Prediction of Soil Salinity/Sodicity and Salt-Affected Soil Classes from Soluble Salt Ions Using Machine Learning Algorithms

• Published in: Soil systems Vol. 7; no. 2; p. 47
• Main Authors: Andrade Foronda, Demis, Colinet, Gilles
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2023
• Subjects: Agricultural land; Algorithms; Analysis; Bolivia; calcium; Calcium ions; Classification; Data mining; Discriminant analysis; Electrical conductivity; Electrical resistivity; exchangeable sodium; exchangeable sodium percentage; Ions; Land degradation; Learning algorithms; least squares; Machine learning; Magnesium; Management; model validation; Performance evaluation; prediction; Random variables; Salinity; Salinity effects; Salt; salt-affected soil classification; Salts; sodicity; Sodium; Soil acidity; Soil improvement; Soil salinity; Soil sciences; Soils; Soils, Salts in; soluble salts; Support vector machines; Water; Bolivia; Netherlands

Salt-affected soils are related to salinity (high content of soluble salts) and/or sodicity (excess of sodium), which are major leading causes of agricultural land degradation. This study aimed to evaluate the performances of three machine learning (ML) algorithms in predicting the soil exchangeable...