English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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 R 2  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.

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