## 2 Related Work  
Current waste management practices often prioritize minimizing economic costs while overlooking social and environmental considerations. However, with the growing public awareness of environmental issues and the implementation of policy initiatives, green objectives--particularly the reduction in vehicle carbon emissions--have become critical in planning waste management systems [4; 5; 6; 7; 8; 9]. The demand for municipal waste management can be summarized as the prediction of municipal domestic waste production needs to consider a variety of factors, which is due to the more complex and heterogeneous process of municipal domestic waste disposal. Nowadays, the main research factors focus on three aspects: social indicators, economic indicators and demographic indicators, among which social indicators mainly include household education level [10] and urban greening coverage [11]; economic indicators include GDP, per capita GDP, per capita consumption expenditure, per capita disposable income, household income, residential consumption level, and total retail sales of social consumer goods [11; 12]; and demographic indicators mainly include population size [13], population distribution [13], and overall education level [14]. In this paper, we collected a large number of relevant indicators in terms of traditional urban data indicators, and finally selected four indicators, namely, the number of people registered in the population,per capita disposable income, per capita consumption expenditure, and the number people of resident in the population, as influencing factors. In recent years, significant progress has been made in the prediction of municipal solid waste (MSW) generation and the optimization of waste management systems. However, some shortcomings remain, particularly in addressing data complexity, prediction accuracy, and model robustness. Firstly, earlier studies have relied mainly on traditional statistical methods, such as linear regression [14] and time series analysis [15]. For example, [PERSON] et al. [16] used time series analysis to predict waste production in a major city in China, despite the simple and straightforward approach, whose model performed poorly in capturing nonlinear relationships of the data. When the urban structure is complicated, the traditional methods show obvious limitations in handling the heterogeneity of the data, which limits the prediction accuracy of the model [17; 18]. Traditional statistical methods usually consider only a single data source (such as population density, economic activities, etc. ), but ignore the timeliness of the urban dynamic changes. Accurately predicting the generation of municipal solid waste (MSW) is essential for effective planning and management of waste disposal facilities, such as landfills. Previous studies have utilized various models to estimate MSW generation and optimize landfill areas. [PERSON] and [PERSON] [3] estimated landfill areas based on solid waste collection predictions using an artificial neural network (ANN) model and final waste disposal options. Their study highlighted the effectiveness of ANN models in forecasting waste generation and optimizing landfill space, providing a more accurate and dynamic approach compared to traditional methods. Moreover, the integration of remote sensing data has shown potential in enhancing the accuracy of MSW generation predictions. For example, [PERSON] and [PERSON] [19] tested the new VIIRS lights data set for estimating population and economic output in Africa. This work demonstrated that satellite-derived night light data could be a valuable proxy for understanding urbanization levels and economic activities, which are closely related to the generation of MSW. By incorporating such data into predictive models, researchers can better account for spatial and temporal variations in waste production, leading to improved planning and management strategies.