Owing to the importance of municipal waste as a determining factor in waste management, developing data-driven models in waste generation data is essential. In the current study, solid waste generation is taken as the function of several parameters, namely month, rainfall, maximum temperature, average temperature, population, household size, educated man, educated women, and income. Two different stand-alone computational models, namely, gene expression programming and optimally pruned extreme machine learning techniques, are used in this study to establish their reliability in municipal solid waste generation forecasting, followed by Mallow’s coefficient feature selection method. The lowest Mallow’s coefficient defines the optimal parameters in solid waste generation forecasting. The novel hybrid models of intrinsic time-scale decomposition-gene expression programming and intrinsic time-scale decomposition- optimally pruned extreme machine learning methods based on Monte-Carlo resampling are employed, and an empirical equation is presented for solid waste generation prediction. For examining the reliability of these models, five statistical criteria, namely coefficient of determination, root mean square error, percent mean absolute relative error, uncertainty at 95% and Willmott’s index of agreement, are implemented. Considering Willmott’s index, the Monte Carlo-intrinsic time-scale decomposition-gene expression programming model attains the closest value (0.957) to the ideal value in the training stage and 0.877 in the testing stage. The hybrid ensemble model of intrinsic time-Scale decomposition-gene expression programming presented lower values of root mean square error (12.279) and percent mean absolute relative error (4.310) in the training phase and in the testing, phase compared to gene expression programming with (12.194) and (5.195), respectively. Overall, the prediction results of the hybrid model of intrinsic time-scale decomposition-gene expression programming using Monte-Carlo resampling technique agrees well with the observed solid waste generation data.

由于城市垃圾作为垃圾管理决定性因素的重要性，在垃圾产生数据中开发数据驱动的模型是必不可少的。在目前的研究中，固体废物的产生被视为几个参数的函数，即月份、降雨量、最高温度、平均温度、人口、家庭规模、受过教育的男性、受过教育的女性和收入。本研究使用两种不同的独立计算模型，即基因表达编程和优化修剪的极端机器学习技术，以建立它们在城市固体废物产生预测中的可靠性，然后是 Mallow 系数特征选择方法。最低的锦葵系数定义了固体废物产生预测中的最佳参数。采用基于蒙特卡罗重采样的内在时间尺度分解-基因表达编程和内在时间尺度分解-优化剪枝极端机器学习方法的新型混合模型，并提出了固体废物产生预测的经验方程。为了检查这些模型的可靠性，实施了五个统计标准，即决定系数、均方根误差、平均绝对相对误差百分比、95% 的不确定性和威尔莫特的一致性指数。考虑到威尔莫特指数，蒙特卡洛内在时间尺度分解基因表达编程模型在训练阶段达到最接近理想值的值（0.957），在测试阶段达到0.877。与基因表达编程相比，内在时间尺度分解-基因表达编程的混合集成模型在训练阶段和测试阶段呈现出较低的均方根误差 (12.279) 和百分比平均绝对相对误差 (4.310)。分别为 (12.194) 和 (5.195)。总体而言，使用蒙特卡罗重采样技术的内在时间尺度分解-基因表达编程混合模型的预测结果与观察到的固体废物产生数据非常吻合。