基于3种机器学习方法的农业干旱监测比较

doi:10.13866/j.azr.2022.01.31

Abstract

Abstract:

Frequent droughts have caused serious harm to the economy and agricultural production of Gansu Province. Therefore, it is very important for the province to use advanced methods to establish accurate and reliable agricultural drought monitoring models. Three machine learning methods (random forest, BP neural network, support vector machine) are used to construct an agricultural drought monitoring model using remote sensing factors and meteorological factors. Analysis of the best model for agricultural drought monitoring in Gansu Province. At the same time, the applicability of the model constructed by machine learning in different environments was further explored. The results show that among the models constructed by the three machine learning methods, the RF model has a high coefficient of determination (0.86) and small errors (RMSE 0.40, MAE 0.31). The monitoring effect of agricultural drought is better than the model constructed by BP and SVM; The three machine learning method models constructed in the two environments, respectively, performed better in the wet environment, while the model constructed by the random forest method performed better than the other two models in monitoring drought in the two environments. The research results provide a new scientific method for agricultural drought monitoring and evaluation in Gansu Province, and are of great significance to agricultural drought research.

Key words: agricultural drought, machine learning, SPEI, MODIS

WANG Xiaoyan,LI Jing,XING Liting. Comparative agricultural drought monitoring based on three machine learning methods[J].Arid Zone Research, 2022, 39(1): 322-332.

Figures/Tables 9

Tab. 1

Remote sensing drought index calculation formula"

干旱指数	公式	文献来源
VCI植被状态指数	$(NDV I i - NDV I min) / (NDV I max - NDV I min)$	[22]
TCI温度状态指数	$(LS T max - LS T i) / (LS T max - LS T min)$	[22]
PCI降水指数	$(TRM M i - TRM M min) / (TRM M max - TRM M min)$	[22]
VSWI植被供水指数	$EVI / LST$	[23]

Tab. 1

Fig. 1

Tab. 2

Fig. 2

Tab. 3

Statistics of the fitting results of the three machine learning methods on the verification data"

