Evaluation and comparison of the advanced metaheuristic and conventional machine learning methods for the prediction of
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ORIGINAL ARTICLE
Evaluation and comparison of the advanced metaheuristic and conventional machine learning methods for the prediction of landslide occurrence Chao Yuan1 · Hossein Moayedi2,3 Received: 20 May 2019 / Accepted: 6 June 2019 © Springer-Verlag London Ltd., part of Springer Nature 2019
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%). Keywords Metaheuristic evolutionary · Classification · Landslide perdition
1 Introduction Traditional approaches of natural slope failure analysis employed various engineering-designed tools [1, 2]. Presenting more progressive designed tools, such as the machine learning-based predictive algorithms, draw attention to a lot of researchers [3, 4]. Most studies have exposed that the machine learning-based techniques are dependable methods to approximate the engineering complex explanations and solutions [5]. The stability of the local slopes against failure is a critical matter that has to be investigated meticulously [6, 7], because of their high impacts on the adjacent engineering buildings (e.g., projects that include excavation and transmission roads, etc.). Also, slope failures cause a lot of damages (e.g., the loss of property and human life) * Hossein Moayedi [email protected] 1
College of Architecture and Civil Engineering, Xi’an University of Science and Technology, Xi’an 710054, Shaanxi, China
2
Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
3
Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam
worldwide every year. There are many factors that need to be considered during the stability of such slopes. As an example, the saturation degree, along with other intrinsic of the soil properties, mostly affects the chances of slope failure [8, 9]. Up to now, many scientists intend to provide effective modeling for the stability of slopes [10, 11]. Some disadvantages of traditional approaches such as the necessity of utilizing laboratory equipment [12–14] along with the high level of complexity make them a difficult solution [15–18]. Additionally, they cannot be utilized as a certain solution, because of their limitation to investigate a specific slope condition (e.g.
