Prediction of methane emission from landfills using machine learning models

Abstract

Modeling the methane emission is challenging due to the heterogeneity of solid waste characteristics and different chemical and physical reactions leading to methane generation. This study focused on monitoring the methane generation from landfills and modeling methane emission using machine learning techniques. Hence, two pilot landfills were constructed with a total capacity of 9327 tons of municipal solid waste. The temperature, methane, and leachate generation from the pilot landfills were measured for 3 years. The effect of leachate recirculation system on methane emission from landfill was evaluated, and the results showed that the methane emission was 35% lower when leachate recirculation system was not utilized in the landfilling process. Three machine learning models, including artificial neural networks, adaptive neuro-fuzzy inference system, and support vector machine, were used for the first time to predict methane generation. Results demonstrated that the support vector machine model was superior to both the adaptive neuro-fuzzy inference system and artificial neural network models for predicting methane generation. The support vector machine model was able to capture 90% and 82% of the variation in methane emission from landfills with and without leachate recirculation, respectively.In general, machine learning models showed considerable potential for forecasting methane generation.

Artificial intelligence for waste management in smart cities: a review

“…The waste landfill has become the most critical waste disposal method in most countries because of its large-scale operation and simple management (Mehrdad et al 2021 ). However, landfills also present problems with siting, leachate, and odors (Abunama et al 2019 ; Mohsin et al 2022 ; Xu et al 2022 ).…”

Prediction uncertainty and volatility for carbon price using an adaptive lower and upper bound estimation model

“…With the rise in artificial intelligence techniques, much research on carbon price prediction based on artificial intelligence methods has emerged. 18 Owing to their advantages in solving large-scale and complicated nonlinear problems, they have been favored by many scholars. 19 For example, Sun and Huang utilized an optimized backpropagation (BP) neural network model to predict carbon prices.…”

Municipal Solid Waste Forecasting in China Based on Machine Learning Models

“…Kumar et al (2018) applied it to the prediction of the production rate of plastic waste, and found that the prediction result of SVM (R 2 0.74) is better than RF (R 2 0.66) and lower than artificial neural network (ANN) (R 2 0.75). Mehrdad et al (2021) argued that SVM was superior to both the adaptive neuro-fuzzy inference system and artificial neural network models in predicting methane generation.…”

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