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http://dx.doi.org/10.3741/JKWRA.2021.54.12.1285

Development of artificial intelligence-based river flood level prediction model capable of independent self-warning  

Kim, Sooyoung (Department of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology)
Kim, Hyung-Jun (Department of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology)
Yoon, Kwang Seok (Department of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology)
Publication Information
Journal of Korea Water Resources Association / v.54, no.12, 2021 , pp. 1285-1294 More about this Journal
Abstract
In recent years, as rainfall is concentrated and rainfall intensity increases worldwide due to climate change, the scale of flood damage is increasing. Rainfall of a previously unobserved magnitude falls, and the rainy season lasts for a long time on record. In particular, these damages are concentrated in ASEAN countries, and at least 20 million people among ASEAN countries are affected by frequent flooding due to recent sea level rise, typhoons and torrential rain. Korea supports the domestic flood warning system to ASEAN countries through various ODA projects, but the communication network is unstable, so there is a limit to the central control method alone. Therefore, in this study, an artificial intelligence-based flood prediction model was developed to develop an observation station that can observe water level and rainfall, and even predict and warn floods at once at one observation station. Training, validation and testing were carried out for 0.5, 1, 2, 3, and 6 hours of lead time using the rainfall and water level observation data in 10-minute units from 2009 to 2020 at Junjukbi-bridge station of Seolma stream. LSTM was applied to artificial intelligence algorithm. As a result of the study, it showed excellent results in model fit and error for all lead time. In the case of a short arrival time due to a small watershed and a large watershed slope such as Seolma stream, a lead time of 1 hour will show very good prediction results. In addition, it is expected that a longer lead time is possible depending on the size and slope of the watershed.
Keywords
Artificial intelligence; Flood forecasting and warning; LSTM; Self-warning;
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