Journal
ENGINEERING WITH COMPUTERS
Volume 36, Issue 4, Pages 1801-1811Publisher
SPRINGER
DOI: 10.1007/s00366-019-00798-x
Keywords
Metaheuristic evolutionary; Classification; Landslide perdition
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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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