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

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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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  1. College of Architecture and Civil Engineering, Xi’an University of Science and Technology, Xi’an, 710054, Shaanxi, China

    Chao Yuan

  2. Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Hossein Moayedi

  3. Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Hossein Moayedi

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  1. Chao Yuan
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Yuan, C., Moayedi, H. Evaluation and comparison of the advanced metaheuristic and conventional machine learning methods for the prediction of landslide occurrence. Engineering with Computers 36, 1801–1811 (2020). https://doi.org/10.1007/s00366-019-00798-x

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