基于CEEMD的LSTM和ARIMA模型干旱预测适用性研究——以新疆为例

doi:10.13866/j.azr.2022.03.07

Abstract

Abstract:

The frequent occurrence of droughts seriously affects normal agricultural production and economic development. Accurate prediction of drought occurrence is of great importance in reducing drought losses. Nevertheless, drought occurrences have not been well predicted. Drought indices can be used to quantitatively evaluate the intensity, duration, and influence range of drought. Thus, on the basis of daily precipitation data from 1960 to 2019 in the Xinjiang Uyghur Autonomous Region, the standardized precipitation index (SPI) at timescales of 1, 3, 6, 9, 12, and 24 months were calculated. Aiming for the nonlinear and nonstationary characteristics of SPI, a new drought prediction method was proposed combining the single model and the complementary ensemble empirical mode decomposition (CEEMD), which can process nonlinear and nonstationary signals. In this paper, the autoregressive integrated moving average (ARIMA) model, the long short-term memory (LSTM) network, the CEEMD-ARIMA combined model, and the CEEMD-LSTM combined model were constructed to predict a multiscale SPI. The validity of prediction models was determined using root mean square error, mean absolute error, and coefficient of determination (R²). Kriging interpolation was used to demonstrate the predicted results of the four models. The results revealed that the forecast accuracy of the four models increases with the increase of SPI timescales, and the highest accuracy is obtained at SPI24. CEEMD decomposition can effectively stabilize the time series. Drought prediction based on the CEEMD provides a stable premise for the single model. At each timescale, combined models obtain higher prediction accuracy than single models, which indicates that combined models are more suitable for drought prediction. The forecast accuracy of the four models in order from the lowest to highest accuracy is the LSTM model, followed by the ARIMA, CEEMD-LSTM, and CEEMD-ARIMA models (the maximum R² values are 0.8882, 0.9103, 0.9403, and 0.9846, respectively). The CEEMD-ARIMA model shows the best ability to forecast SPI values. This study explored the applicability of four drought prediction models and provided a basis for meteorological disaster prevention and mitigation efforts.

Key words: complementary ensemble empirical mode decomposition, long short-term memory network, autoregressive integrated moving average, standardized precipitation index, drought prediction, Xinjiang

DING Yan,XU Dehe,CAO Lianhai,GUAN Xiangrong. Applicability of the LSTM and ARIMA model in drought prediction based on CEEMD: A case study of Xinjiang[J].Arid Zone Research, 2022, 39(3): 734-744.

Figures/Tables 10

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SPI范围	类型
SPI>-0.5	无旱
-1.0<SPI≤-0.5	轻旱
-1.5<SPI≤-1.0	中旱
-2.0<SPI≤-1.5	重旱
SPI≤-2.0	特旱

SPI序列	单位根检验	临界值			P值
SPI序列	单位根检验	1%	5%	10%	P值
SPI1	-8.0407	-3.4419	-2.8667	-2.5695	1.8500e-12
SPI3	-9.4801	-3.4419	-2.8666	-2.5695	3.8938e-16
SPI6	-6.7711	-3.4420	-2.8667	-2.5695	2.6407e-09
SPI9	-4.4529	-3.4423	-2.8668	-2.5696	0.0002
SPI12	-4.0259	-3.4423	-2.8668	-2.5696	0.0013
SPI24	-3.8011	-3.4425	-2.8669	-2.5696	0.0029

SPI序列	p	q	AIC	BIC
SPI1	1	0	1752.561	1766.295
SPI3	0	2	1511.110	1529.410
SPI6	4	1	1274.699	1306.696
SPI9	2	1	1044.266	1067.099
SPI12	2	1	648.904	671.716
SPI24	9	2	181.161	240.251

时间尺度	模型	R²	RMSE	MAE
1个月	LSTM	-0.0146	0.8681	0.6478
	ARIMA	-0.0058	0.8643	0.6431
	CEEMD-LSTM	0.2648	0.7389	0.5683
	CEEMD-ARIMA	0.4488	0.6398	0.4828
3个月	LSTM	0.4200	0.7906	0.6040
	ARIMA	0.4986	0.7350	0.5531
	CEEMD-LSTM	0.5782	0.6742	0.5017
	CEEMD-ARIMA	0.8246	0.4347	0.3355
6个月	LSTM	0.6686	0.6595	0.4710
	ARIMA	0.6870	0.6410	0.4554
	CEEMD-LSTM	0.7776	0.5402	0.4116
	CEEMD-ARIMA	0.9153	0.3334	0.2397
9个月	LSTM	0.7873	0.5732	0.3856
	ARIMA	0.8039	0.5503	0.3553
	CEEMD-LSTM	0.8921	0.4082	0.2839
	CEEMD-ARIMA	0.9619	0.2426	0.1789
12个月	LSTM	0.8592	0.4858	0.3084
	ARIMA	0.8732	0.4610	0.2628
	CEEMD-LSTM	0.9302	0.3420	0.2251
	CEEMD-ARIMA	0.9793	0.1863	0.1271
24个月	LSTM	0.8882	0.4266	0.2700
	ARIMA	0.9103	0.3822	0.2109
	CEEMD-LSTM	0.9403	0.3119	0.1958
	CEEMD-ARIMA	0.9846	0.1584	0.1019

Applicability of the LSTM and ARIMA model in drought prediction based on CEEMD: A case study of Xinjiang

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