Future projections of municipal solid waste (MSW) generation trends can resolve data inadequacy in formulating a sustainable MSW management framework. Artificial neural network (ANN) has been recently adopted to forecast MSW generation, but the reliability and validity of the stochastic forecast are not thoroughly studied. This research develops Bayesian-optimised ANN models coupling ensemble uncertainty analysis to forecast country-scale MSW physical composition trends. Pearson correlation analysis shows that each MSW physical composition exhibits collinearity with different indicators; therefore, the MSW should be forecasted based on its heterogeneity. The Bayesian-optimised ANN models forecast with smaller relative standard deviations (3.64–27.7%) than the default ANN models (11.1–44,400%). Malaysia is expected to generate 42,873 t/d of MSW in 2030, comprising 44% of food waste. This study provides a well-generalised ANN framework and valuable insights for the waste authorities in developing a circular economy via proper waste management.

对城市固体废物 (MSW) 产生趋势的未来预测可以解决数据不足的问题，以制定可持续的 MSW 管理框架。人工神经网络（ANN）最近已被用于预测 MSW 的产生，但随机预测的可靠性和有效性尚未得到深入研究。本研究开发了贝叶斯优化的人工神经网络模型，结合集合不确定性分析来预测国家规模的 MSW 物理成分趋势。Pearson相关分析表明，每个MSW物理成分与不同指标呈现共线性；因此，MSW 应根据其异质性进行预测。贝叶斯优化的 ANN 模型预测的相对标准偏差 (3.64–27.7%) 小于默认 ANN 模型 (11.1–44,400%)。马来西亚预计将产生 42 个，到 2030 年，每天产生 873 吨 MSW，占食物垃圾的 44%。这项研究为废物管理部门通过适当的废物管理发展循环经济提供了一个通用的人工神经网络框架和有价值的见解。