A Comparative Analysis of SMAP-Derived Soil Moisture Modeling by Optimized Machine Learning Methods: A Case Study of the Quebec Province

Environmental Sciences Proceedings

article

Mohammad Zeynoddin¹  Hossein Bonakdari² 

[1] Department of Soils and Agri-Food Engineering, Université Laval;Department of Civil Engineering, University of Ottawa

关键词: teacher learner;    optimization;    ELM;    SVM;    LSTM;    forecast;   

DOI  :  10.3390/ECWS-7-14183

来源: mdpi

【 摘 要 】

Many hydrological responses rely on the water content of the soil (WCS). Therefore, in this study, the surface WCS products of the Google Earth Engine Soil Moisture Active Passive (GEE SMAP) were modeled by a support vector machine (SVM), and extreme learning machine (ELM) models optimized by the teacher learning (TLBO) algorithm for Quebec, Canada. The results showed that the ELM model is only able to forecast 23 steps with Correlation Coefficient (R) = 0.8313, Root Mean Square Error (RMSE) = 6.1285, and Mean Absolute Error (MAE) = 5.0021. The SVM model could only estimate the future steps, one step ahead, with R = 0.8406, RMSE = 18.022, and MAE = 17.9941. Both models’ accuracy dropped significantly while forecasting longer periods.

【 授权许可】

Unknown