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机器学习算法的应用,用于预测纳贾法巴德平原地下水位

Sahar Davari¹, Saeid Eslamian¹, Mohammad Jamali²

¹Department of Water Science and Engineering, College of Agriculture, Isfahan University of Technology, Isfahan, 84156 - 83111, Iran.

Scientific reports

|December 10, 2025

概括

极端梯度提升 (XGBoost) 准确地使用气候和人为因素预测地下水位. 这种机器学习方法对于干旱地区的可持续水资源管理至关重要.

科学领域:

环境科学环境科学
水文学的水文学
数据科学数据科学数据科学

背景情况:

准确地预测地下水位对于可持续的水资源管理至关重要,尤其是在干旱和半干旱地区.
了解气候,水文和人为因素的相互作用是有效管理水资源的关键.

研究的目的:

评估和比较三个机器学习模型的性能:极端梯度增强 (XGBoost),随机森林 (RF) 和支持向量机器 (SVM).
预测伊朗纳贾法巴德平原各种水文地质区的地下水位.
通过整合各种影响因素来确定预测地下水位的最有效模型.

主要方法:

使用了气候 (降水,温度),水文 (以前的地下水位) 和人为 (灌,地下水抽取) 数据.
应用和评估 XGBoost,RF 和 SVM 机器学习模型用于地下水位预测.
在五个水文地质区使用R2,RMSE,MAE,WI和PBIAS等指标评估模型性能.

主要成果:

XGBoost表现出卓越的预测技能,平均R2为0.8480和最小偏差 (PBIAS +0.0400%).
射频实现了中等准确性 (R2 = 0.7480),而SVM显示了强度训练过度装配的迹象,但测试性能较差 (R2 = 0.8220).
这些模型在异构的水文地质条件中表现出稳定性,XGBoost显示了最低的PBIAS范围 (-1.7000%至+2.4000%).

结论:

集成方法,特别是XGBoost,对于准确地预测地下水位非常有效.
整合气候和人为驱动因素对于稳健和可持续的含水层管理至关重要.
该研究为数据稀缺的干旱和半干旱地区的地下水资源管理提供了可靠的框架.

关键词:

极端的梯度增强了极端的梯度.预测地下水位的预测机器学习是机器学习.纳贾法巴德平原是一个平原.随机的森林随机的森林支持矢量机器的支持矢量机器.

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