{"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. 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":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Tropical Cyclone Research and Review","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S222560322100045X","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
引用次数: 9
Abstract
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