently performed well. The model consistently has one of the highest accuracy scores over the years, followed closely by SENTINEL SVM. The analysis can be replicated to other cities and other large-area studies, and can act as a pointer in doing further analysis of the landfill that can further be used to prevent the effects of the waste disposal site on its surrounding environment.