评估和估计排水和土壤损失,使用新型机器学习技术,用于保护长椅露台
Ambrish Kumar1, Manish Kumar2, Narinder Kumar Sharma3
1College of Agricultural Engineering and Technology, Dr. Rajendra Prasad Central Agricultural University, Pusa, Bihar 848125, India; Indian Institute of Soil and Water Conservation, Dehradun, Uttarakhand 248 195, India.
The Science of the total environment
|March 20, 2025
概括
保护基板露台 (CBT) 有效地减少了喜马拉雅山脚下的下水和土壤损失,特别是在75mm以下的风暴中. 线性支向量机 (SVM-L) 模型准确预测这些影响,有助于CBT规划.
科学领域:
- 农业工程 农业工程
- 环境科学 环境科学
- 土壤保护 土壤保护
背景情况:
- 保护土地和水资源对于喜马拉雅山的农业生产力至关重要.
- 流水和土壤损失是该地区面临的主要挑战.
- 需要工程措施来缓解这些问题.
研究的目的:
- 评估保护长椅露台 (CBT) 在减少排水和土壤损失方面的有效性.
- 开发和比较机器学习模型,用于预测排水和土壤损失.
- 为了确定影响流水和土壤损失的关键气候参数.
主要方法:
- 实地实验从2007年至2015年在喜马拉雅山脉的山脚下进行.
- 保护长椅露台 (CBT) 是作为工程措施实施的.
- 多种机器学习模型,包括ANN,SVM-L,SVM-R,MLR,以及它们的波形混合版 (WANN,WSVM-L,WSVM-R),被用来利用气候参数来建模排水和土壤损失.
主要成果:
- 在实验场地,CBT显著减少了水流和土壤损失.
- 线性支向量机 (SVM-L) 模型证明了对流水和土壤损失的最佳预测准确性.
- 雨量被确定为影响下水和土壤损失的最敏感参数.
结论:
- CBT是一种非常有效的措施,可以减少地块层面的排水和土壤损失,特别是在小于75mm的风暴事件中.
- SVM-L模型提供了一个可靠的工具来估计排水和土壤损失,支持在更大的地区规划和设计CBT.
- 这些发现为政策制定者和执行机构在土壤和水资源保护工作中提供了宝贵的见解.
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