Drought Forecasting Using Wavelet - Support Vector Machine and Standardized Precipitation Index (Case Study: Urmia Lake-Iran)

Subject Areas : Water and Environment

Mehdi Komasi ¹ , Soroush Sharghi ²

1 - Assistant Professor, Faculty of Civil Engineering, Ayatollah Boroujerdi University, Boroujerd, Iran. *(Corresponding Author)
2 - MS Student, Hydraulic Structures, Ayatollah Boroujerdi University, Boroujerd, Iran

Received: 2017-08-23 Accepted : 2017-12-27 Published : 2020-09-22

Keywords: SPI, SVM model, Drought forecasting, Urmia Lake watershed, Wavelet Transform,

Abstract :

Background and Objectives: Drought is regarded as a serious threat for people and environment. As a result, finding some indices to forecast the drought is an important issue that needs to be addressed urgently. The appropriate and flexible index for drought classification is the Standardized Precipitation Index (SPI). Artificial intelligence models were commonly used to forecast SPI time series. These models are based on auto regressive property. So, they are not able to monitor the seasonal and long-term patterns in time series. In this study, the Wavelet-Support Vector Machine (WSVM) approach was used for the drought forecasting through employing SPI. Method: In this way, the SPI time series of Urmia Lake watershed was decomposed to multiple frequent time series by wavelet transform; then, these time series were imposed as input data to the Support Vector Machine (SVM) model to forecast the drought. Findings: The results showed that, the maximum value of R2 and minimum value of RMSE indexes for SVM model are 0.865 and 0.237 and for WSVM model are 0.954 and 0.056 respectively in verification step. Discussion and Conclusion: So, the propounded hybrid model has superior ability in forecasting SPI time series comparing with the single SVM model and also it can accurately assess the extreme data in SPI time series by considering the seasonality effects. Finally, it was concluded that, the proposed hybrid model is relatively more appropriate than classical autoregressive models such as ANN.  

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