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http://dx.doi.org/10.17663/JWR.2020.22.2.106

Development of Water Level Prediction Models Using Deep Neural Network in Mountain Wetlands  

Kim, Donghyun (Department of Civil Engineering, Inha University)
Kim, Jungwook (Water Quality Assessment Research Division, Water Environment Research Department, National Institute of Environment Research)
Kwak, Jaewon (Nakdong River Flood Control Office)
Necesito, Imee V. (Department of Civil Engineering, Inha University)
Kim, Jongsung (Department of Civil Engineering, Inha University)
Kim, Hung Soo (Department of Civil Engineering, Inha University)
Publication Information
Journal of Wetlands Research / v.22, no.2, 2020 , pp. 106-112 More about this Journal
Abstract
Wetlands play an important function and role in hydrological, environmental, and ecological, aspects of the watershed. Water level in wetlands is essential for various analysis such as for the determination of wetland function and its effects on the environment. Since several wetlands are ungauged, research on wetland water level prediction are uncommon. Therefore, this study developed a water level prediction model using multiple regression analysis, principal component regression analysis, artificial neural network, and DNN to predict wetland water level. Geumjeong-Mountain Wetland located in Yangsan-city, Gyeongsangnam-do province was selected as the target area, and the water level measurement data from April 2017 to July 2018 was used as the dependent variable. On the other hand, hydrological and meteorological data were used as independent variables in the study. As a result of evaluating the predictive power, the water level prediction model using DNN was selected as the final model as it showed an RMSE value of 6.359 and an NRMSE value of 18.91%. This research study is believed to be useful especially as a basic data for the development of wetland maintenance and management techniques using the water level of the existing unmeasured points.
Keywords
Deep Learning; Mountain Wetland; Principal Component Analysis; Artificial Neural Network;
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Times Cited By KSCI : 2  (Citation Analysis)
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