极限梯度提升和长短期记忆网络相融合的土壤温度预测

李清亮; 蔡凯旋; 耿庆田; 刘光洁; 孙明玉; 张嵛; 于繁华

doi:10.37188/OPE.20202810.2337

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极限梯度提升和长短期记忆网络相融合的土壤温度预测

信息科学 | 更新时间：2020-11-16

- 极限梯度提升和长短期记忆网络相融合的土壤温度预测
- Estimation of soil temperature based on XGBoost and LSTM methods
- 光学精密工程 2020年28卷第10期页码：2337-2348
- 作者机构：
  
  长春师范大学计算机科学技术学院, 吉林长春 130032
- 作者简介：
  
  [ "李清亮(1988-), 男, 博士, 副教授, 硕士生导师, 中国电子学会计算机视觉专业技术人员, 2016年于吉林大学获得博士学位, 主要从事大气物理AI预报, 机器学习和图像处理等方面的研究。E-mail:liqingliang@mail.ccsfu.edu.cn" ]
  [ "耿庆田(1972-), 男, 博士, 教授, 硕士生导师, 2016年于吉林大学获得博士学位, 主要从事机器学习和汽车电子等方面的研究。E-mail:qtgeng@mail.ccsfu.edu.cn" ]
  [ "于繁华(1970-), 男, 博士, 教授, 硕士生导师, 吉林省高校新世纪人才, 长春市第五批有突出贡献专家, 2008年于吉林大学交通学院获得博士学位, 主要从事大气物理AI预报, 机器学习和智能优化等方面的研究。E-mail:yufanhua@163.com" ]
- 基金信息：
  
  国家自然科学基金资助项目(61604019);吉林省省级科技创新项目资助(20190302026GX);吉林省发改委产业技术研究与开发项目资助(2020C019-3);吉林省发改委产业技术研究与开发项目资助(2019C054-8);吉林省科技发展计划资助项目(20180201086SF);吉林省高等教育学会高教科研项目资助(JGJX2018D10);辽宁省科技厅联合开放基金机器人学国家重点实验室开放基金资助项目(2020-KF-22-08);长春师范大学青年教师培育计划项目(长师大自科合字[2019]第006号)
- DOI：10.37188/OPE.20202810.2337
  中图分类号： P401;P343.8
- 收稿日期：2020-03-16，
  
  修回日期：2020-05-24，
  
  录用日期：2020-5-24，
  
  纸质出版日期：2020-10-25
- 稿件说明：
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李清亮, 蔡凯旋, 耿庆田, 等. 极限梯度提升和长短期记忆网络相融合的土壤温度预测[J]. 光学精密工程, 2020,28(10):2337-2348.

Qing-liang LI, Kai-xuan CAI, Qing-tian GENG, et al. Estimation of soil temperature based on XGBoost and LSTM methods[J]. Optics and precision engineering, 2020, 28(10): 2337-2348.
李清亮, 蔡凯旋, 耿庆田, 等. 极限梯度提升和长短期记忆网络相融合的土壤温度预测[J]. 光学精密工程, 2020,28(10):2337-2348. DOI： 10.37188/OPE.20202810.2337.

Qing-liang LI, Kai-xuan CAI, Qing-tian GENG, et al. Estimation of soil temperature based on XGBoost and LSTM methods[J]. Optics and precision engineering, 2020, 28(10): 2337-2348. DOI： 10.37188/OPE.20202810.2337.

摘要

土壤温度是地球科学多个领域的重要变量。其时空变化受多种环境因素影响，这对土壤温度的准确预测带来巨大挑战。以机器学习为核心的数据驱动方法，在土壤温度预测中是重要研究领域，为基于物理过程模型提供重要补充。然而目前针对土壤温度影响因素量性研究较少，因此本文提出XGBoost-LSTM的数据驱动方法。基于极限梯度提升算法（XGBoost）分析土壤温度影响因素的重要性，然后根据影响因素重要性依次组合，并输入至长短期记忆网络（LSTM），得到最优预测模型并实现土壤温度预测。最后在长白山和海北两个气象站完成实验，本文方法的最优均方根误差为2.234、平均绝对误差为1.716、纳什效率系数为0.932、LMI系数为0.729和威尔莫特一致性指数为0.983。结果表明本文提出的XGBoost-LSTM预测模型与目前土壤温度中常用的数据驱动模型相比，均表现出更高的精确度。

Abstract

Soil temperature is an important variable in Earth sciences. The temporal and spatial variations in soil temperature are affected by numerous factors

resulting in various challenges in soil temperature prediction. For soil temperature prediction

the data-driven machine learning method is valuable and can be an important complement to physics-based process models. However

no extensive studies have been carried out on the importance of environmental factors on soil temperature. In this study

a data-driven XGBoost-LSTM method is proposed. The weights of the meteorological inputs are computed based on XGBoost

and then

the combination of meteorological inputs based on their weights is applied to obtain an optimal model by the LSTM method. An experiment is carried out at two stations in China (Changbai Mountain and Haibei). The most accurate performance for soil temperature estimation is attained

with highest values of NS = 0.932

WI = 0.983

and LMI = 0.729 and lowest values of RMSE and MAE of 2.234 and 1.716

respectively. These results show that the proposed model is generally superior to other state-of-the-art predictive models.

关键词

Keywords

references

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