A multi-model forecasting approach for solid waste generation by integrating demographic and socioeconomic factors: a case study of Prayagraj, India.

Abstract

Projecting municipal solid waste generation and identifying socioeconomic factors affecting waste generation is crucial for integrated waste management strategies. The present research work focuses on the projection of municipal solid waste (MSW) generation in Prayagraj, India, based on demographics and socioeconomic factors, using long short-term memory (LSTM), autoregressive integrated moving average (ARIMA), and incremental increase models (IIM). The model was integrated with nine socioeconomic variables to improve accuracy. The influence of socioeconomic variables on MSW generation was evaluated using correlation and fuzzy logic approaches. Waste generation data collected from the Central Pollution Control Board (CPCB) from 1997 to 2015 were used to train the models. The results of the correlation study indicate that population growth, employment, and households have a substantial impact on waste generation rates. Root mean squared error (RMSE), mean absolute percent error (MAPE), and coefficient of determination (R2) suggest that LSTM is the best model to forecast MSW generation in Prayagraj, India. The R2 value indicates that the LSTM is more accurate (0.92) than ARIMA (0.72) and IIM (0.70). LSTM projection indicates that the city will have a population of 1.6 million by 2031, and waste generation will increase by 70.6% in 2031.