Multilayer Perceptron and Their Comparison with Two Nature-Inspired Hybrid Techniques of Biogeography-Based Optimization (BBO) and Backtracking Search Algorithm (BSA) for Assessment of Landslide Susceptibility

Hossein Moayedi (), Peren Jerfi Canatalay, Atefeh Ahmadi Dehrashid (), Mehmet Akif Cifci, Marjan Salari and Binh Nguyen Le
Additional contact information
Hossein Moayedi: Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
Peren Jerfi Canatalay: Department of Computer Engineering, Faculty of Engineering, Haliç University, Istanbul 34394, Turkey
Atefeh Ahmadi Dehrashid: Department of Climatology, Faculty of Natural Resources, University of Kurdistan, Sanandaj 6617715175, Iran
Mehmet Akif Cifci: Department of Computer Engineering, Bandirma Onyedi Eylul University, Balikesir 10200, Turkey
Marjan Salari: Department of Civil Engineering, Sirjan University of Technology, Sirjan 7813733385, Iran
Binh Nguyen Le: Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam

Land, 2023, vol. 12, issue 1, 1-25

Abstract: Regarding evaluating disaster risks in Iran’s West Kurdistan area, the multi-layer perceptron (MLP) neural network was upgraded with two novel techniques: backtracking search algorithm (BSA) and biogeography-based optimization (BBO). Utilizing 16 landslide conditioning elements such as elevation (aspect), plan (curve), profile (curvature), geology, NDVI (land use), slope (degree), stream power index (SPI), topographic wetness index (TWI), rainfall, and sediment transport index (STI), and 504 landslides as target variables, a large geographic database is constructed. Applying the techniques mentioned above to the synthesis of the MLP results in the suggested BBO-MLP and BSA-MLP ensembles. As accuracy standards, we benefit from mean absolute error, mean square error, and area under the receiving operating characteristic curve to assess the utilized models, we have also designed a scoring system. The MLP’s accuracy increases thanks to the application of the BBO and BSA algorithms. Comparing the BBO with the BSA, we find that the former achieves higher average MLP optimization ranks (20, 15, and 14). A further finding showed that the BBO is superior to the BSA at maximizing the MLP.

Keywords: landslides susceptibility assessment; multilayer perceptron; BBO algorithm; BSA algorithm (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2073-445X/12/1/242/pdf (application/pdf)
https://www.mdpi.com/2073-445X/12/1/242/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:12:y:2023:i:1:p:242-:d:1033793

Access Statistics for this article

Land is currently edited by Ms. Carol Ma

More articles in Land from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().