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Updated: May 14, 2025

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预测水质指数使用一般化峰模型,规范化加权内核峰模型和优化的多变量变化模式分解
Marjan Kordani1, Mohsen Bagheritabar2, Iman Ahmadianfar3,4
1Department of Hydrology and Water Resources, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
Scientific reports
|May 10, 2025
概括
预测灌水质量指标,如透度指数 (PI) 和吸收率 (MAR),对于农业至关重要. 这项研究引入了一种新型混合模型 (OMVMD-GRKR),可以准确预测这些指数,优于现有方法.
科学领域:
- 环境科学 环境科学
- 水资源管理 水资源管理
- 机器学习应用 机器学习应用
背景情况:
- 灌水质量指标 (IWQI),如透率指数 (PI) 和吸收率 (MAR),对于评估农业用水的适用性至关重要.
- 准确预测IWQI参数是具有挑战性的,因为时间序列数据复杂,输入序列有限.
- 伊朗的卡鲁恩河是关键的水源,需要可靠的方法来监测其用于灌的质量.
研究的目的:
- 开发一个创新的混合情报框架,用于预测PI和MAR指数.
- 用时间序列数据来解决IWQI预测的复杂性.
- 建立一种可靠的方法来评估卡河流域的农业用水供应.
主要方法:
- 开发了一种新的混合机器学习 (ML) 模型,即用规范局部加权 (GRKR) 方法进行泛化回归和内核回归.
- 优化多变量变化模式分解 (OMVMD) 技术,通过Runge-Kutta算法 (RUN) 进行优化,用于输入变量分解.
- 使用光梯度增强机模型 (LGBM) 来选择有影响力的输入变量,并将GRKR模型与OMVMD相结合.
主要成果:
- 拟议的OMVMD-GRKR模型在Ahvaz和Molasani站预测PI和MAR指数方面表现优异.
- 统计指标显示高准确度:R=0.987,RMSE=0.761的阿瓦兹,和R=0.963,RMSE=1.379的莫拉萨尼.
- OMVMD-GRKR模型显著优于其他方法,包括OMVMD,Ridge,LSSVM,DRVFL和DELM.
结论:
- OMVMD-GRKR框架为预测灌水质量指标提供了一个高度有效和准确的方法.
- 这种新的混合模式为水资源管理和确保农业可持续性提供了有价值的工具.
- 该研究强调了先进的混合ML技术在解决复杂的环境预测挑战方面的潜力.
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