Loading...
Please wait, while we are loading the content...
Similar Documents
Development of a Revised Multi-Layer Perceptron Model for Dam Inflow Prediction
| Content Provider | MDPI |
|---|---|
| Author | Choi, Hyeon Seok Kim, Joong Hoon Lee, Eui Hoon Yoon, Sun-Kwon |
| Copyright Year | 2022 |
| Description | 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. |
| Starting Page | 1878 |
| e-ISSN | 20734441 |
| DOI | 10.3390/w14121878 |
| Journal | Water |
| Issue Number | 12 |
| Volume Number | 14 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-06-10 |
| Access Restriction | Open |
| Subject Keyword | Water Multi-layer Perceptron Dam Inflow Prediction Data Normalization Seasonal Division Weights Update Algorithm Machine Learning |
| Content Type | Text |
| Resource Type | Article |