月份	样本	随机森林			BP神经网路			支持向量机
月份	样本	R²	RMSE	MAE	R²	RMSE	MAE	R²	RMSE	MAE
4	1	0.78	0.79	0.66	0.70	1.00	0.79	0.73	0.81	0.66
	2	0.91	0.43	0.29	0.86	0.45	0.32	0.83	0.50	0.37
	3	0.84	0.50	0.39	0.80	0.58	0.42	0.85	0.49	0.39
	4	0.86	0.56	0.42	0.82	0.53	0.39	0.85	0.49	0.39
	5	0.90	0.36	0.31	0.87	0.40	0.32	0.87	0.37	0.31
	平均值	0.86	0.53	0.41	0.81	0.59	0.45	0.82	0.53	0.42
5	1	0.84	0.56	0.40	0.80	0.68	0.53	0.82	0.58	0.40
	2	0.95	0.33	0.23	0.91	0.34	0.24	0.91	0.35	0.24
	3	0.85	0.37	0.28	0.81	0.44	0.31	0.84	0.42	0.30
	4	0.88	0.36	0.28	0.81	0.47	0.37	0.84	0.38	0.27
	5	0.82	0.37	0.31	0.77	0.41	0.33	0.78	0.41	0.31
	平均值	0.87	0.40	0.30	0.82	0.47	0.36	0.84	0.43	0.30
6	1	0.80	0.38	0.29	0.77	0.39	0.30	0.76	0.40	0.32
	2	0.91	0.24	0.18	0.87	0.29	0.22	0.91	0.25	0.19
	3	0.87	0.23	0.18	0.85	0.35	0.28	0.85	0.27	0.22
	4	0.85	0.31	0.26	0.82	0.29	0.23	0.82	0.38	0.32
	5	0.82	0.36	0.29	0.82	0.35	0.27	0.79	0.35	0.28
	平均值	0.85	0.30	0.24	0.83	0.33	0.26	0.83	0.33	0.26
7	1	0.86	0.36	0.26	0.84	0.39	0.30	0.82	0.51	0.39
	2	0.90	0.38	0.27	0.85	0.41	0.31	0.87	0.39	0.29
	3	0.81	0.42	0.32	0.78	0.43	0.34	0.81	0.41	0.35
	4	0.85	0.51	0.37	0.80	0.48	0.36	0.81	0.48	0.40
	5	0.88	0.36	0.28	0.83	0.42	0.33	0.85	0.41	0.33
	平均值	0.86	0.41	0.30	0.82	0.43	0.33	0.83	0.44	0.35
8	1	0.85	0.49	0.39	0.81	0.52	0.40	0.83	0.50	0.45
	2	0.88	0.40	0.29	0.80	0.47	0.35	0.81	0.48	0.38
	3	0.90	0.34	0.29	0.87	0.38	0.31	0.88	0.36	0.29
	4	0.87	0.40	0.36	0.85	0.45	0.36	0.83	0.50	0.41
	5	0.89	0.39	0.29	0.82	0.48	0.39	0.86	0.43	0.29
	平均值	0.88	0.40	0.32	0.83	0.46	0.36	0.84	0.45	0.36
9	1	0.85	0.44	0.33	0.82	0.48	0.35	0.80	0.53	0.41
	2	0.84	0.45	0.34	0.80	0.46	0.31	0.82	0.46	0.33
	3	0.89	0.32	0.23	0.87	0.35	0.26	0.90	0.32	0.21
	4	0.89	0.34	0.28	0.82	0.43	0.32	0.86	0.42	0.31
	5	0.83	0.44	0.32	0.81	0.46	0.36	0.80	0.50	0.38
	平均值	0.86	0.40	0.30	0.82	0.44	0.32	0.84	0.45	0.33
10	1	0.84	0.44	0.32	0.76	0.54	0.44	0.83	0.46	0.35
	2	0.90	0.39	0.28	0.83	0.46	0.34	0.86	0.42	0.32
	3	0.87	0.36	0.30	0.83	0.49	0.38	0.85	0.37	0.28
	4	0.89	0.36	0.29	0.85	0.39	0.31	0.87	0.41	0.32
	5	0.86	0.37	0.26	0.82	0.42	0.33	0.84	0.41	0.31
	平均值	0.87	0.38	0.29	0.82	0.46	0.36	0.85	0.41	0.32

Tab. 3

Tab. 4

Tab. 5

Fig. 3

Fig. 4

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月份	SPEI_1				SPEI_3				SPEI_6
月份	VCI	TCI	PCI	VSWI	VCI	TCI	PCI	VSWI	VCI	TCI	PCI	VSWI
4	0.06	0.38^**	0.54^**	0.30^*	0.09	0.23^**	0.47^**	0.34^**	0.09	0.28^**	0.56^**	0.30^**
5	0.09	0.28^**	0.61^**	0.29^*	0.07	0.24^**	0.60^**	0.26^**	0.08	0.29^**	0.53^**	0.21^**
6	0.12^**	0.35^**	0.65^**	0.34^**	0.10^**	0.34^**	0.51^**	0.30^*	0.21	0.33^**	0.53^**	0.25^*
7	0.15^**	0.34^**	0.76^**	0.40^*	0.28^**	0.25^**	0.68^**	0.29^**	0.19^**	0.32^**	0.78^**	0.32^*
8	0.16^**	0.24^**	0.78^**	0.27^**	0.17^**	0.27^**	0.65^**	0.28^**	0.20^**	0.25^**	0.62^**	0.23^**
9	-0.02	0.31^**	0.49^*	0.24^**	0.04^*	0.23^**	0.45^*	0.29^*	0.02	0.26^**	0.43^*	0.26^**
10	0.11^*	0.32^**	0.55^**	0.37^**	0.25^*	0.27^**	0.50^**	0.41^**	0.17^**	0.30^**	0.56^**	0.32^*

分类	站点	年平均降水量/mm	平均温度/℃
聚类1	酒泉、张掖、武威、民勤、靖远、永昌、景泰、皋兰、高台、敦煌、瓜州、玉门镇、山丹	155.93	7.97
聚类2	崆峒、临夏、榆中、临洮、环县、西峰、岷县、武都、麦积、华家岭、会宁、天水	483.96	8.60

Comparative agricultural drought monitoring based on three machine learning methods

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