基于机器学习的青藏高原高寒沼泽湿地蒸散发插补研究

doi:10.17521/cjpe.2022.0015

摘要/Abstract

摘要：

以青藏高原典型高寒沼泽湿地为观测研究站, 以实际蒸散发为研究对象, 结合气象因子(净辐射、气温、土壤热通量、风速、相对湿度、土壤含水率), 建立基于多元线性回归(MLR)、决策树(CART)、随机森林(RF)、支持向量回归(SVR)、多层感知机(MLP) 7种组合5类算法的预测模型, 找出对于蒸散发具有较高精度的插补方法, 实现实际蒸散发数据集的构建。结果表明: 1)研究区蒸散发与净辐射相关性最大, 而土壤热通量是影响蒸散发过程的关键因子; 2) 7种组合的5类机器学习算法模型的决定系数变化范围为0.58-0.83, 均方根误差变化范围为0.038-0.089 mm·30 min^-1; 2)随机森林回归模型决定系数最高, 模型稳定性最佳, 插补效果最优; 3)插补完整的蒸散发与净辐射、土壤热通量、气温日尺度变化趋势相同, 与风速、相对湿度变化趋势相反。日蒸散发主要集中在生长季, 日最大值为8.77 mm·d^-1, 出现在7月9日, 日最小值为0.21 mm·d^-1, 出现在1月30日。

关键词: 机器学习, 高寒沼泽湿地, 蒸散发, 交叉验证

Abstract:

Aims This study aims to explore a high-precision interpolation method of evapotranspiration based on machine learning to construct high-quality data set of actual evapotranspiration.

Methods Taking the typical alpine marsh wetland on the Qingzang Plateau as the observation station to study evapotranspiration, combined with meteorological factors (net radiation, air temperature, soil heat flux, wind speed, relative humidity, soil volumetric water content), we established a prediction model to construct an actual evapotranspiration data set with a high-precision interpolation method based on combining five methods including multiple linear regression (MLR), decision tree (CART), random forest (RF), support vector regression (SVR) and multi-layer perceptron (MLP).

Important findings 1) The correlation between evapotranspiration and net radiation was the largest in the study area, and soil heat flux was the key factor affecting the evapotranspiration process. 2) The determination coefficients are from 0.58 to 0.83 among five machine learning algorithm models with seven combinations, and the root mean square error ranges from 0.038 to 0.089 mm·30 min^-1. 3) The random forest regression model has the highest determination coefficient, the best model stability and the best interpolation. 4) Interpolated evapotranspiration data had the same diurnal variation trend with net radiation, soil heat flux and ari temperature, but the opposite diurnal variation trend with wind speed and relative humidity. Daily evapotranspiration is mainly concentrated in the growing season, with the daily maximum (8.77 mm) on July 9 and the daily minimum (0.21 mm) on January 30.

Key words: machine learning, alpine marshes, evapotranspiration, cross validation

王秀英, 陈奇, 杜华礼, 张睿, 马红璐. 基于机器学习的青藏高原高寒沼泽湿地蒸散发插补研究. 植物生态学报, 2023, 47(7): 912-921. DOI: 10.17521/cjpe.2022.0015

WANG Xiu-Ying, CHEN Qi, DU Hua-Li, ZHANG Rui, MA Hong-Lu. Evapotranspiration interpolation in alpine marshes wetland on the Qingzang Plateau based on machine learning. Chinese Journal of Plant Ecology, 2023, 47(7): 912-921. DOI: 10.17521/cjpe.2022.0015

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链接本文: https://www.plant-ecology.com/CN/10.17521/cjpe.2022.0015

https://www.plant-ecology.com/CN/Y2023/V47/I7/912

图/表 11

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	月份 Month
	1	2	3	4	5	6	7	8	9	10
有效记录 Active record	1 206	1 206	1 382	1 309	1 110	1 057	1 152	1 282	1 075	936
缺失数据 Missing data	282	138	106	131	378	383	336	206	365	240
缺失率 Absence rate (%)	19	10	7	9	25	27	23	14	25	20

	月份 Month
	1	2	3	4	5	6	7	8	9	10
有效记录 Active record	1 206	1 206	1 382	1 309	1 110	1 057	1 152	1 282	1 075	936
缺失数据 Missing data	282	138	106	131	378	383	336	206	365	240
缺失率 Absence rate (%)	19	10	7	9	25	27	23	14	25	20

