Forecasting MSW generation using artificial neural network time series model: a study from metropolitan city

Forecasting the quantity of municipal solid waste generation is an essential task for sustainable solid waste management and strategy implementation. The estimation of future waste generation rates can help to motivate for analyzing gaps in existing waste management and better planning strategies. Improper management and unsafe disposal of solid waste create a threat to the environment and human health. Hence, a sound forecasting of solid waste generation is very crucial for planning and management accordingly. Artificial intelligence is an excellent and new application of soft computing which is used as a forecasting tool. The main objective of this study is to apply ANN time series model along with autoregressive technique to forecast the monthly solid waste generation in Kolkata. For the same, data related to the monthly solid waste generation was gathered from 2010 to 2017. Total data of 96 months were divided into three categories, i.e., 70%, 15%, and 15% for training, validation, and testing, respectively. The model was evaluated based on performance value of mean square error, root mean square error, and regression coefficient. The ANN structure of 1-19-1 was considered as optimized model for solid waste forecasting because it has the lowest mean square error and the highest regression coefficient. The applied time series model forecasts that Kolkata will generate about 5205 MT/day municipal solid waste in 2030 which will add more than 1000 MT/day waste with the existing rate of generation. The present study helps in estimating and allocating essential resources that need in future for sound solid waste management and preparing alternative strategies to reach the sustainable goals.