基于XGBoost和数值天气预报的黄淮海平原参考作物蒸散量预测模型研究

doi:10.11924/j.issn.1000-6850.casb2023-0793

中国农学通报 ›› 2024, Vol. 40 ›› Issue (28): 126-133.doi: 10.11924/j.issn.1000-6850.casb2023-0793

• 资源·环境·生态·土壤·气象 • 上一篇下一篇

基于XGBoost和数值天气预报的黄淮海平原参考作物蒸散量预测模型研究

朱春霞¹(), 秦安振²()

¹ 周口市土壤肥料和农业资源保护中心，河南周口 466000
² 中国农业科学院农田灌溉研究所，河南新乡 453002

收稿日期:2023-11-08 修回日期:2024-07-23 出版日期:2024-10-05 发布日期:2024-09-29
通讯作者:
秦安振，男，1984年出生，河南新乡人，副研究员，博士，研究方向为智慧灌溉预报技术。通信地址：453002 河南省新乡市牧野区宏力大道东380号农田灌溉研究所，Tel：0373-3393321，E-mail：qinanzhen@caas.cn。
作者简介:
朱春霞，女，1971年出生，河南周口人，高级农艺师，本科，研究方向为水肥一体化技术。通信地址：466000 河南省周口市川汇区交通路中段60号，Tel：0394-8106517，E-mail：zkszcx2266@163.com。
基金资助:
河南省重点研发与推广专项（科技攻关）(222102110175); 新乡市科技攻关计划项目(GG2021024)

Forecasting of Reference Crop Evapotranspiration in Huang-Huai-Hai Plain Based on XGBoost Machine Learning Model and Numerical Weather Prediction Data

ZHU Chunxia¹(), QIN Anzhen²()

¹ Center for Soil and Fertilizer and Agricultural Resources Protection of Zhoukou City, Zhoukou, Henan 466000
² Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang, Henan 453002

Received:2023-11-08 Revised:2024-07-23 Published:2024-10-05 Online:2024-09-29

摘要/Abstract

摘要：

为提高黄淮海平原参考作物蒸散量(ET₀)的预测精度，在豫北地区新乡市利用2020—2021年历史气象数据和2022年日数值天气预报数据（最高气温、最低气温、太阳总辐射量、日照时数、相对湿度和2 m风速），建立反向传播(BP)、极限梯度提升(XGBoost)和梯度提升决策树(CatBoost)3种预测ET₀的机器学习模型，并与FAO-56 Penman-Monteith模型的结果进行比较。结果显示，气象参数中太阳总辐射量(R_a)、最高气温(T_max)和最低气温(T_min)与ET₀的相关性最高，可作为模型的输入因子。从预测时间尺度来看，3种机器学习模型对1~16 d的ET₀预报效果最佳。其中，XGBoost模型在验证期的R=0.875、RMSE=0.230 mm/d、MAE=0.181 mm/d、MAPE=8.45%。R较CatBoost和BP模型平均提高10.2%，RMSE、MAE和MAPE平均下降39.9%~62.4%。鉴于XGBoost模型预测ET₀的精度和稳定性，推荐将其作为黄淮海平原参考作物蒸散量的预测方法。

关键词: P-M模型, BP神经网络模型, 参考作物蒸散量, 机器学习, 预测模型, 黄淮海平原, 极限梯度提升(XGBoost), 梯度提升决策树(CatBoost)

Abstract:

This study seeks to enhance the prediction accuracy of reference crop evapotranspiration (ET₀) in the Huang-Huai-Hai Plain. Three models, namely BP, XGBoost, and CatBoost, were developed utilizing historical daily meteorological data from 2020-2021. These data included maximum air temperature (T_max), minimum air temperature (T_min), total solar radiation (R_a), sunshine hours (S), relative humidity (RH), and wind speed at a height of 2 m (U₂) for training purposes. The models were tested and forecasted using daily numerical weather prediction data from Xinxiang city, located in northern Henan Province, for the year 2022. The forecasted results were compared with the ET₀ data calculated using FAO-56 Penman-Monteith model. The results showed that R_a, T_max, and T_min were most correlated with ET₀ among all factors, and therefore were considered as input factors for model running. The three models generated acceptable ET₀ accuracy at 1-16 d forecast scale. In model testing, XGBoost model had R=0.875, RMSE=0.230 mm/d, MAE=0.181 mm/d, and MAPE=8.45%, respectively. On average, the R value of XGBoost model was increased by 10.2%, and values of RMSE, MAE, and MAPE were decreased by 39.9%-62.4%, compared to BP and CatBoost models. In view of the accuracy and stability of the XGBoost model, it can be a recommended model for ET₀ forecasting in the Huang-Huai-Hai Plain.

Key words: P-M model, BP neural network model, reference crop evapotranspiration, machine learning, forecasting models, Huang-Huai-Hai Plain, XGBoost, CATBoost

朱春霞, 秦安振. 基于XGBoost和数值天气预报的黄淮海平原参考作物蒸散量预测模型研究[J]. 中国农学通报, 2024, 40(28): 126-133.

