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.

中文翻译：

---

使用机器学习模型预测垃圾填埋场的甲烷排放

由于固体废物特性的异质性以及导致甲烷生成的不同化学和物理反应，对甲烷排放进行建模具有挑战性。这项研究的重点是监测垃圾填埋场产生的甲烷，并使用机器学习技术模拟甲烷排放。因此，建设了两个试点垃圾填埋场，总容量为 9327 吨城市固体废物。对试验垃圾填埋场的温度、甲烷和渗滤液产生量进行了 3 年的测量。评估了渗滤液再循环系统对垃圾填埋场甲烷排放的影响，结果表明，在垃圾填埋过程中不使用渗滤液再循环系统时，甲烷排放量降低了 35%。三种机器学习模型，包括人工神经网络，自适应神经模糊推理系统和支持向量机首次被用于预测甲烷的产生。结果表明，支持向量机模型在预测甲烷生成方面优于自适应神经模糊推理系统和人工神经网络模型。支持向量机模型能够分别捕获有和没有渗滤液再循环的垃圾填埋场甲烷排放量的 90% 和 82% 的变化。总的来说，机器学习模型在预测甲烷生成方面显示出相当大的潜力。结果表明，支持向量机模型在预测甲烷生成方面优于自适应神经模糊推理系统和人工神经网络模型。支持向量机模型能够分别捕获有和没有渗滤液再循环的垃圾填埋场甲烷排放量的 90% 和 82% 的变化。总的来说，机器学习模型在预测甲烷生成方面显示出相当大的潜力。结果表明，支持向量机模型在预测甲烷生成方面优于自适应神经模糊推理系统和人工神经网络模型。支持向量机模型能够分别捕获有和没有渗滤液再循环的垃圾填埋场甲烷排放量的 90% 和 82% 的变化。总的来说，机器学习模型在预测甲烷生成方面显示出相当大的潜力。