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http://dx.doi.org/10.5389/KSAE.2017.59.3.097

Reservoir Water Level Forecasting Using Machine Learning Models  

Seo, Youngmin (Kyungpook National University, Department of Constructional and Environmental Engineering)
Choi, Eunhyuk (Korea Rural Community Corporation, Project Planning Department)
Yeo, Woonki (Daegu Gyeongbuk Development Institute, Future Strategic Research Lab.)
Publication Information
Journal of The Korean Society of Agricultural Engineers / v.59, no.3, 2017 , pp. 97-110 More about this Journal
Abstract
This study investigates the efficiencies of machine learning models, including artificial neural network (ANN), generalized regression neural network (GRNN), adaptive neuro-fuzzy inference system (ANFIS) and random forest (RF), for reservoir water level forecasting in the Chungju Dam, South Korea. The models' efficiencies are assessed based on model efficiency indices and graphical comparison. The forecasting results of the models are dependent on lead times and the combination of input variables. For lead time t = 1 day, ANFIS1 and ANN6 models yield superior forecasting results to RF6 and GRNN6 models. For lead time t = 5 days, ANN1 and RF6 models produce better forecasting results than ANFIS1 and GRNN3 models. For lead time t = 10 days, ANN3 and RF1 models perform better than ANFIS3 and GRNN3 models. It is found that ANN model yields the best performance for all lead times, in terms of model efficiency and graphical comparison. These results indicate that the optimal combination of input variables and forecasting models depending on lead times should be applied in reservoir water level forecasting, instead of the single combination of input variables and forecasting models for all lead times.
Keywords
Reservoir water level forecasting; Artificial neural network; Generalized regression neural network; Adaptive neuro-fuzzy inference system; Random forest;
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