Citation :

K. Priya, R. Surendiran, K. Sujith, N. Vanjulavalli, "An Energy Efficient Model to Forecast Solid Waste Generation using Green Artificial Intelligence Methodology," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 3, pp. 104-114, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I3P108

Abstract

Forecasting Municipal Solid Waste Generation (MSWG) is the need of the hour all around the world. Every day, researchers are developing numerous models to accurately predict waste generation to take immediate action. The models have been constructed using neural networks that require powerful GPUs or TPUs to compute. These machines exhibit high carbon footprints and harmful gases. Accurate prediction is a must; on the other hand, controlling Greenhouse Gas (GHG) emissions is in high demand. Keeping this as the objective, in this article, a study was conducted to identify an efficient model that effectively forecasts the MSWG, which is resilient to the number of incoming features. Most of the researchers have used XGBoost, Long Short-Term Memory (LSTM), and Temporal Convolutional Networks (TCNs) to predict and made these models the base models for predictive analytics. Hence, these models were compared with the Spike Neural Networks (SNNs), a mimic of the human membrane working principle. The effectiveness of these models is evaluated using the Peru MSWG dataset, and the results showed 99% accuracy by all 4 models on a Google Colab T4 GPU environment. The computational time and Carbon dioxide (CO2) emission were recorded. The SNNs are very good in prediction with less computational time, but they emitted considerable CO2 emissions when compared to all three other models. For future deployment efficiency on edge computing, SNNs may show promising results on a Neuromorphic hardware.

Keywords

Energy Efficient Model for prediction, Forecasting Solid Waste Generation, Green AI, Municipal Solid Waste Generation, Spike Neural Networks.

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