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Soil Erosion Susceptibility Mapping in Kozetopraghi Catchment, Iran: A Mixed Approach Using Rainfall Simulator and Data Mining Techniques

Author

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  • Abazar Esmali Ouri

    (Department of Watershed Management, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil 5619911367, Iran)

  • Mohammad Golshan

    (Administration of Natural Resources and Watershed Management, Astara, Guilan 4391817897, Iran)

  • Saeid Janizadeh

    (Department of Watershed Management Engineering and Sciences, Faculty in Natural Resources and Marine Science, Tarbiat Modares University, Tehran 14115-111, Iran)

  • Artemi Cerdà

    (Soil Erosion and Degradation Research Group Department of Geography, Valencia University, Blasco Ibàñez, 28, 46010 Valencia, Spain)

  • Assefa M. Melesse

    (Department of Earth and Environment, AHC-5-390, Florida International University, 11200 SW 8th Street, Miami, FL 33199, USA)

Abstract

Soil erosion determines landforms, soil formation and distribution, soil fertility, and land degradation processes. In arid and semiarid ecosystems, soil erosion is a key process to understand, foresee, and prevent desertification. Addressing soil erosion throughout watersheds scales requires basic information to develop soil erosion control strategies and to reduce land degradation. To assess and remediate the non-sustainable soil erosion rates, restoration programs benefit from the knowledge of the spatial distribution of the soil losses to develop maps of soil erosion. This study presents Support Vector Machine (SVM), Random Forest (RF), and adaptive boosting (AdaBoost) data mining models to map soil erosion susceptibility in Kozetopraghi watershed, Iran. A soil erosion inventory map was prepared from field rainfall simulation experiments on 174 randomly selected points along the Kozetopraghi watershed. In previous studies, this map has been prepared using indirect methods such as the Universal Soil Loss Equation to assess soil erosion. Direct field measurements for mapping soil erosion susceptibility have so far not been carried out in our study site in the past. The soil erosion rate data generated by simulated rainfall in 1 m 2 plots at rainfall rate of 40 mmh −1 was used to develop the soil erosion map. Of the available data, 70% and 30% were randomly classified to calibrate and validate the models, respectively. As a result, the RF model with the highest area under the curve (AUC) value in a receiver operating characteristics (ROC) curve (0.91), and the lowest mean square error (MSE) value (0.09), has the most concordance and spatial differentiation. Sensitivity analysis by Jackknife and IncNodePurity methods indicates that the slope angle is the most important factor within the soil erosion susceptibility map. The RF susceptibility map showed that the areas located in the center and near the watershed outlet have the most susceptibility to soil erosion. This information can be used to support the development of sustainable restoration plans with more accuracy. Our methodology has been evaluated and can be also applied in other regions.

Suggested Citation

  • Abazar Esmali Ouri & Mohammad Golshan & Saeid Janizadeh & Artemi Cerdà & Assefa M. Melesse, 2020. "Soil Erosion Susceptibility Mapping in Kozetopraghi Catchment, Iran: A Mixed Approach Using Rainfall Simulator and Data Mining Techniques," Land, MDPI, vol. 9(10), pages 1-18, October.
    • Handle: RePEc:gam:jlands:v:9:y:2020:i:10:p:368-:d:422914
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    1. Ara Jeong & Ronald I. Dorn & Yeong-Bae Seong & Byung-Yong Yu, 2021. "Acceleration of Soil Erosion by Different Land Uses in Arid Lands above 10 Be Natural Background Rates: Case Study in the Sonoran Desert, USA," Land, MDPI, vol. 10(8), pages 1-28, August.

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