Journal of Range and Watershed Managment

Application of Geomorphometric attributes in digital soil mapping by using of machine learning and fuzzy logic approaches

Document Type : Research Paper

Authors

Asghar Rahmani 1
Fereydoon Sarmadian 2
Sayed Roholla Mousavi 2
Seyyed Erfan Khamoshi 3

1 Soil science department,Faculty of agriculture,university of tehran,karaj,Iran.

2 Soil Science Department, faculty of agriculture, University of Tehran

3 Soil science department,faculty of agriculture, university of tehran.

https://doi.org/10.22059/jrwm.2020.288580.1418
Abstract
Conventional soil mapping is related to High density sampling, affected by scale and expert knowledge So using of new data mining methods in digital soil properties mapping was the main aim of this study for resolving conventional soil survey problems. In this research, 62 surface soil samples based on regular grid and expert knowledge opinion were selected after that soil organic carbon(SOC), clay content and CaCO3 were determined in some part of Dryland Kuhin region with area of 372 ha. Data sets were divided to two 80%(calibration) and 20%(validation), respectively. From digital elevation model with 10-meter spatial resolution were derived 19 geomorphometric attribute in SAGA GIS software. Three geomorphometric covariate included TPI, TRI, DEM and landform map unit were chosen PCA and expert knowledge. RStudio and SoLIM Solution software were used for random forest (RF) and fuzzy logic modelling, respectively. The RF modelling results show that for SOC, clay and CaCO3 based on determination coefficient (R2) had 0.63,0.75,0.63 and RMSE 0.17,7.5,5.77 percentage and for SoLIM method revealed that R2 0.47,0.42,0.42 and RMSE 0.2,8.08,4.68 percentage, respectively. Generally, the RF model with creating nonlinear relationship among soil properties and environmental covariate can predicted digital map with appropriate precision for management and sustainable land utilization

Keywords

Digital soil mapping
Data mining
Random Forest
Soil Landscape Inference Model
Journal of Range and Watershed Managment
Volume 73, Issue 1
Spring 2020
Pages 105-124

  • Receive Date 07 September 2019
  • Revise Date 13 June 2020
  • Accept Date 19 February 2020
  • Publish Date 21 May 2020

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