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Engineering with Computers

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08.07.2019 | Original Article

Evaluation and comparison of the advanced metaheuristic and conventional machine learning methods for the prediction of landslide occurrence

verfasst von: Chao Yuan, Hossein Moayedi

Erschienen in: Engineering with Computers | Ausgabe 4/2020

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Abstract

The present study aims to assess the superiority of the metaheuristic evolutionary when compared to the conventional machine learning classification techniques for landslide occurrence estimation. To evaluate and compare the applicability of these metaheuristic algorithms, a real-world problem of landslide assessment (i.e., including 266 records and fifteen landslide conditioning factors) is selected. In the first step, seven of the most common traditional classification techniques are applied. Then, after introducing the elite model, it is optimized using six state-of-the-art metaheuristic evolutionary techniques. The results show that applying the proposed evolutionary algorithms effectively increases the prediction accuracy from 81.6 to the range (87.8–98.3%) and the classification ratio from 58.3% to the range (60.1–85.0%).

Vorheriger Artikel Enhanced leadership-inspired grey wolf optimizer for global optimization problems

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Titel: Evaluation and comparison of the advanced metaheuristic and conventional machine learning methods for the prediction of landslide occurrence
verfasst von: Chao Yuan
Hossein Moayedi
Publikationsdatum: 08.07.2019
Verlag: Springer London
Erschienen in: Engineering with Computers / Ausgabe 4/2020
Print ISSN: 0177-0667
Elektronische ISSN: 1435-5663
DOI: https://doi.org/10.1007/s00366-019-00798-x

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