基于随机森林的电容式土壤水分传感器校准研究

汪正; 胡顺; 马瑞; 孙自永; 葛孟琰; 王俊友; 乔树锋

doi:10.19509/j.cnki.dzkq.2022.0133

基于随机森林的电容式土壤水分传感器校准研究

doi: 10.19509/j.cnki.dzkq.2022.0133

中国地质大学(武汉)环境学院, 武汉 430078

基金项目:

国家重点研发项目 2017YFC0406105

详细信息

作者简介:
汪正(1996-), 男, 现正攻读地下水科学与工程硕士学位, 主要从事生态水文学研究工作。E-mail: wangzheng@cug.edu.cn

通讯作者:
胡顺(1992-), 男, 副教授, 主要从事生态水文学教学与科研工作。E-mail: hushun@cug.edu.cn

中图分类号: TU411.91
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- 文章访问数: 305
- PDF下载量: 41
- 被引次数: 0
出版历程
- 收稿日期: 2022-01-11
- 录用日期: 2022-04-29
- 修回日期: 2022-04-18

Calibration of capacitive soil moisture sensor based on random forest

School of Environmental Studies, China University of Geosciences(Wuhan), Wuhan 430078, China

摘要

摘要:
土壤含水量信息对于自然生态修复、农田灌溉管理、土体工程建设等具有重要的意义, 电容式土壤水分传感器是获取该信息的主要途径之一。为准确校准电容式土壤水分传感器(以5TE设备为例)的土壤含水量观测数据, 开展了不同温度、含盐量、土壤含水量条件下的土壤介电常数、电导率和温度观测实验, 构建了基于随机森林机器学习方法的土壤含水量估计模型。结果表明: ①变温、变含盐量情况下, 土壤介电常数受含盐量和温度影响显著, 仅基于土壤介电常数的传统土壤含水量估计模型失效; ②以电容式土壤水分传感器观测的土壤介电常数、电导率和温度数据为输入, 基于随机森林机器学习方法的土壤含水量估计模型可有效改善土壤含水量估计结果(RMSE为0.05 m³/m³, R²为0.77;修正Topp公式的土壤含水量估计结果: RMSE为0.07 m³/m³, R²为0.54);③土壤电导率观测对土壤含水量估计最为重要, 介电常数次之, 温度最弱, 但均未达到可忽略不计的程度。研究成果可为电容式土壤水分传感器在变温、变含盐量地区的成功应用提供支撑。
- 土壤含水量 /
- 介电常数 /
- 电导率 /
- 温度 /
- 随机森林 /
- 土壤水分传感器
Abstract: Objective
Soil moisture information crucial for various applications, such as natural ecological restoration, farmland irrigation management, and soil engineering construction. One of the main sensors used to obtain this information is the capacitive soil moisture sensor.
Methods
To accurately calibrate the soil water content observation data of the 5TE capacitive soil water sensor, soil dielectric permittivity, electric conductivity and temperature observation experiments were carried out under different temperature, salt content and soil water content conditions. A soil water content estimation model based on the random forest machine learning method was established.
Results
The results showed that: ① The soil dielectric permittivity was significantly affected by varying salinity and temperature with constant soil water content. The traditional soil water content estimation model based only on soil dielectric permittivity became invalid, ② The soil water content estimation model based on the random forest method could effectively improve the soil water content estimation with the soil dielectric permittivity, electric conductivity and temperature data as input. Random forest method obtained soil moisture estimation with RMSE=0.05 m³/m³ and R²=0.77, while RMSE=0.07 m³/m³ and R²=0.54 were obtained by the modified Topp equation, and ③ The soil electric conductivity was the most important factor for soil water content estimation, followed by the dielectric permittivity and temperature. Nevertheless, the importance of the dielectric permittivity and temperature did not reach a negligible level.
Conclusion
This study provides a way to support the successful application of capacitive soil moisture sensors in areas with variable temperature and salinity.
- soil water content /
- dielectric permittivity /
- electric conductivity /
- temperature /
- random forest /
- soil moisture sensor
注释:

(所有作者声明不存在利益冲突)

HTML全文

图 1 土壤介电常数、电导率与温度的关系图

Figure 1. Relationship between soil dielectric permittivity, electric conductivity and temperature

下载: 全尺寸图片幻灯片

图 2 基于Topp公式的土壤含水量估计效果(θ_m、θ_p分别为实测值与估计值)

a. 出厂Topp公式；b. 修正Topp公式

Figure 2. Estimation performance of soil water content based on the Topp equation

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图 3 不同参数值组合下的500次重复抽样验证集评价指标均值及最优参数组合

Figure 3. Mean evaluation indices of 500 times repeated sampling for validation set under different parameter value combinations and the optimal parameter combination

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图 4 mtree=650、ntry=3时500次重复土壤含水量估计评价指标频数分布

μ. 均值；σ. 标准差；p>0.05表示服从正态分布

Figure 4. Frequency distribution of evaluation indices for estimated soil water content by 500 repetitions when mtree=500 and ntry=3

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图 5 单次随机抽样训练集(a, b)与验证集(c, d)的土壤含水量估计效果

Figure 5. Estimation performance of soil water content in the training set (a, b) and verification set (c, d) with single random sampling

下载: 全尺寸图片幻灯片

图 6 输入变量重要性(B_p越大表示变量越重要)

Figure 6. Importance of input variables (the larger B_p is, the more important the variable is)

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表 1 实验土壤基本物理特征

Table 1. Basic physical characteristics of the experimental soil

土壤类型	干容重/ (g·cm^-3)	含盐量/ (g·kg^-1)	砂粒	粉粒	黏粒
土壤类型	干容重/ (g·cm^-3)	含盐量/ (g·kg^-1)	w_B/%
砂质壤土	1.259	25.57	61.31	34.50	4.19
砂土	1.601	2.29	99.07	0.93	0

下载: 导出CSV

表 2 有效观测数据量及其属性范围

Table 2. Amount of valid observation data and their attribute ranges

	数据量/组	范围
	数据量/组	E_b	E_c/(dS·m^-1)	T_s/℃	θ/(m³·m^-3)	SSC/(g·kg^-1)
砂质壤土	754	2.87~79.56	0~13.21	6.5~40.1	0.007~0.468	25.57~34.30
砂土	871	2.88~79.89	0~9.2	7.0~40.2	0.005~0.333	2.29~26.52
注：E_b为介电常数；E_c为电导率；T_s为温度；θ为体积含水量；SSC为土壤全盐质量分数

下载: 导出CSV

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