Predicting Municipal Solid Waste Generation Using Machine Learning

Municipal Solid Waste generation is a byproduct of daily human activities, originating from households, offices, commercial enterprises, and other establishments, with the rise in global population, coupled with economic development. The proportion of waste being generated has risen over the years, presenting notable challenges to waste management systems worldwide. Accurate prediction of Municipal Solid waste is important for waste management organizations to plan and make efficient waste management strategies. This thesis explores the utilization of machine learning approaches in predicting municipal solid waste generation accurately for a specific geographic region, concentrating on the city of Boralesgamuwa's in the Colombo District of Sri Lanka based on historical waste generation patterns. Additionally, the research incorporates feature engineering to enrich the dataset by adding relevant features to it using the pandas datetime library. This research is based on two distinct prediction methods, a single-model approach and a multimodel ensemble approach, while incorporating feature engineering. The dataset used in this investigation has historical municipal solid waste.