混合PSO-SVM和象征回归模型用于农业用水需求预测
Hong Lv1, Yuting Zhao1, Wei Wang2
1Yellow River Engineering Consulting Co., Ltd., Zhengzhou, 450003, China.
Scientific reports
|January 13, 2026
概括
农业用水需求受到增长驱动因素和政策限制的影响. 巴扬努尔市 巴扬努尔市
科学领域:
- 农业用水管理农业用水管理
- 水资源可持续性 水资源的可持续性
- 粮食安全 粮食安全
背景情况:
- 农业用水对于粮食安全和可持续的水资源管理至关重要.
- 了解农业用水需求的进化机制对于有效规划至关重要.
- 拜安努尔市在平衡农业用水资源方面面临着挑战.
研究的目的:
- 确定控制巴扬努尔市农业用水需求的关键因素.
- 开发未来农业用水需求的预测模型.
- 分析农业用水中的驱动和限制因素之间的相互作用.
主要方法:
- 粒子集群优化-支持向量机 (PSO-SVM) 模型用于因子识别.
- 用于构建预测方程的符号回归.
- 分析用水的双向引导机制的分析.
主要成果:
- 农业用水需求是由有效灌面积和粮食播种面积驱动的.
- 高效灌率,水压力指数和农业用水价格作为线性限制因素.
- 巴扬努尔市的农业用水需求预计从2023年至2035年每年将超过50亿立方米,在2028年达到顶峰.
结论:
- 与政策相关的限制因素越来越多地主导着生产驱动的指标.
- 该研究为区域水资源管理提供了可靠的预测.
- 将机器学习与可解释的建模集成,为水资源管理框架提供了实际支持.
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