Using physical method, machine learning and hybrid method to model soil water movement

IF 5.9 1区 地球科学 Q1 ENGINEERING, CIVIL
Jinjun Zhou , Tianyi Huang , Hao Wang , Wei Du , Yi Zhan , Aochuan Duan , Guangtao Fu
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Abstract

This study explores the performance of Phycically-based modelling (PBM), Machine learning (ML), and Hybrid modelling (HM) in soil water movement. Three types of models were tested on experiments under different soils and external pressure head conditions. In PBM, we proposed an adaptive step-length model named Time Cellular Automata (TCA), achieving an RMSE of 5.91, which outperforms HYDRUS (RMSE 7.92). In ML, Root Mean Square Error (RMSE) of all four tested models was below 1.5, with eXtreme Gradient Boosting (XGBoost) performing the best. The predictive accuracy of ML significantly outperformed PBM. SHapley Additive exPlanation was used to interpret the data and feature importance of machine learning. Middle-layer soil temperature, surface-layer soil salinity, water head and air temperature were identified as important parameters for ML. Heuristic algorithm can assist in searching for optimal parameters for TCA (Optimized TCA) and improve RMSE from 5.91 to 4.79. By integrating PBM and ML, developed a hybrid modeling strategy named HM. The HM was constructed using XGB and TCA, and achieved an error rate falling between Non-Optimized TCA (5.91) and Optimized TCA (5.51). This study presents a method for constructing HM from PBM and ML which is guided by data-driven approaches to make the analysis of soil water movement more efficient and economical.

Abstract Image

利用物理方法、机器学习和混合方法对土壤水分运动进行建模
本研究探讨了基于物理的建模(PBM)、机器学习(ML)和混合建模(HM)在土壤水分运动中的表现。对三种模型在不同土壤和外压头条件下进行了试验。在PBM中,我们提出了一个自适应步长模型,称为时间细胞自动机(TCA),实现了5.91的RMSE,优于HYDRUS (RMSE 7.92)。在ML中,所有四种测试模型的均方根误差(RMSE)均低于1.5,其中eXtreme Gradient Boosting (XGBoost)表现最好。ML的预测准确性明显优于PBM。使用SHapley Additive exPlanation来解释机器学习的数据和特征重要性。发现中层土壤温度、表层土壤盐度、水头和空气温度是ML的重要参数,启发式算法可以帮助寻找TCA的最优参数(Optimized TCA),将RMSE从5.91提高到4.79。通过集成PBM和ML,开发了一种名为HM的混合建模策略。使用XGB和TCA构建HM,错误率介于非优化TCA(5.91)和优化TCA(5.51)之间。本文提出了一种以数据驱动为指导,从PBM和ML中构建HM的方法,以提高土壤水分运动分析的效率和经济性。
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来源期刊
Journal of Hydrology
Journal of Hydrology 地学-地球科学综合
CiteScore
11.00
自引率
12.50%
发文量
1309
审稿时长
7.5 months
期刊介绍: The Journal of Hydrology publishes original research papers and comprehensive reviews in all the subfields of the hydrological sciences including water based management and policy issues that impact on economics and society. These comprise, but are not limited to the physical, chemical, biogeochemical, stochastic and systems aspects of surface and groundwater hydrology, hydrometeorology and hydrogeology. Relevant topics incorporating the insights and methodologies of disciplines such as climatology, water resource systems, hydraulics, agrohydrology, geomorphology, soil science, instrumentation and remote sensing, civil and environmental engineering are included. Social science perspectives on hydrological problems such as resource and ecological economics, environmental sociology, psychology and behavioural science, management and policy analysis are also invited. Multi-and interdisciplinary analyses of hydrological problems are within scope. The science published in the Journal of Hydrology is relevant to catchment scales rather than exclusively to a local scale or site.
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