Modelling and Prediction of Soil Classes Using Boosting Regression Tree and Random Forests Machine Learning Algorithms in Some Part of Qazvin Plain

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MOUSAVI SEYED ROOHOLLAH; SARMADIAN FERAIDON; RAHMANI ASGHAR

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Paper Information

Journal: Iranian Journal of Soil and Water Research Year:2020 | Volume:50 | Issue:10 Page(s): 2525-2538

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Information Journal Paper

Title

Modelling and Prediction of Soil Classes Using Boosting Regression Tree and Random Forests Machine Learning Algorithms in Some Part of Qazvin Plain

Author(s)

MOUSAVI SEYED ROOHOLLAH | SARMADIAN FERAIDON | RAHMANI ASGHAR | Issue Writer Certificate

Keywords

Digital Soil MappingQ1

Learning AlgorithmQ1

Random Forests ModelQ1

Boosting Regression TreeQ1

Data miningQ2

Abstract

Appropriate selection of ancillary covariates have a specific important on Digital Soil Mapping. Currently, use of machine Learning Algorithms for digital mapping and updating of conventional soil map has been developed in Iran. The current study has been done to compare the BRT and RF models for spatial prediction of subgroup and family classes with selection of axillary variables using VIF approach in some part of Qazvin Plain. 61 pedons were sampled based on stratified random, digged, described and classified with consideration of laboratory analysis up to family level. The most appropriate variables were selected among 15 Geomorphometry and Remote Sensing Indices using Variance Inflation Factor (VIF). Soil landscape modeling was conducted with RF and BRT Learning Algorithm in RStudio software based on Randomforest and C5. 0 packages at subgroup and family levels. The results showed that six indices including CHA, DEM, STH, SI DVI and NDVI were selected as input variables. Assessment indices such as the Overall Accuracy (OA) and Kappa were obtained for BRT (35, 26%) and RF (70, 60%) at family level, respectively. Sensitivity analysis based on the mean decrease accuracy (MDA) revealed that the modified catchment area variable is the most relative important variable among the selected variables. Generally, by using feature selection innovative approach and effective Learning Algorithms, the spatial distribution of soil maps could be made even in low relief lands with acceptable accuracy.

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APA: Copy

MOUSAVI, SEYED ROOHOLLAH, SARMADIAN, FERAIDON, & RAHMANI, ASGHAR. (2020). Modelling and Prediction of Soil Classes Using Boosting Regression Tree and Random Forests Machine Learning Algorithms in Some Part of Qazvin Plain. IRANIAN JOURNAL OF SOIL AND WATER RESEARCH, 50(10 ), 2525-2538. SID. https://sid.ir/paper/368028/en

Vancouver: Copy

MOUSAVI SEYED ROOHOLLAH, SARMADIAN FERAIDON, RAHMANI ASGHAR. Modelling and Prediction of Soil Classes Using Boosting Regression Tree and Random Forests Machine Learning Algorithms in Some Part of Qazvin Plain. IRANIAN JOURNAL OF SOIL AND WATER RESEARCH[Internet]. 2020;50(10 ):2525-2538. Available from: https://sid.ir/paper/368028/en

IEEE: Copy

SEYED ROOHOLLAH MOUSAVI, FERAIDON SARMADIAN, and ASGHAR RAHMANI, “Modelling and Prediction of Soil Classes Using Boosting Regression Tree and Random Forests Machine Learning Algorithms in Some Part of Qazvin Plain,” IRANIAN JOURNAL OF SOIL AND WATER RESEARCH, vol. 50, no. 10 , pp. 2525–2538, 2020, [Online]. Available: https://sid.ir/paper/368028/en

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