Remote Sensing and Predictive Modeling Approaches for Thermal Dynamics of Municipal Solid Waste Dumpsite Integrating Biomining Effect

Abstract A heat recovery and management system for municipal solid waste decomposition in an open dumpsite is crucial for preventing urban heat island effects and reducing greenhouse gas emissions. The temperature rise has reached the ignition point in open dumpsites, increasing serious environmental issues. The present study systematically evaluated heat output from the Ariyamangalam dumpsite in Tiruchirappalli, using landfill degradation and transport equations combined with thermodynamic principles, and estimating smoldering potential for different waste fractions. The results indicate that the existing physical composition produces 1,348 MJ per tonne of waste. A combined approach of remote sensing and machine learning was adopted to investigate surface thermal characteristics and predict future surface temperatures. Thermal infrared sensor data and high-resolution satellite images were used to derive the spatial distribution of temperature. The thermal footprint mapping was conducted in ArcGIS (version 10.4.1), and variations in surface temperatures were correlated with biomining activities, which increased temperatures by 2°C–5°C across seasons. At the same time, the random forest algorithm identified surface temperature trends using Landsat imagery from 2004 to 2024. The random forest executed well with an out-of-bag error of 9.5% and an R 2 of 0.81. The outcomes highlight the significant thermal load caused by waste decomposition and biomining. Without efficient recovery, heat accumulation at the dumpsite is a growing concern, intensifying fire risks and local climate stress.