Improving the spatial prediction of soil salinity in arid regions using wavelet transformation and support vector regression models

Geoderma ◽  
2021 ◽  
Vol 383 ◽  
pp. 114793
Author(s):  
Ruhollah Taghizadeh-Mehrjardi ◽  
Karsten Schmidt ◽  
Norair Toomanian ◽  
Brandon Heung ◽  
Thorsten Behrens ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Soil Salinity ◽  
Regression Models ◽  
Wavelet Transformation ◽  
Arid Regions ◽  
Spatial Prediction ◽  
Support Vector

Predicting and mapping of soil salinity using machine learning algorithms in central arid regions of Iran

2020 ◽  
Author(s):  
Ruhollah Taghizadeh-Mehrjardi ◽  
Norair Toomanian ◽  
Shahab Shamshirband ◽  
Amir Mosavi ◽  
Thorsten Behrens ◽  
...  
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Support Vector Regression ◽  
Soil Salinity ◽  
Wavelet Transformation ◽  
Soil Depth ◽  
Central Iran ◽  
Support Vector ◽  
Topographic Features ◽  
Wide Range

<p>In this study, we predicted and mapped soil salinity using machine learning (ML) and digital soil mapping (DSM) approaches. Support vector regression (SVR) and the hybrid of SVR with wavelet transformation (W-SVR) where applied to correlate soil salinity of the upper 200 cm of soil to a wide range of environmental covariates derived from a digital elevation model (DEM), remote sensing (RS) and climatic data. Results indicate that W-SVR performed better in predicting soil salinity at all depth intervals with scattered index ranging from 1.45 to 1.68 compared to the standalone SVR. This is particularly true at the lowest soil depth when W-SVR indicated ~1.5 times higher accuracy compared to the SVR. At this soil depth topographic features are the main covariates in the models. For topsoil salinity, land use represented by RS features controls the spatial distribution of the salinity widely. Independent from soil depth, climatic features are the most important predictors for soil salinity in all ML models. The predicted salinity maps show the highest salinity for soils in the eastern parts of central Iran. Furthermore, the importance of topographic features for all ML algorithms coincides with most landform characteristics in central Iran and confirms a close relation of soil salinity not only to land use practices like irrigation but also to soil-landscape relationships in this dry region.</p><p>Keywords: Soil salinity, machine learning, spatial variation, central Iran, support vector regression, wavelet transformation</p><p><strong>Acknowledgments</strong></p><p>Ruhollah Taghizadeh-Mehrjardi has been supported by the Alexander von Humboldt Foundation under the grant number: Ref 3.4 - 1164573 - IRN - GFHERMES-P.</p>

A class of new Support Vector Regression models

2020 ◽  
Vol 94 ◽  
pp. 106446
Author(s):  
Pritam Anand ◽  
Reshma Rastogi ◽  
Suresh Chandra
Keyword(s):  
Support Vector Regression ◽  
Regression Models ◽  
Support Vector
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