| Cite this article: | 王冠智,粟晓玲,张特,等.基于DWT-WFGM(1,1)-ARMA组合模型的农业用水量预测[J].灌溉排水学报,0,():-. |
| Wang Guanzhi,Su Xiaoling,Zhang Te,et al.基于DWT-WFGM(1,1)-ARMA组合模型的农业用水量预测[J].灌溉排水学报,0,():-. |
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| Combined Model for Prediction of Agricultural Water Consumption Based on DWT-WFGM(1,1)-ARMA |
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Wang Guanzhi1,2, Su Xiaoling1,2, Zhang Te1,2, Jiang Tianliang1,2, Chu Jiangdong1,2
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1.College of Water Resources and Architectural Engineering, Northwest A&2.F University, Yangling
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| Abstract: |
| 【Objective】Agricultural water consumption prediction is important for regional water resources planning and management. To solve the oscillation of agricultural water consumption and the single result of traditional models, this paper proposed a novel combined forecasting model named DWT-WFGM(1,1)-ARMA which divided the original series into approximate and detailed series by using the Discrete Wavelet Transform.【Method】The Auto regressive Moving Average Model and the Fractional Order Grey Model were applied to predict the detailed series and the approximate series, respectively. Then, the Weighted Markov Chain was adopted for the approximate series error correction, and the predicted results of different component series were added to obtain the predicted results and interval of agricultural water consumption.【Result】Moreover, the proposed model was applied to predict agricultural water consumption in Shaanxi Province and Inner Mongolia Province, and was compared with the traditional grey models including GM(1,1), DWT-GM(1,1)-ARMA and DWT-FGM(1,1)-ARMA. The results showed the mean absolute percentage error of the DWT-FGM(1,1)-ARMA model in Shaanxi Province and Inner Mongolia Province were 1.25% and 1.01% respectively, indicating that the proposed model performed better than other models. And, the proposed model was practical that include compute a reasonable range of fluctuations of agricultural water consumption.【Conclusion】In summary, the model constructed in this paper can effectively improve the accuracy of agricultural water consumption prediction, while the proposed prediction interval can provide a more reliable basis for regional agricultural water consumption prediction. |
| Key words: agricultural water consumption; fractional order grey model; weighted Markov chains; discrete wavelet transform; predicted intervals |
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