标题: Mechanism-learning coupling paradigms for parameter inversion and simulation in earth surface systems
作者: Shen, HF (Shen, Huanfeng); Zhang, LP (Zhang, Liangpei)
来源出版物: SCIENCE CHINA-EARTH SCIENCES DOI: 10.1007/s11430-022-9999-9 提前访问日期: JAN 2023
摘要: Building the physics-driven mechanism model has always been the core scientific paradigm for parameter estimation in Earth surface systems, and developing the data-driven machine learning model is a crucial way for paradigm transformation in geoscience research. The coupling of mechanism and learning models can realize the combination of "rationalism" and "empiricism", which is one of the most concerned research hotspots. In this paper, for remote sensing inversion and dynamic simulation, we deeply analyze the internal bottleneck and complementarity of mechanism and learning models and build a coupling paradigm framework with mechanism-learning cascading model, learning-embedded mechanism model, and mechanism-infused learning model. We systematically summarize ten specific coupling methods, including preprocessing and initialization, intermediate variable transfer, post-refinement processing, model substitution, model adjustment, model solution, input variable constraints, objective function constraints, model structure constraints, hybrid, etc., and analyze the main existing problems and future challenges. The research aims to provide a new perspective for in-depth understanding and application of the mechanism-learning coupling model and provide theoretical and technical support for improving the inversion and simulation capabilities of parameters in Earth surface systems and serving the development of Earth system science.
作者关键词: Mechanism model; Machine learning; Model coupling; Remote sensing inversion; Numerical simulation
地址: [Shen, Huanfeng] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.
[Shen, Huanfeng] Wuhan Univ, Key Lab Geog Informat Syst, Minist Educ, Wuhan 430079, Peoples R China.
[Zhang, Liangpei] Wuhan Univ, State Key Lab Informat Engn Survey Mapping & Remot, Wuhan 430079, Peoples R China.
通讯作者地址: Shen, HF (通讯作者),Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.
Shen, HF (通讯作者),Wuhan Univ, Key Lab Geog Informat Syst, Minist Educ, Wuhan 430079, Peoples R China.
Zhang, LP (通讯作者),Wuhan Univ, State Key Lab Informat Engn Survey Mapping & Remot, Wuhan 430079, Peoples R China.
电子邮件地址: shenhf@whu.edu.cn; zlp62@whu.edu.cn
影响因子:5.492
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