Meisam Samadi; Abdolreza Bahremand; Abolhasan Fathabadi
Abstract
In any water resource management plan, there is a pivotal need to undertake the future conditions to allocate the water resources to different sectors (e.g. drinking-water supply, agriculture sector, etc.) more efficiently. Meanwhile, it is important to forecast water resources inflow for future months. ...
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In any water resource management plan, there is a pivotal need to undertake the future conditions to allocate the water resources to different sectors (e.g. drinking-water supply, agriculture sector, etc.) more efficiently. Meanwhile, it is important to forecast water resources inflow for future months. To this aim, it is of prime interest to adopt models that are capable of coping with data scarcity problem and able to forecast the stream flow with the least possible error. The current study was aimed at forecasting the monthly inflow of the Boustan Dam by employing three models namely: time series method, Artificial Neural Network (ANN), Support Vector Machine (SVM), and their ensembles. The hydrometric data was obtained from the Tamar Station. Afterward, the models were compared by using several evaluation criteria. According to the Akaike and Schwarz criteria, the ARIMA (2, 0, 0) (1, 0, 1) was found to be the best time series model with a parsimonious behavior. Moreover, the ANN model with two and four input neurons and the SVM model with three input neurons were the best performing models compared to their other counterparts with different input numbers. Considering the evaluation criteria altogether, the time series method was the best performing model with the RMSE, AARE, MBE, and CE values of 0.88, 4.71, -0.024, and 0.36, respectively. Therefore, the time series method was introduced as the premier model for monthly inflow forecasting in the studied stations.
