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A New Approach for Smart Soil Erosion Modeling: Integration of Empirical and Machine-Learning Models

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Abstract

Despite advances in artificial intelligence modeling, the lack of soil-erosion data and other watershed information still limits soil-erosion modeling. The limited number of parameters and a lack of evaluation criteria are major disadvantages to the use of empirical soil-erosion models. To overcome these limitations, we introduce a new approach that integrates empirical and artificial intelligence models. Erosion-prone locations that experience ≥ 16 tons/ha/year of erosion are identified using the RUSLE model. A soil-erosion map is prepared using 4 machine-learning algorithms: random forest (RF), artificial neural network (ANN), classification tree analysis (CTA), and generalized linear model (GLM). Thirteen factors (river buffer, aspect, slope, soil properties (texture, EC, depth, density, available water, hydrological groups), rainfall erosivity, land use, drainage density, and physiographic features) that influence the severity of soil erosion were compiled for the Talar watershed, Iran, for input into modeling processes in order to improve the accuracy of spatial prediction of erosion. The results reveal that the RF model has the highest prediction performance (AUC = 0.97, kappa = 0.78, accuracy = 0.93, and bias = 0.92). A soil-erosion distribution predicted by RF forecast that 53.42% of the Talar watershed had very low soil erosion risk, 12.84% had low erosion risk, 9.24% had moderate risk, 9.5% had high risk, and 14.98% had very high risk. The results indicate that slope angle, land use/land cover, elevation, and rainfall erosivity are the factors that have the greatest influence on the likelihood of soil erosion in the watershed. Curvature and topography position index (TPI) were removed from the analysis due to multicollinearity with other factors. The resulting modeling procedure can improve the identification of soil erosion hot spots, especially in watersheds lacking soil-erosion data.

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Authors and Affiliations

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

    Mohammadtaghi Avand, Maziar Mohammadi & Fahimeh Mirchooli

  2. Department of Forests, Rangelands, and Watershed Management Engineering, Kohgiluyeh & Boyer Ahmad Agricultural and Natural Resources Research and Education Centre, AREEO, Yasouj, Iran

    Mohammadtaghi Avand

  3. Faculty of Natural Resources, Sari Agricultural Sciences and Natural Resources University, Sari, Iran

    Ataollah Kavian

  4. Department of Geography and Environmental Studies, Texas State University, San Marcos, TX, USA

    John P. Tiefenbacher

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Contributions

All authors contributed to the study’s conception and design. Material preparation, data collection, and analysis were performed by [M. Avand], [M. Mohammadi], and [F. Mirchouli]. The first draft of the manuscript was written by [M. Avand] and [M. Mohammadi]. The review and editing of the article were done by [Kavian] and [Tiefenbacher]. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Avand, M., Mohammadi, M., Mirchooli, F. et al. A New Approach for Smart Soil Erosion Modeling: Integration of Empirical and Machine-Learning Models. Environ Model Assess 28, 145–160 (2023). https://doi.org/10.1007/s10666-022-09858-x

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