Forecasting reservoir water level based on the change in rainfall pattern using neural network

• Main Authors: Raja Mohamad, Raja Nurul Mardhiah, Wan Ishak, Wan Hussain
• Format: Conference or Workshop Item
• Language: English
• Published: 2018
• Subjects: QA76 Computer software
• Online Access: http://repo.uum.edu.my/24238/1/p372.pdf; http://repo.uum.edu.my/24238/

Reservoir water level is a level of storage space for water.During heavy rainfall, waterstorage space is used to hold excessive amount of water. During less rainfall, water storage maintains the water supply for its major uses.The change in rainfall pattern may influence the water storage. Thus, understanding the change in rainfall pattern can be used in gate opening decision.On top of that, the upstream precipitation is always not coincide with the consequences and the flood downstream usually cause expensive damages and great devastation as well.This study focus on the analysis of the upstream rainfall data in order to obtain the rainfall pattern.This study deployed basic steps in Artificial Neural Network (ANN) modeling which are data selection, data preparation, data pre-processing and finally Neural Network model development and evaluation. The performance of ANN was based on MAE (mean absolute error) and RMSE (root mean square error). In this study, three datasets have been formed that represent the change in upstream rainfall pattern. The findings show that the best RMSE achieve is 0.644 from the third dataset.