@inproceedings{8b02a52060644c82b792b45a87cea3c9,
title = "River Water Level Prediction Based on Deep Learning: Case Study on the Geum River, South Korea",
abstract = "At present, deep learning models have been widely applied in many studies related to the field of water resource management. In this study, several deep learning neural network models based on the Gated Recurrent Unit (GRU) architectures have been applied to the river water level prediction for a short-time period, from one hour to nine hours ahead. The input data of these models are hourly water levels which are observed at four hydrological stations on the Geum River, South Korea. Though the model does not require data such as topography, land cover, or precipitation data, the forecasted results indicate significant stability and performance. Compared to the observed water level data, the correlation coefficient NSE (Nash-Sutcliffe efficiency) is up to more than 99\% in the case of a 1-hour forecast. The results of this study prove the potential of deep learning models in predicting water level and applicable to other river basins.",
keywords = "Deep learning, Gated recurrent unit (GRU), Geum river, Water level prediction, Water resource management",
author = "Le, \{Xuan Hien\} and Sungho Jung and Minho Yeon and Giha Lee",
note = "Publisher Copyright: {\textcopyright} 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 3rd International Conference on Sustainability in Civil Engineering, ICSCE 2020 ; Conference date: 26-11-2020 Through 27-11-2020",
year = "2021",
doi = "10.1007/978-981-16-0053-1\_40",
language = "English",
isbn = "9789811600524",
series = "Lecture Notes in Civil Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "319--325",
editor = "Thanh Bui-Tien and \{Nguyen Ngoc\}, Long and \{De Roeck\}, Guido",
booktitle = "Proceedings of the 3rd International Conference on Sustainability in Civil Engineering - ICSCE 2020",
address = "Germany",
}