Predicting soil water content using support vector machines improved by meta-heuristic algorithms and remotely sensed data

ABSTRACT

Soil water content characteristics included field capacity (FC) and permanent wilting point (PWP) are key soil indicators to study permeability, soil water retention capacity, drainage, irrigation, plant water stress and solute movement. It is necessary to use an appropriate model for predicting these characteristics because of the hardness of their measurement. The present research shows a comparison of intelligent models included ANN, ANFIS, SVM and SVM optimised by firefly and particle swarm meta-heuristic algorithms (SVM-FFA and SVM-PSA) to predict soil water content at −33 and −1500 kPa matric potentials in agricultural soils. Results showed that the SVM-PSA and SVM-FFA hybrid models presented the best performance rather than the others, with predictor variables included geometric mean diameter (dg) of soil particles, bulk density, organic carbon, NDVI and NSMI and the values of R² and NRMSE in validation dataset, 0.94, 0.081, 0.96 and 0.077 for FC and PWP in SVM-PSA, respectively, and 0.96, 0.079, 0.97 and 0.075 for FC and PWP in SVM-FFA model. Therefore, these intelligent machines are preferable for accurate prediction and could be used to predict soil water content of soils with different soil textures in other regions of Iran or even the world.

KEYWORDS:

• Field capacity
• firefly
• machine learning algorithms
• particle swarm
• permanent wilting point
• remotely sensed data

Acknowledgments

This project was funded by a grant from the Soil and Water Research Institute of Iran. The authors wish to express their sincere thanks to the Soil and Water Research Institute of Iran for supporting field studies and samplings. The authors would like to thank the anonymous reviewers for their valuable comments and suggestions that improved the quality of the paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The funding for this research was provided by the Soil and Water Research Institute of Iran. This paper has been extracted using data of national research plan with approved code 014-10-10-9452.