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Gully Erosion Modeling Using GIS-Based Data Mining Techniques in Northern Iran: A Comparison Between Boosted Regression Tree and Multivariate Adaptive Regression Spline

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Natural Hazards GIS-Based Spatial Modeling Using Data Mining Techniques

Part of the book series: Advances in Natural and Technological Hazards Research ((NTHR,volume 48))

Abstract

Land degradation occurs in the form of soil erosion in many regions of the world. Among the different type of soil erosion, high sediment yield volume in the watersheds is allocated to gully erosion. So, the purpose of this research is to map the susceptibility of the Valasht Watershed in northern Iran (Mazandaran Province) to gully erosion. For this purpose, spatial distribution of gullies was digitized by sampling and field monitoring. Identified gullies were divided into a training (two-thirds) and validating (one-third) datasets. In the second step, eleven effective factors including topographic (elevation, aspect, slope degree, TWI, plan curvature, and profile curvature), hydrologic (distance from river and drainage density), man-made (land use, distance from roads), and lithology factors were considered for spatial modeling of gully erosion. Then, Boosted Regression Tree (BRT) and Multivariate Adaptive Regression Spline (MARS) algorithms were implemented to model gully erosion susceptibility. Finally, Receiver Operating Characteristic (ROC) used for the assessment of prepared models. Based on the findings, BRT model (AUC = 0.894) had better efficiency than MARS model) AUC = 0.841) for gully erosion modeling and located in very good class of accuracy. In addition, altitude, aspect, slope degree, and land use were selected as the most conditioning agents on the gully erosion occurrence. The results of this research can be used for the prioritization of critical areas and better decision making in the soil and water management in the Valasht Watershed. In addition, the used models are recommended for spatial modeling in other regions of the worlds.

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Authors and Affiliations

  1. Department of Watershed Management Engineering, Faculty of Natural Resources, Tarbiat Modares University, Tehran, Iran

    Mohsen Zabihi, Alireza Motevalli & Mohamad Ali Zakeri

  2. Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran

    Hamid Reza Pourghasemi

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  1. Mohsen Zabihi
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  2. Hamid Reza Pourghasemi
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  3. Alireza Motevalli
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  4. Mohamad Ali Zakeri
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Correspondence to Hamid Reza Pourghasemi .

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Editors and Affiliations

  1. Natural Resources and Environmental Engineering, Shiraz University, Shiraz, Iran

    Hamid Reza Pourghasemi

  2. IRPI, National Research Council, Perugia, Italy

    Mauro Rossi

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Zabihi, M., Pourghasemi, H.R., Motevalli, A., Zakeri, M.A. (2019). Gully Erosion Modeling Using GIS-Based Data Mining Techniques in Northern Iran: A Comparison Between Boosted Regression Tree and Multivariate Adaptive Regression Spline. In: Pourghasemi, H., Rossi, M. (eds) Natural Hazards GIS-Based Spatial Modeling Using Data Mining Techniques. Advances in Natural and Technological Hazards Research, vol 48. Springer, Cham. https://doi.org/10.1007/978-3-319-73383-8_1

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