Next Article in Journal
Flood Management Issues in Hilly Regions of Uttarakhand (India) under Changing Climatic Conditions
Next Article in Special Issue
Comparison of Two Convergence Criterion in the Optimization Process Using a Recursive Method in a Multi-Reservoir System
Previous Article in Journal
Intermittent Microaeration Technology to Enhance the Carbon Source Release of Particulate Organic Matter in Domestic Sewage
Previous Article in Special Issue
An Improved Transfer Learning Model for Cyanobacterial Bloom Concentration Prediction
 
 
Article

Development of a Revised Multi-Layer Perceptron Model for Dam Inflow Prediction

1
School of Civil, Environmental and Architectural Engineering, Korea University, Seoul 02841, Korea
2
Department of Safety and Disaster Prevention Research, Seoul Institute of Technology, Seoul 03909, Korea
3
School of Civil Engineering, Chungbuk National University, Cheongju 28644, Korea
*
Authors to whom correspondence should be addressed.
Academic Editors: Jianjun Ni and Zhenxiang Xing
Water 2022, 14(12), 1878; https://doi.org/10.3390/w14121878
Received: 12 May 2022 / Revised: 2 June 2022 / Accepted: 9 June 2022 / Published: 10 June 2022
(This article belongs to the Special Issue Using Artificial Intelligence for Smart Water Management)
It is necessary to predict dam inflow in advance for flood prevention and stable dam operations. Although predictive models using deep learning are increasingly studied, these existing studies have merely applied the models or adapted the model structure. In this study, data preprocessing and machine learning algorithms were improved to increase the accuracy of the predictive model. Data preprocessing was divided into two types: The learning method, which distinguishes between peak and off seasons, and the data normalization method. To search for a global solution, the model algorithm was improved by adding a random search algorithm to the gradient descent of the Multi-Layer Perceptron (MLP) method. This revised model was applied to the Soyang Dam Basin in South Korea, and deep learning-based discharge prediction was performed using historical data from 2004 to 2021. Data preprocessing improved the accuracy by up to 61.5%, and the revised model improved the accuracy by up to 40.3%. With the improved algorithm, the accuracy of dam inflow predictions increased to 89.4%. Based on these results, stable dam operation is possible through more accurate inflow predictions. View Full-Text
Keywords: multi-layer perceptron; dam inflow prediction; data normalization; seasonal division; weights update algorithm; machine learning multi-layer perceptron; dam inflow prediction; data normalization; seasonal division; weights update algorithm; machine learning
Show Figures

Figure 1

MDPI and ACS Style

Choi, H.S.; Kim, J.H.; Lee, E.H.; Yoon, S.-K. Development of a Revised Multi-Layer Perceptron Model for Dam Inflow Prediction. Water 2022, 14, 1878. https://doi.org/10.3390/w14121878

AMA Style

Choi HS, Kim JH, Lee EH, Yoon S-K. Development of a Revised Multi-Layer Perceptron Model for Dam Inflow Prediction. Water. 2022; 14(12):1878. https://doi.org/10.3390/w14121878

Chicago/Turabian Style

Choi, Hyeon Seok, Joong Hoon Kim, Eui Hoon Lee, and Sun-Kwon Yoon. 2022. "Development of a Revised Multi-Layer Perceptron Model for Dam Inflow Prediction" Water 14, no. 12: 1878. https://doi.org/10.3390/w14121878

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop