Comparison of the performances of the gene expression programming model and the RegCM model in predicting monthly runoff

IF 2.7 4区环境科学与生态学 Q2 WATER RESOURCES

Journal of Water and Climate Change Pub Date : 2023-09-22 DOI:10.2166/wcc.2023.439

Sajjad Pouyanfar, Hamed Nozari, Mehraneh Khodamoradpour

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引用次数: 0

Abstract

Abstract Prediction of rainfall and runoff is one of the most important issues in managing catchment water resources and sustainable use of water resources. In this study, the accuracy and efficiency of the Gene Expression Programming (GEP) model and the Regional Climate Model (RegCM) to predict runoff values from monthly precipitation were investigated. For this purpose, monthly precipitation data of 48 synoptic stations, monthly temperature data of 21 synoptic stations, and also monthly runoff data of 40 hydrometric stations located in the Karkheh basin during 45 years (1972–2017) were used. Out of this statistical period, 40 years was used for calibration, and five years (1995–1999) for the validation of the model results. The results showed that the GEP model with an average R2 value of 0.948, average RMSE value of 19.4 m3/s, average NSE value of 0.91, and average SE value of 0.3, had a much more accurate performance than the RegCM model, which had an average R2 value of 0.04, average RMSE value of 298.2 m3/s, average NSE value of −0.64, and average SE value of 4.6 in predicting monthly runoff.

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基因表达规划模型与RegCM模型在月径流预测中的性能比较

摘要降雨径流预测是流域水资源管理和水资源可持续利用的重要问题之一。研究了基因表达编程(GEP)模型和区域气候模型(RegCM)预测月降水径流值的准确性和效率。利用45 a(1972-2017)库区48个天气站的月降水资料、21个天气站的月气温资料和40个水文站的月径流资料。在此统计期间，40年用于校准，5年(1995-1999)用于模型结果的验证。结果表明，GEP模型对月径流的预测精度显著高于RegCM模型，平均R2为0.948，平均RMSE为19.4 m3/s，平均NSE为0.91，平均SE为0.3，平均RMSE为298.2 m3/s，平均NSE为- 0.64，平均SE为4.6。

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来源期刊

Journal of Water and Climate Change WATER RESOURCES-

CiteScore

4.80

自引率

10.70%

发文量

168

审稿时长

>12 weeks

期刊介绍： Journal of Water and Climate Change publishes refereed research and practitioner papers on all aspects of water science, technology, management and innovation in response to climate change, with emphasis on reduction of energy usage.