气象因子 Meteorological factor	月份 Month										平均 Mean
气象因子 Meteorological factor	1	2	3	4	5	6	7	8	9	10	平均 Mean
净辐射 Net radiation	0.80	0.83	0.69	0.72	0.77	0.76	0.79	0.83	0.81	0.69	0.769
土壤热通量 Soil heat flux	0.74	0.43	0.54	0.86	0.84	0.87	0.84	0.84	0.84	0.85	0.765
相对湿度 Relative humidity	-0.62	-0.68	-0.69	-0.74	-0.71	-0.75	-0.78	-0.80	-0.78	-0.72	-0.727
气温 Air temperature	0.63	0.64	0.56	0.75	0.73	0.69	0.77	0.78	0.66	0.69	0.690
风速 Wind velocity	0.74	0.74	0.71	0.61	0.59	0.43	0.39	0.29	0.46	0.63	0.559
土壤温度 Soil temperature (5 cm)	0.29	0.07	0.35	0.60	0.52	0.53	0.55	0.46	0.41	0.48	0.426
土壤温度 Soil temperature (10 cm)	-0.12	0.12	0.13	0.04	-0.07	-0.01	0	0.21	0.13	0.15	0.058
土壤温度 Soil temperature (20 cm)	-0.37	0	0.11	-0.21	-0.22	-0.13	-0.34	-0.20	0	0.03	-0.133
土壤温度 Soil temperature (30 cm)	-0.32	0.10	0.12	0.13	-0.08	-0.03	-0.10	-0.04	0.09	0.12	-0.001
土壤温度 Soil temperature (40 cm)	0	-0.06	0.14	0.10	0.08	0.10	0.15	0.08	0.12	0.18	0.089
土壤含水率 Soil moisture content (5 cm)	0.26	0.08	0.11	-0.08	0.30	0.22	0.04	0.36	0.40	0.33	0.202
土壤含水率 Soil moisture content (10 cm)	-0.24	0	0.04	0.07	0.04	-0.08	-0.23	-0.03	-0.10	0.11	-0.042
土壤含水率 Soil moisture content (20 cm)	-0.24	0.10	0.07	0.12	0.05	-0.01	-0.14	-0.08	-0.11	0.10	-0.014
土壤含水率 Soil moisture content (30 cm)	-0.02	-0.10	0.09	0.15	-0.02	0.03	-0.15	-0.02	-0.09	0.11	-0.002
土壤含水率 Soil moisture content (40 cm)	-0.03	0.04	0.10	0.15	-0.02	0	0	0	-0.05	0.15	0.034

气象因子 Meteorological factor	月份 Month										平均 Mean
气象因子 Meteorological factor	1	2	3	4	5	6	7	8	9	10	平均 Mean
净辐射 Net radiation	0.80	0.83	0.69	0.72	0.77	0.76	0.79	0.83	0.81	0.69	0.769
土壤热通量 Soil heat flux	0.74	0.43	0.54	0.86	0.84	0.87	0.84	0.84	0.84	0.85	0.765
相对湿度 Relative humidity	-0.62	-0.68	-0.69	-0.74	-0.71	-0.75	-0.78	-0.80	-0.78	-0.72	-0.727
气温 Air temperature	0.63	0.64	0.56	0.75	0.73	0.69	0.77	0.78	0.66	0.69	0.690
风速 Wind velocity	0.74	0.74	0.71	0.61	0.59	0.43	0.39	0.29	0.46	0.63	0.559
土壤温度 Soil temperature (5 cm)	0.29	0.07	0.35	0.60	0.52	0.53	0.55	0.46	0.41	0.48	0.426
土壤温度 Soil temperature (10 cm)	-0.12	0.12	0.13	0.04	-0.07	-0.01	0	0.21	0.13	0.15	0.058
土壤温度 Soil temperature (20 cm)	-0.37	0	0.11	-0.21	-0.22	-0.13	-0.34	-0.20	0	0.03	-0.133
土壤温度 Soil temperature (30 cm)	-0.32	0.10	0.12	0.13	-0.08	-0.03	-0.10	-0.04	0.09	0.12	-0.001
土壤温度 Soil temperature (40 cm)	0	-0.06	0.14	0.10	0.08	0.10	0.15	0.08	0.12	0.18	0.089
土壤含水率 Soil moisture content (5 cm)	0.26	0.08	0.11	-0.08	0.30	0.22	0.04	0.36	0.40	0.33	0.202
土壤含水率 Soil moisture content (10 cm)	-0.24	0	0.04	0.07	0.04	-0.08	-0.23	-0.03	-0.10	0.11	-0.042
土壤含水率 Soil moisture content (20 cm)	-0.24	0.10	0.07	0.12	0.05	-0.01	-0.14	-0.08	-0.11	0.10	-0.014
土壤含水率 Soil moisture content (30 cm)	-0.02	-0.10	0.09	0.15	-0.02	0.03	-0.15	-0.02	-0.09	0.11	-0.002
土壤含水率 Soil moisture content (40 cm)	-0.03	0.04	0.10	0.15	-0.02	0	0	0	-0.05	0.15	0.034

模型特征组合 Model feature combination	输入特征 Input characteristic	特征个数 Feature number
组合1 Combination 1	Rn、Sg、RH、T、WS	5
组合2 Combination 2	Rn、Sg、RH、T、WS、ST_5 cm	6
组合3 Combination 3	Rn、Sg、RH、T、WS、SV_5 cm	6
组合4 Combination 4	Rn、Sg、RH、T、WS、ST_5 cm、SV_5 cm	7
组合5 Combination 5	Rn、Sg、RH、T、WS、ST_5 cm、ST_10 cm、ST_20 cm、ST_30 cm、ST_40 cm	10
组合6 Combination 6	Rn、Sg、RH、T、WS、SV_5 cm、SV_10 cm、SV_20 cm、SV_30 cm、SV_40 cm	10
组合7 Combination 7	Rn、Sg、RH、T、WS、ST_5 cm-ST_40 cm、SV_5 cm-SV_40 cm	15