## 2 Related Work
With the continuous advancement of machine learning, recent studies have applied machine learning algorithms to waste yield prediction, thereby overcoming the limitations of traditional methods in handling data heterogeneity. [PERSON] et al. [20] discussed the generation patterns and prediction methods of domestic waste in New York City. The authors used a gradient boosting model to predict short-term waste generation in New York City, demonstrating the effectiveness of the GBM model for short-term waste prediction. [PERSON] et al. [21] used six machine learning models to predict municipal household waste generation by selecting residential areas in Vietnam and found that the random forest and KNN algorithms had better predictive power for the training data, and the test data had R2 value exceeded 0.96. [PERSON] et al. [22] conducted an empirical study of solid waste data from five Canadian provinces using a multi-layer perceptron (MLP) and support vector regression (SVR) machine learning approach to develop a predictive model for municipal solid waste management in Canada. [PERSON] et al. [23] used multiple indicators and three different models to predict China's total future municipal waste, including population, urbanization rate, waste utilization rate, etc. The machine learning-based prediction model can effectively predict total future municipal waste in China with high prediction accuracy. [PERSON] et al. [24] used algorithms including linear regression, decision trees, support vector machines, and artificial neural networks, as well as some feature selection techniques to improve the prediction accuracy. The above findings suggest that machine learning models can predict domestic waste production more accurately and can provide a better reference for waste treatment and management. Based on the aforementioned research, we have selected six of the most frequently utilized machine learning algorithms from previous studies as the modeling tools for this research. The aforementioned studies have not considered remote sensing light data as a variable influencing waste generation in their predictive models. This oversight in effectively capturing the spatiotemporal dynamics of urban socio-economic activities has consequently diminished the accuracy of waste generation predictions across both spatial and temporal dimensions [19; 25; 26]. However, this research presents an innovative modeling approach that integrates machine learning algorithms with remote sensing light data to estimate waste production. The dual-objective model proposed by [PERSON] and [PERSON] [27] performed well in optimizing costs and environmental risks. However, this study assumed that waste output was fixed and ignored the random volatility of waste output. [PERSON] et al. [28] considered multiple objectives in the LRP model, but still did not fully consider the volatility of waste output. [PERSON] et al. [29] solved the random demand problem in vehicle path planning by using a multi-objective optimization algorithm, but the research was still not thorough enough in dealing with the volatility of garbage production. The green LRP model proposed by [PERSON] et al. [30] has made some contributions to optimizing the environmental impact of transportation and distribution, but these studies are mostly limited to fixed input data and lack adaptability to dynamic data. This study significantly improves the prediction accuracy and the robustness of the model by introducing remote sensing data and considering the volatility of waste production, providing a more comprehensive, dynamic, and practical solution for waste management systems compared to previous studies. These innovations give this study important theoretical value and practical potential in the field of waste management system optimization.