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

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KOMASI MEHDI; SHARGHI SOROUSH

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Paper Information

Journal: JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY Year:2020 | Volume:22 | Issue:7 (98) Page(s): 83-101

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Title

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

Author(s)

KOMASI MEHDI | SHARGHI SOROUSH | Issue Writer Certificate

Keywords

SVM ModelQ1

Wavelet TransformQ1

SPIQ1

Drought ForecastingQ1

Urmia Lake WatershedQ1

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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APA: Copy

KOMASI, MEHDI, & SHARGHI, SOROUSH. (2020). Drought Forecasting Using Wavelet-Support Vector Machine and Standardized Precipitation Index (Case Study: Urmia Lake-Iran). JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY, 22(7 (98) ), 83-101. SID. https://sid.ir/paper/402616/en

Vancouver: Copy

KOMASI MEHDI, SHARGHI SOROUSH. Drought Forecasting Using Wavelet-Support Vector Machine and Standardized Precipitation Index (Case Study: Urmia Lake-Iran). JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY[Internet]. 2020;22(7 (98) ):83-101. Available from: https://sid.ir/paper/402616/en

IEEE: Copy

MEHDI KOMASI, and SOROUSH SHARGHI, “Drought Forecasting Using Wavelet-Support Vector Machine and Standardized Precipitation Index (Case Study: Urmia Lake-Iran),” JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY, vol. 22, no. 7 (98) , pp. 83–101, 2020, [Online]. Available: https://sid.ir/paper/402616/en

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