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缺少的每日降雨数据的归算;人工智能和统计技术之间的比较

Angkool Wangwongchai¹, Muhammad Waqas^2,3, Porntip Dechpichai¹

¹Department of Mathematics, Faculty of Science, King Mongkut's University of Technology Thonburi (KMUTT), Bangkok 10140, Thailand.

|November 29, 2023

概括

这项研究评估了统计和人工智能技术,用于归因泰国缺少的每日降雨数据. 在山区推多重线性回归 (MLR),以确保其准确性和透明度.

科学领域:

水文学的水文学
数据科学数据科学数据科学
环境建模环境建模

背景情况:

缺失的数据是水文建模中的一个重大挑战.
准确计算每日降雨量对于可靠的水资源管理至关重要,特别是在复杂的地形中.

研究的目的:

评估统计技术 (ST) 和人工智能技术 (AIT) 用于归因缺失的每日降雨数据.
为泰国北部山区推一种有效的归算方法.

主要方法:

从泰国北部的20个站点收集了30年的每日降雨数据.
根据空间相关性,删除了四个站的25-35%数据.
开发并评估了使用平均绝对误差 (MAE),根平均平方误差 (RMSE),R2和相关系数 (r) 的归算模型.
与AITs (LSTM-RNN,M5模型树,MLPNN,SVR) 进行了ST (算术平均,多重线性回归,正常比率,NIPALS,线性插值) 的比较.

主要成果:

多重线性回归 (MLR) 模型表现出强的表现,平均MAE为0.98,RMSE为4.52,R2为79.6%.
M5模型树 (M5-MT) 的表现也很好,平均MAE为0.91,RMSE为4.52,R2为79.8%.
由于其准确性,透明度和易用性的平衡,MLR被确定为推的方法.

结论:

无论是MLR还是M5-MT都是有效的,用于归因缺失的每日降雨数据.
由于其透明度和最低限度的先决条件,建议在泰国北部山区实践应用MLR.
该研究为解决水文研究中缺少数据的问题提供了有价值的框架.

关键词:

计量AIT缺少的每日降雨数据人工智能的人工智能是人工智能.深度学习是一种深度学习.计入计算是指计入计算的方法.机器学习机器学习缺少的数据数据.神经网络的神经网络的神经网络降雨量降雨量降雨量

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