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Spatial mapping of gully erosion susceptibility using an efficient metaheuristic neural network

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Abstract

Developing a susceptibility map is a crucial primary step for dealing with undesirable natural phenomena, gully erosion included. On the other hand, recent computational progress call for employing new methodologies to keep the solutions updated. In this work, the performance of a conventional artificial neural network (ANN) is improved by applying a metaheuristic algorithm (symbiotic organisms search—SOS) for generating the gully erosion susceptibility map of an area in Golestan Province, Northern Iran. A geo-database is created from the gully erosion inventory and twenty conditioning factors. After analyzing the interrelated relationships between the geo-database components, training and testing data sets are formed. The models are executed with proper configurations and according to the results, the SOS algorithm could enhance the training accuracy of the ANN from 92.8% to 98.4%, and testing accuracy from 89.8% to 91.4%. In addition, comparing the performance of the SOS with shuffled complex evolution (SCE-NN) and electromagnetic field optimization (EFO-NN) algorithms revealed the greater accuracy of the SOS. However, the SCE-NN and EFO-NN performed more accurately than conventional ANN. Therefore, it can be concluded that the use of metaheuristic techniques may improve the prediction ability of the ANN in gully erosion susceptibility mapping. Finally, a monolithic equation is extracted from the SOS–ANN model to be used as a predictive formula for similar purposes.

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Availability of data and materials

The data used for the current study is available upon the reasonable request from the authors.

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Politecnico di Milano, Lecco Campus, Lecco, Italy

    Mohammad Mehrabi

  2. Department of Watershed Management, Gorgan University of Agricultural Sciences and Natural Resources (GUASNR), Gorgan, Golestan, Iran

    Omid Asadi Nalivan

  3. Department of Architecture, Built Environment and Construction Engineering, Politecnico di Milano, Milan, Italy

    Marco Scaioni

  4. Faculty of Natural Resources and Agriculture, Islamic Azad University, Science and Research Branch, Tehran, Iran

    Mahdiyeh Karvarinasab

  5. Research Department of Natural Resources, Golestan Agricultural and Natural Resources Research and Education Center, AREEO, Gorgan, Iran

    Aiding Kornejady

  6. Institute of Research and Development, Duy Tan University, Da Nang, Vietnam

    Hossein Moayedi

  7. School of Engineering and Technology, Duy Tan University, Da Nang, Vietnam

    Hossein Moayedi

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  1. Mohammad Mehrabi
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  2. Omid Asadi Nalivan
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  3. Marco Scaioni
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  4. Mahdiyeh Karvarinasab
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  5. Aiding Kornejady
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  6. Hossein Moayedi
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by MM, OAN, and MS. The first draft of the manuscript was written by MM, OAN, MK, and AK. MS and HM commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Hossein Moayedi.

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Mehrabi, M., Nalivan, O.A., Scaioni, M. et al. Spatial mapping of gully erosion susceptibility using an efficient metaheuristic neural network. Environ Earth Sci 82, 459 (2023). https://doi.org/10.1007/s12665-023-11106-8

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