Drought Prediction Based on Artificial Neural Network and Support Vector Machine
Drought has aggravated in the humid areas of South China due to climate warming.Drought prediction is of great significance for the optimal management of water resources and the alleviation of drought.Based on the standardized precipitation evapotranspiration index (SPEI) of different time scales fo...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Pearl River
2021-01-01
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| Series: | Renmin Zhujiang |
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| Online Access: | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2021.04.001 |
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| _version_ | 1841535654836568064 |
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| author | ZHAO Guoyang TU Xinjun WANG Tian XIE Yuting MO Xiaomei |
| author_facet | ZHAO Guoyang TU Xinjun WANG Tian XIE Yuting MO Xiaomei |
| author_sort | ZHAO Guoyang |
| collection | DOAJ |
| description | Drought has aggravated in the humid areas of South China due to climate warming.Drought prediction is of great significance for the optimal management of water resources and the alleviation of drought.Based on the standardized precipitation evapotranspiration index (SPEI) of different time scales for drought evaluation,this paper constructs the artificial neural network (ANN) and support vector regression (SVR) models to predict droughts in the prediction periods of 1 to 3 months,and builds the EMD-ANN and EMD-SVR coupling models to increase the prediction precision for the SPEI1 with the scale of 1 month.The results showed that:The ANN and SVR models have good prediction precision for SPEI with the scales of 3 months.In addition,the prediction precision of the SVR model is slightly better than that of ANN model.The shorter the prediction period is,the higher the prediction precision is.The coefficient of determination of the ANN and SVR models for the drought prediction period of 1 month accounts for 0.834~0.911.The ANN and SVR models are not suitable for the prediction of the SPEI1 with scale of 1 month.After processing by EMD and wavelet denoising,the prediction precision of the SPEI1 by the EMD-ANN and EMD-SVR models is significantly increased. |
| format | Article |
| id | doaj-art-c9c83c825c284256b82a3496fd8e37ed |
| institution | Kabale University |
| issn | 1001-9235 |
| language | zho |
| publishDate | 2021-01-01 |
| publisher | Editorial Office of Pearl River |
| record_format | Article |
| series | Renmin Zhujiang |
| spelling | doaj-art-c9c83c825c284256b82a3496fd8e37ed2025-01-15T02:29:48ZzhoEditorial Office of Pearl RiverRenmin Zhujiang1001-92352021-01-014247648882Drought Prediction Based on Artificial Neural Network and Support Vector MachineZHAO GuoyangTU XinjunWANG TianXIE YutingMO XiaomeiDrought has aggravated in the humid areas of South China due to climate warming.Drought prediction is of great significance for the optimal management of water resources and the alleviation of drought.Based on the standardized precipitation evapotranspiration index (SPEI) of different time scales for drought evaluation,this paper constructs the artificial neural network (ANN) and support vector regression (SVR) models to predict droughts in the prediction periods of 1 to 3 months,and builds the EMD-ANN and EMD-SVR coupling models to increase the prediction precision for the SPEI1 with the scale of 1 month.The results showed that:The ANN and SVR models have good prediction precision for SPEI with the scales of 3 months.In addition,the prediction precision of the SVR model is slightly better than that of ANN model.The shorter the prediction period is,the higher the prediction precision is.The coefficient of determination of the ANN and SVR models for the drought prediction period of 1 month accounts for 0.834~0.911.The ANN and SVR models are not suitable for the prediction of the SPEI1 with scale of 1 month.After processing by EMD and wavelet denoising,the prediction precision of the SPEI1 by the EMD-ANN and EMD-SVR models is significantly increased.http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2021.04.001drought predictionANNSVREMDwavelet denoisingDongjiang River Basin |
| spellingShingle | ZHAO Guoyang TU Xinjun WANG Tian XIE Yuting MO Xiaomei Drought Prediction Based on Artificial Neural Network and Support Vector Machine Renmin Zhujiang drought prediction ANN SVR EMD wavelet denoising Dongjiang River Basin |
| title | Drought Prediction Based on Artificial Neural Network and Support Vector Machine |
| title_full | Drought Prediction Based on Artificial Neural Network and Support Vector Machine |
| title_fullStr | Drought Prediction Based on Artificial Neural Network and Support Vector Machine |
| title_full_unstemmed | Drought Prediction Based on Artificial Neural Network and Support Vector Machine |
| title_short | Drought Prediction Based on Artificial Neural Network and Support Vector Machine |
| title_sort | drought prediction based on artificial neural network and support vector machine |
| topic | drought prediction ANN SVR EMD wavelet denoising Dongjiang River Basin |
| url | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2021.04.001 |
| work_keys_str_mv | AT zhaoguoyang droughtpredictionbasedonartificialneuralnetworkandsupportvectormachine AT tuxinjun droughtpredictionbasedonartificialneuralnetworkandsupportvectormachine AT wangtian droughtpredictionbasedonartificialneuralnetworkandsupportvectormachine AT xieyuting droughtpredictionbasedonartificialneuralnetworkandsupportvectormachine AT moxiaomei droughtpredictionbasedonartificialneuralnetworkandsupportvectormachine |