Open AccessImproving model using estimate error for daily inflow forecasting
Author(s) -
Yaowalak Ngamsanroaj,
Kreangsak Tamee
Publication year - 2020
Publication title -
ecti transactions on computer and information technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.132
H-Index - 2
ISSN - 2286-9131
DOI - 10.37936/ecti-cit.2019132.198508
Subject(s) - inflow , mean squared error , approximation error , artificial neural network , statistics , environmental science , computer science , mathematics , meteorology , machine learning , physics
Inflow forecasting is one of the important components for reservoir operation and resource management. To obtain enhanced accuracy for forecasting reservoir inflow, this paper proposed an improved model for forecasting the inflow of Bhumibol reservoir. The 3,169 records of daily inflow data from June 1, 2008, to February 1, 2017, had been collected to calculate the inflow into the reservoir by using Artificial Neural Networks (ANN) Back-Propagation Learning Algorithm for forecasting the inflow of the reservoir in the main model and error prediction model. The performance of the model can be evaluated by four methods: the coefficient of determination (R2), the Nash-Sutcliffe efficiency (NSE), the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE). Thus, these proposed main and error prediction models were combined to support the forecast of reservoir inflow. The performance of the proposed model can be evaluated by following measured values: R2 was 0.927, NSE was 0.925, RMSE was 6.805 and MAE was 3.611. This indicates that the improved model provides more accurate value.