Abstract
Knowledge about the spatial distribution of the soil physical and mechanical properties is crucial for soil management, water yield, and sustainability at the watershed scale; however, the lack of soil data hinders the application of this tool, thus urging the need to estimate soil properties and consequently, to perform the spatial distribution. This research attempted to examine the proficiency of three machine learning methods (RF: Random Forest; Cubist: Regression Tree; and SVM: Support Vector Machine) to predict soil physical and mechanical properties, saturated hydraulic conductivity (Ks), Cohesion measured by fall-cone at the saturated (Psat) and dry (Pdry) states, hardness index (HI) and dry shear strength (SS) by integrating environmental variables and soil features in the Zayandeh-Rood dam watershed, central Iran. To determine the best combination of input variables, three scenarios were examined as follows: scenario I, terrain attributes derivative from a digital elevation model (DEM) + remotely sensed data; scenario II, covariates of scenario I + selected climatic data and some thematic maps; scenario III, covariates in scenario II + intrinsic soil properties (Clay, Silt, Sand, bulk density (BD), soil organic matter (SOM), calcium carbonate equivalent (CCE), mean weight diameter (MWD) and geometric weight diameter (GWD)). The results showed that for Ks, Psat Pdry and SS, the best performance was found by the RF model in the third scenario, with R2= 0.53, 0.32, 0.31 and 0.41, respectively, while for soil hardness index (HI), Cubist model in the third scenario with R2= 0.25 showed the highest performance. For predicting Ks and Psat, soil characteristics (i.e. clay and soil SOM and BD), and land use were the most important variables. For predicting Pdry, HI, and SS, some topographical characteristics (Valley depth, catchment area, mlti-resolution of ridge top flatness index), and some soil characteristics (i.e. clay, SOM and MWD) were the most important input variables. The results of this research present moderate accuracy, however, the methodology employed provides quick and cost-effective information serving as the scientific basis for decision-making goals.
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Data Availability: The datasets generated during this study are available from the corresponding author upon reasonable request and within the framework of cooperation agreements and scientific research projects
Abbreviations
- DEM:
-
Digital elevation model
- RF:
-
Random forest
- SVM:
-
Support vector machine
- K s :
-
Soil saturated hydraulic conductivity
- P sat :
-
Cohesion measured by fall-cone at saturated state
- P dry :
-
Cohesion at dry (Pdry) state
- SS:
-
Shear strength
- HI:
-
Soil hardness index
- DSM:
-
Digital soil mapping
- cLHS:
-
Conditional Latin hypercube sampling
- CNBL:
-
Channel network base level
- CSC:
-
Cross-sectional curvature
- TPI:
-
Topographic position index
- RSP:
-
Relative slope position
- MRRTF:
-
Multi-resolution of ridge top flatness index
- MRVBF:
-
Multi-resolution valley bottom flatness index
- TWI:
-
Topographic wetness index
- VDCN:
-
Vertical distance to channel network
- CA:
-
Catchment area
- BD:
-
Bulk density
- SOM:
-
Soil organic matter
- CCE:
-
Calcium carbonate equivalent
- MWD:
-
Mean weight diameter
- GWD:
-
Geometric mean diameter
- MAE:
-
Mean absolute error
- RMSE:
-
Root mean square error
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Acknowledgments
Authors greatly acknowledged the Iranian National Science Foundation (INSF) for the financial support of this research under Project Number 4004169 and Isfahan University of Technology.
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Mohammad Sajjad Ghavami: Filed work, Laboratory analyses, writing original draft; Shamsollah Ayoubi: Methodology, Funding, Conceptualization, Writing-review & editing, Supervision; Mohamamd Reza Mosaddeghi: Conceptualization, Writing-review & editing; Salman Naimi: Data analysis and modeling, visualization.
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Ghavami, M.S., Ayoubi, S., Mosaddeghi, M.R. et al. Digital mapping of soil physical and mechanical properties using machine learning at the watershed scale. J. Mt. Sci. 20, 2975–2992 (2023). https://doi.org/10.1007/s11629-023-8056-z
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DOI: https://doi.org/10.1007/s11629-023-8056-z