Random Forests with Bagging and Genetic Algorithms Coupled with Least Trimmed Squares Regression for Soil Moisture Deficit Using SMOS Satellite Soil Moisture

Random Forests with Bagging and Genetic Algorithms Coupled with Least Trimmed Squares Regression for Soil Moisture Deficit Using SMOS Satellite Soil Moisture

Soil Moisture Deficit (SMD) is a key indicator of soil water content changes and is valuable to a variety of applications, such as weather and climate, natural disasters, agricultural water management, etc. Soil Moisture and Ocean Salinity (SMOS) is a dedicated mission focused on soil moisture retri...

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Bibliographic Details
Main Authors: Prashant K. Srivastava, George P. Petropoulos, Rajendra Prasad, Dimitris Triantakonstantis
Format: Article
Language:English
Published: MDPI AG 2021-07-01
Series:ISPRS International Journal of Geo-Information
Subjects:
SMOS
soil moisture deficit
rainfall-runoff model
random forest
Genetic Algorithm
Online Access:https://www.mdpi.com/2220-9964/10/8/507

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