Forecasting model for the change in stage of reservoir water level

Reservoir is one of major structural approaches for flood mitigation. During floods, early reservoir water release is one of the actions taken by the reservoir operator to accommodate incoming heavy rainfall. Late water release might give negative effect to the reservoir structure and cause flood at downstream area. However, current rainfall may not directly influence the change of reservoir water level. The delay may occur as the streamflow that carries the water might take some time to reach the reservoir. This study is aimed to develop a forecasting model for the change in stage of reservoir water level. The model considers the changes of reservoir water level and its stage as the input and the future change in stage of reservoir water level as the output. In this study, the Timah Tasoh reservoir operational data was obtained from the Perlis Department of Irrigation and Drainage (DID). The reservoir water level was categorised into stages based on DID manual. A modified sliding window algorithm has been deployed to segment the data into temporal patterns. Based on the patterns, three models were developed: the reservoir water level model, the change of reservoir water level and stage of reservoir water level model, and the combination of the change of reservoir water level and stage of reservoir water level model. All models were simulated using neural network and their performances were compared using on mean square error (MSE) and percentage of correctness. The result shows that the change of reservoir water level and stage of reservoir water model produces the lowest MSE and the highest percentage of correctness when compared to the other two models. The findings also show that a delay of two previous days has affected the change in stage of reservoir water level. The model can be applied to support early reservoir water release decision making. Thus, reduce the impact of flood at the downstream area

Forecasting model for the change of reservoir water level stage based on temporal pattern of reservoir water level

Reservoir water level forecasting is vital in reservoir operation and management.The output of the forecasting model can be used in reservoir decision support systems.This study demonstrates the application of Artificial Neural Network (ANN) in developing the forecasting model for the change of reservoir water level stage.In this study, sliding window technique has been used to extract the temporal pattern that represents time delays in the reservoir water level. The patterns are used as input to the ANN model.The results show that a model with 4 days of time delay has produced the acceptable performance with both low error rate and high accuracy

Neural network application in the change of reservoir water level stage forecasting

Artificial Neural Network is one of the computational algorithms that can be applied in developing a forecasting model for the change of reservoir water level stage.Forecasting of the change of reservoir water level stage is vital as the change of the reservoir water level can affect the reservoir operator’s decision.The decision of water release is very critical in both flood and drought seasons where the reservoir should maintain high volume of water during less rainfall and enough space for incoming heavy rainfall. The changes of reservoir water level which provides insights on the increase or decrease water level that affects water level stage.In this study, neural network model for forecasting the change of reservoir water level stage is studied. Six neural network models based on standard back propagation algorithm have been developed and tested.Sliding windows have been used to segment the data into various ranges. The finding shows that 2 days of delay have affected the change in stage of the reservoir water level. The finding was achieved through 4-17-1 neural network architecture

Forecasting Model for the Change of Reservoir Water Level Stage Based on Temporal

ABSTRACT. Reservoir water level forecasting is vital in reservoir operation and management. The output of the forecasting model can be used in reservoir decision support systems. This study demonstrates the application of Artificial Neural Network (ANN) in developing the forecasting model for the change of reservoir water level stage. In this study, sliding window technique has been used to extract the temporal pattern that represents time delays in the reservoir water level. The patterns are used as input to the ANN model. The results show that a model with 4 days of time delay has produced the acceptable performance with both low error rate and high accuracy