Chapter
| Title | Watershed Level Forecasting Using Support Vector Regression Machine Posted by Lemuel Clark Velasco |
| Authors | Velasco, Lemuel Clark; Estose, Alyssa Jenn; Opon, Melcris; Tabanao, Emily |
| Publication date | 2024/3/21 |
| Chapter of the book | Lecture Notes in Networks and Systems |
| Volume | 919 |
| Pages | 449-466 |
| Publisher | Springer Nature Switzerland |
| Abstract | Accurate water level forecasting in a watershed as an early warning system is of great importance especially in flood prone areas. This could help the disaster management agencies in alerting the people with enough time as well as have a real time control of hydraulic structures to mitigate flood effects. This paper presents a strategy for days-ahead water level forecasting of a watershed that utilize Support Vector Regression Machine (SVRM). Data preparation was conducted in order to solve anomalies in terms of the 11.52% missing data and 0.023% time inconsistences attributed primarily from the Water Level Monitoring Stations (WLMS) that serves as the sensor that captures the watershed data. Min-Max scaling method was then used in data transformation so that the dataset can be implemented by a SVRM model that uses the t-12, t-24, t-72, t-168 Architecture with a Radial Basis Function (RBF) kernel having C = 115, ε = 0.01 and γ = 0.001 as SVRM parameters. Through proper data preparation and SVRM implementation, the results which compared the actual and the forecasted water level shows a Mean Absolute Percentage Error (MAPE) of 2.186 and Root Mean Square Error (RMSE) of 0.00601. This study clearly suggests that SVRM has the potential to be a viable days-ahead water level forecasting model for a watershed. |
| Index terms / Keywords | support vector machine; support vector regression machine; water level forecasting; days-ahead water level forecasting, watershed, river basin |
| DOI | https://doi.org/10.1007/978-3-031-53960-2_29 |
| URL | https://doi.org/10.1007/978-3-031-53960-2_29 |