基于EMD-GWO-LSTM模型的新疆标准化降水蒸散指数预测方法研究

doi:10.13866/j.azr.2024.04.01

Abstract

Abstract:

Drought prediction has always been a major challenge in the field of drought research. Improving the accuracy of drought prediction is the key to solving the drought problem. The standardized precipitation evapotranspiration index (SPEI) was calculated on the basis of the monthly precipitation and average temperature data from 34 meteorological stations in Xinjiang from 1961 to 2019. Dry and wet changes in the Xinjiang region were analyzed. An empirical mode decomposition (EMD)-Gray Wolf Optimizer (GWO)-long short-term memory network is proposed. A combination prediction model based on the data decomposition of LSTM was used to forecast the drought, and the performance of the model was evaluated. The results were as follows: (1) the drought periodicity was stable and the periodicity was long. (2) EMD can effectively optimize the stationarity of data, GWO can optimize the parameters of the prediction model, and the prediction accuracy of the combination model is significantly higher than that of the single prediction model. (3) The accuracy of the results of the four prediction models in descending order was as follows: EMD-GWO-LSTM, GWO-LSTM, GWO-support vector regression (SVR), and LSTM (goodness of fit: 0.972, 0.939, 0.862, 0.830, respectively). The prediction accuracy of the EMD-GWO-LSTM combination prediction model was higher than that of the other three prediction models. The EMD-GWO-LSTM combination prediction model can effectively improve the accuracy of meteorological drought prediction and provide a novel approach for meteorological drought forecasting and drought mitigation in Xinjiang.

Key words: EMD-GWO-LSTM model, standardized precipitation evapotranspiration index, drought prediction, Xinjiang

XU Chaojie, DOU Yan, MENG Qilin. Prediction of the standardized precipitation evapotranspiration index in the Xinjiang region using the EMD-GWO-LSTM model[J].Arid Zone Research, 2024, 41(4): 527-539.

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时间尺度	Sen’s斜率
月	-4.4752×10^-4
春季	-0.0139
夏季	-0.0103
秋季	-0.0120
冬季	0.0010
年	-0.0169

时间尺度	主周期	旱涝交替尺度/a	周期个数/个	时间尺度	主周期	旱涝交替尺度/a	周期个数/个
年	第一主周期	16	2.5	秋	第一主周期	16	2.5
	第二主周期	3	13		第二主周期	9	4.5
	第三主周期	5	7.5		第三主周期	5	8.5
	第四主周期	7	5.5		第四主周期	2	22
	第五主周期	4	10		第五主周期	3	13
春	第一主周期	6.5	6	冬	第一主周期	16	2.5
	第二主周期	13	3		第二主周期	6	6.5
	第三主周期	3	13		第三主周期	4	10.5
	第四主周期	8	5		第四主周期	3	15
夏	第一主周期	20	2		第五主周期	9	4.5
	第二主周期	3	13
	第三主周期	8	5

名称	IMF1	IMF2	IMF3	IMF4	IMF5	IMF6	Res
学习率	0.0039	0.0148	0.0479	0.0816	0.0592	0.0454	0.0607
隐藏层节点	53	31	55	10	12	28	18
L2正则化系数	0.0815	0.0311	0.0045	0.0830	0.0012	0.0142	0.0312
网络结构	12-53-1	12-31-1	12-55-1	12-10-1	12-12-1	12-28-1	12-18-1

	RMSE	MAE	SMAPE
IMF1	0.089	0.069	1.310
IMF2	0.028	0.022	0.450
IMF3	0.032	0.023	0.258
IMF4	0.038	0.026	0.267
IMF5	0.006	0.005	0.075
IMF6	0.004	0.002	0.033
Res	0.040	0.017	0.036

模型	参数
LSTM	双隐藏层，网络结构：12-32-16-1，迭代次数500，学习率0.001
GWO-SVR	核函数为RBF，正则化系数c=11.690089，核函数的系数γ=0.010547
GWO-LSTM	单隐藏层，网络结构：12-100-1，学习率0.0174，迭代次数500，L2正则化系数0.001

Prediction of the standardized precipitation evapotranspiration index in the Xinjiang region using the EMD-GWO-LSTM model

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模型	RMSE	MAE	SMAPE	R²
LSTM	0.287	0.201	0.398	0.830
GWO-SVR	0.247	0.171	0.505	0.862
GWO-LSTM	0.165	0.114	0.384	0.939
EMD-GWO-LSTM	0.112	0.087	0.315	0.972

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