{"title":"水文模型和深度学习技术在降雨径流分析中的性能比较","authors":"Chorong Kim, Chung-Soo Kim","doi":"10.1016/j.tcrr.2021.12.001","DOIUrl":null,"url":null,"abstract":"<div><p>Rainfall-runoff analysis is the most important and basic analysis in water resources management and planning. Conventional rainfall-runoff analysis methods generally have used hydrologic models. Rainfall-runoff analysis should consider complex interactions in the water cycle process, including precipitation and evapotranspiration. In this study, rainfall-runoff analysis was performed using a deep learning technique that can capture the relationship between a hydrological model used in the existing methodology and the data itself. The study was conducted in the Yeongsan River basin, which forms a large-scale agricultural area even after industrialization, as the study area. As the hydrology model, SWAT (Soil and Water Assessment Tool) was used, and for the deep learning method, a Long Short-Term Memory (LSTM) network was used among RNNs (Recurrent Neural Networks) mainly used in time series analysis. As a result of the analysis, the correlation coefficient and NSE (Nash-Sutcliffe Efficiency), which are performance indicators of the hydrological model, showed higher performance in the LSTM network. In general, the LSTM network performs better with a longer calibration period. In other words, it is worth considering that a data-based model such as an LSTM network will be more useful than a hydrological model that requires a variety of topographical and meteorological data in a watershed with sufficient historical hydrological data.</p></div>","PeriodicalId":44442,"journal":{"name":"Tropical Cyclone Research and Review","volume":"10 4","pages":"Pages 215-222"},"PeriodicalIF":2.4000,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S222560322100045X/pdfft?md5=d23bfc73ce457cb23b9328c31b5ddd4c&pid=1-s2.0-S222560322100045X-main.pdf","citationCount":"9","resultStr":"{\"title\":\"Comparison of the performance of a hydrologic model and a deep learning technique for rainfall- runoff analysis\",\"authors\":\"Chorong Kim, Chung-Soo Kim\",\"doi\":\"10.1016/j.tcrr.2021.12.001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Rainfall-runoff analysis is the most important and basic analysis in water resources management and planning. 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引用次数: 9
摘要
降雨径流分析是水资源管理和规划中最重要、最基础的分析方法。传统的降雨径流分析方法通常使用水文模型。降雨径流分析应考虑水循环过程中复杂的相互作用,包括降水和蒸散发。在本研究中,使用深度学习技术进行了降雨径流分析,该技术可以捕获现有方法中使用的水文模型与数据本身之间的关系。研究对象是在产业化后仍形成大规模农业区的荣山江流域。水文模型使用SWAT (Soil and Water Assessment Tool),深度学习方法在主要用于时间序列分析的rnn (Recurrent Neural network)中使用长短期记忆(LSTM)网络。分析结果表明,水文模型的相关系数和NSE (Nash-Sutcliffe Efficiency)在LSTM网络中表现出更高的性能。一般来说,LSTM网络的校准周期越长,性能越好。换句话说,值得考虑的是,基于数据的模型(如LSTM网络)将比需要在具有足够历史水文数据的流域中获取各种地形和气象数据的水文模型更有用。
Comparison of the performance of a hydrologic model and a deep learning technique for rainfall- runoff analysis
Rainfall-runoff analysis is the most important and basic analysis in water resources management and planning. Conventional rainfall-runoff analysis methods generally have used hydrologic models. Rainfall-runoff analysis should consider complex interactions in the water cycle process, including precipitation and evapotranspiration. In this study, rainfall-runoff analysis was performed using a deep learning technique that can capture the relationship between a hydrological model used in the existing methodology and the data itself. The study was conducted in the Yeongsan River basin, which forms a large-scale agricultural area even after industrialization, as the study area. As the hydrology model, SWAT (Soil and Water Assessment Tool) was used, and for the deep learning method, a Long Short-Term Memory (LSTM) network was used among RNNs (Recurrent Neural Networks) mainly used in time series analysis. As a result of the analysis, the correlation coefficient and NSE (Nash-Sutcliffe Efficiency), which are performance indicators of the hydrological model, showed higher performance in the LSTM network. In general, the LSTM network performs better with a longer calibration period. In other words, it is worth considering that a data-based model such as an LSTM network will be more useful than a hydrological model that requires a variety of topographical and meteorological data in a watershed with sufficient historical hydrological data.
期刊介绍:
Tropical Cyclone Research and Review is an international journal focusing on tropical cyclone monitoring, forecasting, and research as well as associated hydrological effects and disaster risk reduction. This journal is edited and published by the ESCAP/WMO Typhoon Committee (TC) and the Shanghai Typhoon Institute of the China Meteorology Administration (STI/CMA). Contributions from all tropical cyclone basins are welcome.
Scope of the journal includes:
• Reviews of tropical cyclones exhibiting unusual characteristics or behavior or resulting in disastrous impacts on Typhoon Committee Members and other regional WMO bodies
• Advances in applied and basic tropical cyclone research or technology to improve tropical cyclone forecasts and warnings
• Basic theoretical studies of tropical cyclones
• Event reports, compelling images, and topic review reports of tropical cyclones
• Impacts, risk assessments, and risk management techniques related to tropical cyclones