Use of Socio-economic, Climatic, and Land use Land Cover Patterns in Solid Waste Forecasting with Integrated Gradient LSTNet Based Model in Lomé, Togo

Few studies have explored recurrent neural network models in municipal solid waste (MSW) generation forecasting regarding the inefficiency of conventional and probabilistic tools. This study aimed to develop a new approach of Integrated Gradient Long- and Short-term Time series Network (LSTNet+IG)-based models, assess the baseline neural network models, and provide features influences explanation on MSW generation by using socio-economic, climatic, and Land Use Land Cover (LULC) patterns. Hence, the Random Forest Regressor and Gradient Boosting Regressor methods were used for feature selection, and the IG component was incorporated to LSTNet model for feature influence explanation alongside baseline models implementation. Moreover, the metrics such as root relative square error (RSE) and relative absolute error (RAE) were used to evaluate models’ performance. As a result, the LSTNet+IG outperforms others with the lowest RSE (0.9727) and RAE (0.9108). Moreover, five key influencing features were found, namely the number of households and institutions, mean relative humidity, electricity consumption, built area, and mean temperature. Hence, the socio-economic, climatic, and LULC features in MSW generation forecasting have shown to be important in decision-making for effective MSW management. Therefore, the LSTNet+IG model adoption by stakeholders could help to anticipate and mitigate MSW mismanagement through better planning.