 Solid waste generation forecasts using long short-term memory approachAya Idrissi, Rajaa Benabbou, Jamal Benhra and Mounia El Haji International Journal of Applied Management Science, 2025, vol. 17, issue 2, 163-183 Abstract: In recent years, Artificial Intelligence (AI) has become increasingly prominent across various domains with the objective of emulating human intelligence, and no industry has escaped its powerful advancement, including the waste management sector. This study proposes a time-series forecasting method based on a Long Short-Term Memory (LSTM) network to accurately predict the amount of waste daily collected. The LSTM model was trained using data points that align with the same phase of the seasonal cycle as the forecasted point, this approach preserves the chronological order of the data while focusing on points corresponding to the same phase of the seasonal cycle. To better illustrate the LSTM neural network's accuracy and robustness, a Back-Propagation Artificial Neural Network (BP-ANN) based on the Levenberg-Marquardt training method was also used. The results demonstrated that the proposed LSTM outperformed in terms of accuracy and precision, proving its excellence in capturing the complex patterns. Keywords: solid waste generation; artificial intelligence; LSTM; long short-term memory; neural network; back propagation. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:injams:v:17:y:2025:i:2:p:163-183 Access Statistics for this article More articles in International Journal of Applied Management Science from Inderscience Enterprises Ltd |
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