Development of Water Level Prediction Models Using Machine Learning in Wetlands: A Case Study of Upo Wetland in South Korea

Wetlands play a vital role in hydrologic and ecologic communities. Since there are few studies conducted for wetland water level prediction due to the unavailability of data, this study developed a water level prediction model using various machine learning models such as artificial neural network (...

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Bibliographic Details
Published in:Water (Basel) 2020-01, Vol.12 (1), p.93
Main Authors: Choi, Changhyun, Kim, Jungwook, Han, Heechan, Han, Daegun, Kim, Hung Soo
Format: Article
Language:English
Subjects:
Case studies
Correlation coefficient
Correlation coefficients
Dependent variables
Groundwater
Hydrology
Independent variables
Learning algorithms
Learning theory
Machine learning
Meteorological data
Neural networks
Precipitation
Prediction models
Real estate development
Rivers
Root-mean-square errors
Support vector machines
Variables
Water levels
Wetland management
Wetlands
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Summary:Wetlands play a vital role in hydrologic and ecologic communities. Since there are few studies conducted for wetland water level prediction due to the unavailability of data, this study developed a water level prediction model using various machine learning models such as artificial neural network (ANN), decision tree (DT), random forest (RF), and support vector machine (SVM). The Upo wetland, which is the largest inland wetland in South Korea, was selected as the study area. The daily water level gauge data from 2009 to 2015 were used as dependent variables, while the meteorological data and upstream water level gauge data were used as independent variables. Predictive performance evaluation using RF as the final model revealed 0.96 value for correlation coefficient (CC), 0.92 for Nash–Sutcliffe efficiency (NSE), 0.09 for root mean square error (RMSE), and 0.19 for persistence index (PI). The results indicate that the water level of the Upo wetland was well predicted, showing superior results compared to that of the ANN, which was used in a previous study. The results intend to provide basic data for development of a wetland management method, using water levels of previously ungauged areas.
ISSN:2073-4441
2073-4441
DOI:10.3390/w12010093