| Application of the combined ARIMA-SVR model in drought prediction based on the Standardized Precipitation Index |
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| DOI：10.7606/j.issn.1000-7601.2020.02.38 |
| Key Words: drought prediction Standardized Precipitation Index (SPI) ARIMA-SVR combined model ARIMA SVR |
| Author Name | Affiliation | | XU Dehe | College of Surveying and Geo\|informatics, North China University of Water Resources and Electric Power, Zhengzhou, Henan 450000, China | | ZHANG Qi | College of Geosciences and Engineering, North China University of Water Resources and Electric Power, Zhengzhou, Henan 450000，China | | HUANG Huiping | College of Surveying and Geo\|informatics, North China University of Water Resources and Electric Power, Zhengzhou, Henan 450000, China |
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| Abstract: |
| Carrying out drought prediction is the premise basis for effectively coping with drought risk. The multi\|scale Standardized Precipitation Index (SPI) was calculated by using the daily precipitation data of Zhengzhou meteorological station in Henan Province from 1951 to 2017, and the SPI sequence autoregressive moving average model (ARIMA) and autoregressive moving average and support vector machine regression combined model (ARIMA-SVR) were established. After the model parameters were determined and verified, the multi\|scale SPI value of Zhengzhou meteorological station in Henan Province was predicted by using the established model. The validity of the regression prediction model was determined by means of the root mean square error（RMSE） and the mean absolute percentage error (MAPE). The results showed that RMSE values of ARIMA-SVR combined model in SPI1 (1 month) and SPI12 (12 months) were 80.05 and 0.74, respectively, which were lower than 92.25 and 1.24 of ARIMA model, indicating that both the prediction accuracies of SPI of the ARIMA-SVR combined model and the single ARIMA model were related to the time scale of the index, and they gradually increased with the increase of time scale. The prediction accuracy of the two models of SPI12 was higher than that of SPI1, SPI3 (3 months), and SPI6 (6 months). Comparing the measured data with the predicted data of the model showed that the ARIMA-SVR combined model had higher prediction accuracy than the single ARIMA model, and can well fit the standardized precipitation index at different time scales. |
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