Incorporating machine learning models and remote sensing to assess the spatial distribution of saturated hydraulic conductivity in a light-textured soil

Author:

Rezaei MeisamORCID,Mousavi Seyed Rohollah,Rahmani Asghar,Zeraatpisheh Mojtaba,Rahmati Mehdi,Pakparvar Mojtaba,Jahandideh Mahjenabadi Vahid Alah,Seuntjens Piet,Cornelis Wim

Publisher

Elsevier BV

Subject

Horticulture,Computer Science Applications,Agronomy and Crop Science,Forestry

Reference115 articles.

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3. Some practical aspects of predicting texture data in digital soil mapping;Amirian-Chakan;Soil Tillage Res.,2019

4. Evaluating machine learning approaches for the interpolation of monthly air temperature at Mt. Kilimanjaro;Appelhans;Tanzania. Spatial Statistics,2015

5. Using machine learning for prediction of saturated hydraulic conductivity and its sensitivity to soil structural perturbations;Araya;Water Resour. Res.,2019

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