Development of a Wavelet Hybrid Models for Estimating Regional Droughts in Siminehroud Basin

Subject Areas : hydrology

Erfan  Rostam Zade ¹ (Department of Civil Engineering - Islamic Azad University - Urmia - Iran)
alireza  parvishi ² (Department of Civil Engineering - Islamic Azad University - Urmia Branch- Iran)

Received: 2022-02-20 Accepted : 2022-05-17 Published : 2022-07-23

Keywords: Artificial Neural Network (ANN), Support Vector Machin (SVM), Wavelet theory (W), Standardized Precipitation Index(SPI), Siminehroud Basin,

Abstract :

In the present study, the drought of Siminehroud basin was investigated by intelligent Support Vector Machine (SVM) models, Artificial Neural Network (ANN) and Wavelet theory (W). Data from six rain gauge stations in the region were used and drought index was calculated in four time scales. The first-order autocorrelation was also selected as the optimal delay. Then the appropriate structure of the Artificial Neural Network was determined using Trial and Error Method and the three coefficients of the SVM model were determined and modeled. The results of evaluating individual models showed that there is no significant difference between two methods in predicting droughts. Then WANN and WSVM hybrid models were prepared. The results showed that the application of Wavelet theory greatly improved the performance of individual models and the amount of RMSE and MAE indices decreased by 19% and 21% and the correlation coefficient increased by 30%, respectively.