{"title":"基于人工智能的降雨水位预报技术分析","authors":"Chorong Kim, Chung-Soo Kim","doi":"10.1016/j.tcrr.2021.12.002","DOIUrl":null,"url":null,"abstract":"<div><p>Water level forecasting according to rainfall is important for water resource management and disaster prevention. Existing hydrological analysis is accompanied by difficulties in water level forecasting analysis such as topographic data and model parameter optimization of the area. Recently, with the improvement of AI (Artificial Intelligence) technology, a research using AI technology in the water resource field is being conducted.</p><p>In this research, water level forecasting was performed using an AI-based technique that can capture the relationship between data. As the watershed for the study, the Seolmacheon catchment which has the rich historical hydrological data, was selected. SVM (Support Vector Machine) and a gradient boosting technique were used for AI machine learning. For AI deep learning, water level forecasting was performed using a Long Short-Term Memory (LSTM) network among Recurrent Neural Networks (RNNs) used for time series analysis.</p><p>The correlation coefficient and NSE (Nash-Sutcliffe Efficiency), which are mainly used forhydrological analysis, were used as performance indicators. As a result of the analysis, all three techniques performed excellently in water level forecasting. Among them, the LSTM network showed higher performance as the correction period using historical data increased.</p><p>When there is a concern about an emergency disaster such as torrential rainfall in Korea, water level forecasting requires quick judgment. It is thought that the above requirements will be satisfied when an AI-based technique that can forecast water level using historical hydrology data is applied.</p></div>","PeriodicalId":44442,"journal":{"name":"Tropical Cyclone Research and Review","volume":"10 4","pages":"Pages 223-228"},"PeriodicalIF":2.4000,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2225603221000461/pdfft?md5=0d0081342740112a8759bcca377f54fc&pid=1-s2.0-S2225603221000461-main.pdf","citationCount":"4","resultStr":"{\"title\":\"Analysis of AI-based techniques for forecasting water level according to rainfall\",\"authors\":\"Chorong Kim, Chung-Soo Kim\",\"doi\":\"10.1016/j.tcrr.2021.12.002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Water level forecasting according to rainfall is important for water resource management and disaster prevention. Existing hydrological analysis is accompanied by difficulties in water level forecasting analysis such as topographic data and model parameter optimization of the area. Recently, with the improvement of AI (Artificial Intelligence) technology, a research using AI technology in the water resource field is being conducted.</p><p>In this research, water level forecasting was performed using an AI-based technique that can capture the relationship between data. As the watershed for the study, the Seolmacheon catchment which has the rich historical hydrological data, was selected. SVM (Support Vector Machine) and a gradient boosting technique were used for AI machine learning. For AI deep learning, water level forecasting was performed using a Long Short-Term Memory (LSTM) network among Recurrent Neural Networks (RNNs) used for time series analysis.</p><p>The correlation coefficient and NSE (Nash-Sutcliffe Efficiency), which are mainly used forhydrological analysis, were used as performance indicators. As a result of the analysis, all three techniques performed excellently in water level forecasting. Among them, the LSTM network showed higher performance as the correction period using historical data increased.</p><p>When there is a concern about an emergency disaster such as torrential rainfall in Korea, water level forecasting requires quick judgment. It is thought that the above requirements will be satisfied when an AI-based technique that can forecast water level using historical hydrology data is applied.</p></div>\",\"PeriodicalId\":44442,\"journal\":{\"name\":\"Tropical Cyclone Research and Review\",\"volume\":\"10 4\",\"pages\":\"Pages 223-228\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2021-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2225603221000461/pdfft?md5=0d0081342740112a8759bcca377f54fc&pid=1-s2.0-S2225603221000461-main.pdf\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Tropical Cyclone Research and Review\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2225603221000461\",\"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}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Tropical Cyclone Research and Review","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2225603221000461","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}
Analysis of AI-based techniques for forecasting water level according to rainfall
Water level forecasting according to rainfall is important for water resource management and disaster prevention. Existing hydrological analysis is accompanied by difficulties in water level forecasting analysis such as topographic data and model parameter optimization of the area. Recently, with the improvement of AI (Artificial Intelligence) technology, a research using AI technology in the water resource field is being conducted.
In this research, water level forecasting was performed using an AI-based technique that can capture the relationship between data. As the watershed for the study, the Seolmacheon catchment which has the rich historical hydrological data, was selected. SVM (Support Vector Machine) and a gradient boosting technique were used for AI machine learning. For AI deep learning, water level forecasting was performed using a Long Short-Term Memory (LSTM) network among Recurrent Neural Networks (RNNs) used for time series analysis.
The correlation coefficient and NSE (Nash-Sutcliffe Efficiency), which are mainly used forhydrological analysis, were used as performance indicators. As a result of the analysis, all three techniques performed excellently in water level forecasting. Among them, the LSTM network showed higher performance as the correction period using historical data increased.
When there is a concern about an emergency disaster such as torrential rainfall in Korea, water level forecasting requires quick judgment. It is thought that the above requirements will be satisfied when an AI-based technique that can forecast water level using historical hydrology data is applied.
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