Revolutionising waste management with the impact of Long Short-Term Memory networks on recycling rate predictions

Abstract

This study explores the efficacy of Long Short-Term Memory (LSTM) networks in predicting recycling rates and enhancing resource allocation in waste management systems. It addresses the limitations of traditional statistical models and machine learning algorithms that struggle with sequential data and temporal dependencies. The methodology comprised collecting extensive datasets from public repositories, configuring the LSTM network architecture, training the model with historical data, and testing various activation functions and hyperparameters. The model’s performance was rigorously compared to traditional models and alternative machine learning algorithms using metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R2). The findings demonstrate that the LSTM model significantly outperformed traditional approaches, achieving an MAE of 3.5%, an RMSE of 2.8%, and an R2 of 0.92. These results underscore the superior capability of LSTM networks to capture complex temporal patterns in recycling data, offering substantial improvements in predictive accuracy and reliability. Consequently, the study highlights the potential of LSTM networks to revolutionize waste management strategies, contributing to more effective and sustainable practices.

Keywords

• LSTM Networks
• Recycling Prediction
• Waste Management
• Machine Learning
• Sustainability