Feier Liu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Feier Liu has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Dongyuan Sun
College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou 730070, China
1 shared publication - Guoyan Gao
College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou 730070, China
1 shared publication - Xingfan Wang
College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou 730070, China
1 shared publication - Yali Ma
College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou 730070, China
1 shared publication - Yanqiang Cui
College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou 730070, China
1 shared publication
Latest Publications
- Open Access
- 7 June 2026
- Published by MDPI AG in Agriculture
- Vol. 16 (12) , 1263
Spatiotemporal Variation Characteristics and Prediction of Water Resource Carrying Capacity in Gansu Province Based on Machine Learning
Water Resource Carrying Capacity (WRCC) is a crucial measure for assessing the balance between regional water availability, socioeconomic development, and ecological needs, especially in arid and semi-arid regions. This study evaluates the spatiotemporal evolution of WRCC across 14 prefecture-level units in Gansu Province, China, from 2000 to 2023. A multi-dimensional evaluation system comprising 29 indicators across water resources, ecological environment, economy, society, and coordination subsystems was established. The Entropy Weight Method was applied to determine indicator weights and calculate a comprehensive index (CI) to quantify carrying pressure. A Random Forest model identified dominant influencing factors, and an autoregressive integrated moving average model projected trends from 2024 to 2028. The results show the provincial mean CI increased from 0.49 to 0.91, indicating intensifying pressure and a shift toward mild overload. Spatially, pressure exhibits a stable west–east gradient, with the highest levels persistently in western prefectures like Jiuquan, Jinchang, and Baiyin. In contrast, Gannan and Longnan in the south maintain lower pressure but show high interannual variability, indicating ecological sensitivity. The Random Forest model demonstrated strong performance, with training R2 values exceeding 0.88 across all regions and mean absolute error mostly below 0.10. Projections suggest continued high pressure from 2024 to 2028 in the west, while central and southern regions show stable or slightly decreasing trends. These findings provide a quantitative basis for establishing differentiated, zoned water resource management and sustainable demand-side regulation strategies in water-limited regions.
- 7 June 2026
- Published by MDPI AG in Agriculture
- Vol. 16 (12) , 1263
Open AccessSpatiotemporal Variation Characteristics and Prediction of Water Resource Carrying Capacity in Gansu Province Based on Machine Learning
Water Resource Carrying Capacity (WRCC) is a crucial measure for assessing the balance between regional water availability, socioeconomic development, and ecological needs, especially in arid and semi-arid regions. This study evaluates the spatiotemporal evolution of WRCC across 14 prefecture-level units in Gansu Province, China, from 2000 to 2023. A multi-dimensional evaluation system comprising 29 indicators across water resources, ecological environment, economy, society, and coordination subsystems was established. The Entropy Weight Method was applied to determine indicator weights and calculate a comprehensive index (CI) to quantify carrying pressure. A Random Forest model identified dominant influencing factors, and an autoregressive integrated moving average model projected trends from 2024 to 2028. The results show the provincial mean CI increased from 0.49 to 0.91, indicating intensifying pressure and a shift toward mild overload. Spatially, pressure exhibits a stable west–east gradient, with the highest levels persistently in western prefectures like Jiuquan, Jinchang, and Baiyin. In contrast, Gannan and Longnan in the south maintain lower pressure but show high interannual variability, indicating ecological sensitivity. The Random Forest model demonstrated strong performance, with training R2 values exceeding 0.88 across all regions and mean absolute error mostly below 0.10. Projections suggest continued high pressure from 2024 to 2028 in the west, while central and southern regions show stable or slightly decreasing trends. These findings provide a quantitative basis for establishing differentiated, zoned water resource management and sustainable demand-side regulation strategies in water-limited regions.