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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

Authors: Chao Yuan, Hossein Moayedi

Published in: Engineering with Computers | Issue 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%).

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Title: Evaluation and comparison of the advanced metaheuristic and conventional machine learning methods for the prediction of landslide occurrence
Authors: Chao Yuan
Hossein Moayedi
Publication date: 08-07-2019
Publisher: Springer London
Published in: Engineering with Computers / Issue 4/2020
Print ISSN: 0177-0667
Electronic ISSN: 1435-5663
DOI: https://doi.org/10.1007/s00366-019-00798-x

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