Prediction of Meteorological Droughts in Kuhrang Using the Hybrid Model of Wavelet and Artificial Neural Network

Document Type : Research Paper

Authors

1 MSc. Student of Water Resources Engineering, Shahrekord University, Shahrekord, Iran

2 Associate Professor, Department of Water Engineering, Shahrekord University Address: Water Eng. department, Faculty of Agriculture, Shahrekord University, Shahrekord, Iran.

3 3- Assistant Professor, Department of Water Engineering, Shahrekord University Shahrekord, Iran

4 PhD. of Water Resources Engineering, Department of Water Engineering, Shahid Chamran University od Ahvaz, Ahvaz, Iran

Abstract

Meteorological drought is defined as a lack of rainfall over long periods, which reduces soil moisture and river flow. One of the critical drought assessment tools is drought indices (Tsakiris & Vangelis, 2005). So far, many drought indicators have been developed by researchers, for example, the RDI )Reconnaissance Drought Index) (Tsakiris & Vangelis, 2005). The difference between this index and other drought indices is that it is estimated based on two variables of precipitation and potential evapotranspiration. For this reason, it is more accurate than indices that are calculated only based on precipitation. So far, some studies have been used the RDI for drought assessment. Zarei et al. (2016) studied the spatial pattern of drought using the RDI index in southern Iran. The results showed that the area with dry conditions had an increasing trend. Asadi Zarch (2017) investigated the drought trend in Yazd province between 1966-2009 using the RDI index. The results showed that drought occurrence in Yazd increased during the studied period. Because, unlike other natural disasters, it is difficult to accurately determine the onset and the end of the drought period (Moried et al., 2005). Accordingly, it is difficult to diagnose and evaluate the drought phenomenon. Therefore, monitoring and predicting drought is very important in water resources management. The use of wavelets is a new and very effective way of analyzing signals and time series. Application of Wavelet in Wavelet- Artificial Neural Network (WANN) models as a function for training has recently been used as an alternative for Artificial Neural Network (ANN) models. In recent years, the combination of wavelet theory and artificial neural networks has led to the development of wavelet neural networks (Thuillard, 2000). Zhang et al. (2017) applied the ARIMA, ANN, WANN, and Support Vector Regression (SVR) models to predict droughts in China's northern Haihe River basin using the SPI index. The results showed that the WANN model performed better than other considered models for predicting the SPI values at 6 and 12 months time scales. This study aimed to predict the meteorological droughts in the Kuhrang region using ANN and WANN models. To this end, the efficiency of ANN and WANN models in predicting precipitation and potential evapotranspiration will be evaluated. Then, the Resilience Drought Index (RDI) will be calculated based on the predicted values by ANN and WANN to describe and prediction of Kuhrang wetness conditions.

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References

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Irrigation Sciences and Engineering
Volume 44, Issue 3
November 2021
Pages 61-74

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History

  • Receive Date: 15 October 2019
  • Revise Date: 14 June 2020
  • Accept Date: 20 June 2020
  • Publish Date: 23 September 2021

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