ZHU Chunxia, QIN Anzhen. Forecasting of Reference Crop Evapotranspiration in Huang-Huai-Hai Plain Based on XGBoost Machine Learning Model and Numerical Weather Prediction Data[J]. Chinese Agricultural Science Bulletin, 2024, 40(28): 126-133.

图/表 6

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	T_max	T_min	T_mean	R_a	S	RH	U₂
T_min	0.912^**	1.000
T_mean	0.943^**	0.867^**	1.000
R_a	0.865^**	0.835^**	0.876^**	1.000
S	0.635^*	0.685^*	0.638^*	0.785^*	1.000
RH	0.762^*	0.635^*	0.586	0.672^*	0.324	1.000
U₂	0.124	0.213	0.215	0.314	0.121	0.168	1.000
ET₀	0.884^**	0.715^*	0.735^*	0.727^*	0.477	0.458	0.125

	T_max	T_min	T_mean	R_a	S	RH	U₂
T_min	0.912^**	1.000
T_mean	0.943^**	0.867^**	1.000
R_a	0.865^**	0.835^**	0.876^**	1.000
S	0.635^*	0.685^*	0.638^*	0.785^*	1.000
RH	0.762^*	0.635^*	0.586	0.672^*	0.324	1.000
U₂	0.124	0.213	0.215	0.314	0.121	0.168	1.000
ET₀	0.884^**	0.715^*	0.735^*	0.727^*	0.477	0.458	0.125

模型	训练阶段				验证阶段
模型	R	RMSE/(mm/d)	MAE/(mm/d)	MAPE/%	R	RMSE/(mm/d)	MAE/(mm/d)	MAPE/%
BP	0.817b	0.496a	0.864a	23.5a	0.757b	0.485a	0.452a	33.6a
CatBoost	0.857b	0.335b	0.268b	15.2b	0.831a	0.281b	0.226b	11.4b
XGBoost	0.923a	0.183c	0.122c	7.32c	0.875a	0.230b	0.181c	8.45b

模型	训练阶段				验证阶段
模型	R	RMSE/(mm/d)	MAE/(mm/d)	MAPE/%	R	RMSE/(mm/d)	MAE/(mm/d)	MAPE/%
BP	0.817b	0.496a	0.864a	23.5a	0.757b	0.485a	0.452a	33.6a
CatBoost	0.857b	0.335b	0.268b	15.2b	0.831a	0.281b	0.226b	11.4b
XGBoost	0.923a	0.183c	0.122c	7.32c	0.875a	0.230b	0.181c	8.45b

模型	预报天数/d	R	RMSE/(mm/d)	MAE/(mm/d)	MAPE/%
BP	1~8	0.65b	0.185c	0.240b	21.6b
	1~16	0.54c	0.230b	0.281b	27.6ab
	1~32	0.33e	0.315a	0.311a	30.7a
	1~56	0.31e	0.326a	0.325a	32.6a
CatBoost	1~8	0.62b	0.190c	0.183c	9.63c
	1~16	0.55c	0.176c	0.153cd	12.6c
	1~32	0.30e	0.275b	0.221b	22.6b
	1~56	0.28e	0.281b	0.226b	24.8b
XGBoost	1~8	0.72a	0.118c	0.114d	4.65d
	1~16	0.74a	0.126c	0.113d	10.7c
	1~32	0.48d	0.226b	0.193c	16.7b
	1~56	0.43d	0.230b	0.181c	15.6b

基于XGBoost和数值天气预报的黄淮海平原参考作物蒸散量预测模型研究

Forecasting of Reference Crop Evapotranspiration in Huang-Huai-Hai Plain Based on XGBoost Machine Learning Model and Numerical Weather Prediction Data

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日期(年-月-日)	ET₀真实值	ET₀预测值
日期(年-月-日)	ET₀真实值	BP模型	CatBoost模型	XGBoost模型
2022-3-1	1.88	1.75	1.81	1.76
2022-3-2	1.85	1.72	1.65	1.77
2022-3-3	1.89	1.62	1.69	1.77
2022-3-4	1.86	1.64	1.61	1.78
2022-3-5	1.94	2.01	1.76	1.76
2022-3-6	1.79	1.85	1.78	1.77
2022-3-7	1.87	1.96	1.75	1.74
2022-3-8	1.82	2.01	1.81	1.78
2022-3-9	1.96	2.16	1.70	1.75
2022-3-10	1.74	1.68	1.95	1.90
2022-3-11	1.98	1.63	2.01	1.95
2022-3-12	2.01	1.62	2.15	1.98
2022-3-13	2.14	1.68	2.23	2.03
2022-3-14	1.86	1.98	2.10	2.16
2022-3-15	2.16	1.85	2.32	2.14
2022-3-16	2.23	1.69	2.14	2.19

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