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

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 (as...

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Vydáno v:Land (Basel) Ročník 12; číslo 1; s. 242
Hlavní autoři: Moayedi, Hossein, Canatalay, Peren Jerfi, Ahmadi Dehrashid, Atefeh, Cifci, Mehmet Akif, Salari, Marjan, Le, Binh Nguyen
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.01.2023
Témata:
Algorithms
Artificial intelligence
BBO algorithm
Biogeography
BSA algorithm
Disaster risk
Evolution
Genetic algorithms
Geology
Land use
Landslides
Landslides & mudslides
landslides susceptibility assessment
multilayer perceptron
Multilayer perceptrons
Multilayers
Neural networks
Optimization
Optimization algorithms
Rainfall
Search algorithms
Sediment transport
Iran
ISSN:2073-445X, 2073-445X
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Shrnutí: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.
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ISSN:2073-445X
2073-445X
DOI:10.3390/land12010242