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http://dx.doi.org/10.3741/JKWRA.2021.54.S-1.1195

Role of unstructured data on water surface elevation prediction with LSTM: case study on Jamsu Bridge, Korea  

Lee, Seung Yeon (Department of Civil and Environmental Engineering, Hongik University)
Yoo, Hyung Ju (Department of Civil and Environmental Engineering, Hongik University)
Lee, Seung Oh (Department of Civil and Environmental Engineering, Hongik University)
Publication Information
Journal of Korea Water Resources Association / v.54, no.spc1, 2021 , pp. 1195-1204 More about this Journal
Abstract
Recently, local torrential rain have become more frequent and severe due to abnormal climate conditions, causing a surge in human and properties damage including infrastructures along the river. In this study, water surface elevation prediction algorithm was developed using the LSTM (Long Short-term Memory) technique specialized for time series data among Machine Learning to estimate and prevent flooding of the facilities. The study area is Jamsu Bridge, the study period is 6 years (2015~2020) of June, July and August and the water surface elevation of the Jamsu Bridge after 3 hours was predicted. Input data set is composed of the water surface elevation of Jamsu Bridge (EL.m), the amount of discharge from Paldang Dam (m3/s), the tide level of Ganghwa Bridge (cm) and the number of tweets in Seoul. Complementary data were constructed by using not only structured data mainly used in precedent research but also unstructured data constructed through wordcloud, and the role of unstructured data was presented through comparison and analysis of whether or not unstructured data was used. When predicting the water surface elevation of the Jamsu Bridge, the accuracy of prediction was improved and realized that complementary data could be conservative alerts to reduce casualties. In this study, it was concluded that the use of complementary data was relatively effective in providing the user's safety and convenience of riverside infrastructure. In the future, more accurate water surface elevation prediction would be expected through the addition of types of unstructured data or detailed pre-processing of input data.
Keywords
Unstructured data; Machine learning; LSTM; Water surface elevation prediction; Wordcloud;
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