Comparative study of reference evapotranspiration estimation models based on machine learning algorithm: a case study of Zhengzhou City

IF 2.7 4区 环境科学与生态学 Q2 Environmental Science Hydrology Research Pub Date : 2023-07-20 DOI:10.2166/nh.2023.040
Chaojie Niu, S. Jian, Shanshan Liu, Chengshuai Liu, Shan-e-hyder Soomro, Cai-hong Hu
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引用次数: 1

Abstract

Reference evapotranspiration (ET0) is an important parameter to characterize hydrological water cycle and energy balance. An extremely heavy rainstorm occurred in Zhengzhou City, Henan Province on 20 July 2021, causing heavy casualties and economic losses. One of the important reasons for this rainstorm was abnormal water circulation. The purpose of this study is to estimate ET0 accurately and avoid extreme disasters caused by abnormal water cycles. This study compared and analyzed the accuracy and robustness of ET0 prediction based on the improved Levenberg–Marquardt (L-M) model based on artificial neural network and the genetic algorithm-backward neural network (GA-BP) model. The model uses seven weather stations in Zhengzhou, including mountain climate and plain climate. By utilizing the Pearson correlation analysis technique, six distinct input scenarios were identified, and the efficacy of the model was assessed using evaluation metrics, including RMSE, MAE, NSE, and SI. The results show that the estimation accuracy of the L-M model is better than that of the GA-BP model; when the number of input meteorological parameters is the same, the combined simulation effect including wind speed is the best; the R2 of L-M3 and L-M4 are 0.9285 and 0.9675, respectively; Models can accurately estimate ET0 with limited data.
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基于机器学习算法的参考蒸散估算模型比较研究——以郑州市为例
参考蒸散量(ET0)是表征水文水循环和能量平衡的重要参数。2021年7月20日,河南省郑州市发生特大暴雨,造成重大人员伤亡和经济损失。这次暴雨的一个重要原因是水循环异常。本研究的目的是准确估计ET0,避免由异常水循环引起的极端灾害。本研究比较分析了基于人工神经网络的改进Levenberg–Marquardt(L-M)模型和遗传算法后向神经网络(GA-BP)模型的ET0预测的准确性和稳健性。该模型使用了郑州的七个气象站,包括山地气候和平原气候。利用Pearson相关分析技术,识别了六种不同的输入场景,并使用RMSE、MAE、NSE和SI等评估指标评估了模型的有效性。结果表明,L-M模型的估计精度优于GA-BP模型;当输入的气象参数数量相同时,包括风速在内的组合模拟效果最好;L-M3和L-M4的R2分别为0.9285和0.9675;模型可以在有限的数据下准确估计ET0。
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来源期刊
Hydrology Research
Hydrology Research Environmental Science-Water Science and Technology
CiteScore
5.30
自引率
7.40%
发文量
70
审稿时长
17 weeks
期刊介绍: Hydrology Research provides international coverage on all aspects of hydrology in its widest sense, and welcomes the submission of papers from across the subject. While emphasis is placed on studies of the hydrological cycle, the Journal also covers the physics and chemistry of water. Hydrology Research is intended to be a link between basic hydrological research and the practical application of scientific results within the broad field of water management.
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