1. Prediction of rural domestic water and sewage production based on automated machine learning in northern China.
- Author
-
Cao, Yingnan, Wang, Zixuan, Li, Pengyu, Zhou, Ziyu, Li, Wenkai, Zheng, Tianlong, Liu, Jianguo, Wu, Wenjun, Shi, Zhining, and Liu, Junxin
- Subjects
- *
SEWAGE , *RESIDENTIAL water consumption , *MACHINE learning , *SEWAGE purification , *WATER consumption - Abstract
Determining the rural domestic water consumption (RDWS, L/person-day) and rural sewage production (RSP, L/person-day) of rural residents at the county scale can provide decision support for rural domestic sewage treatment. However, methods for predicting county-scale RDWS and RSP based on geographical, social, and governance factors are rarely reported. This paper proposes an RDWS and RSP prediction method based on Automated Machine Learning (AutoML) for the Inner Mongolia Autonomous Region. The RDWS and RSP in the Inner Mongolia Autonomous Region were successfully predicted from multi-source data. The AutoML model accurately predicted RDWS (mean absolute error = 1.7072, R2 = 0.7759) and RSP (mean absolute error = 0.6580, R2 = 0.65107). Average years of education (AYOS), proportion of resident population, and proportion of elderly and children (POEAC) were key driving factors related to RDWS prediction. The most important factors associated with RSP prediction were AYOS, POEAC, and annual average precipitation (AAP). An explanatory analysis further revealed dependence between the input variables, RDWS, and RSP. The framework was demonstrated to be a practical tool for qualitative and quantitative analyses of rural domestic water consumption and domestic sewage production in the Inner Mongolia Autonomous Region. Overall, this study provides a solution for predicting county-level RDWS and RSP, providing a reference for the design of rural domestic sewage treatment. [Display omitted] • Less than 24% of rural domestic sewage is currently treated in Inner Mongolia. • Planning needs county-scale domestic water consumption and sewage production data. • Best of many models based on data from multiple sources were selected automatically. • Nonlinear relationships between input output variables were quickly revealed. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF