eprintid: 14997
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/49/97
datestamp: 2023-11-10 03:29:35
lastmod: 2023-11-10 03:29:35
status_changed: 2023-11-10 01:58:22
type: article
metadata_visibility: show
creators_name: Balogun, A.-L.
creators_name: Rezaie, F.
creators_name: Pham, Q.B.
creators_name: Gigovi�, L.
creators_name: Drobnjak, S.
creators_name: Aina, Y.A.
creators_name: Panahi, M.
creators_name: Yekeen, S.T.
creators_name: Lee, S.
title: Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression (SVR) with GWO, BAT and COA algorithms
ispublished: pub
keywords: algorithm; heuristics; landslide; modeling; optimization; prediction; spatial analysis; support vector machine, Serbia, Canis lupus
note: cited By 92
abstract: In this study, we developed multiple hybrid machine-learning models to address parameter optimization limitations and enhance the spatial prediction of landslide susceptibility models. We created a geographic information system database, and our analysis results were used to prepare a landslide inventory map containing 359 landslide events identified from Google Earth, aerial photographs, and other validated sources. A support vector regression (SVR) machine-learning model was used to divide the landslide inventory into training (70) and testing (30) datasets. The landslide susceptibility map was produced using 14 causative factors. We applied the established gray wolf optimization (GWO) algorithm, bat algorithm (BA), and cuckoo optimization algorithm (COA) to fine-tune the parameters of the SVR model to improve its predictive accuracy. The resultant hybrid models, SVR-GWO, SVR-BA, and SVR-COA, were validated in terms of the area under curve (AUC) and root mean square error (RMSE). The AUC values for the SVR-GWO (0.733), SVR-BA (0.724), and SVR-COA (0.738) models indicate their good prediction rates for landslide susceptibility modeling. SVR-COA had the greatest accuracy, with an RMSE of 0.21687, and SVR-BA had the least accuracy, with an RMSE of 0.23046. The three optimized hybrid models outperformed the SVR model (AUC = 0.704, RMSE = 0.26689), confirming the ability of metaheuristic algorithms to improve model performance. © 2020 Elsevier B.V.
date: 2021
publisher: Elsevier B.V.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099248181&doi=10.1016%2fj.gsf.2020.10.009&partnerID=40&md5=b196fab9f3f4b9d79fb46fd858d6efe6
id_number: 10.1016/j.gsf.2020.10.009
full_text_status: none
publication: Geoscience Frontiers
volume: 12
number: 3
refereed: TRUE
issn: 16749871
citation:   Balogun, A.-L. and Rezaie, F. and Pham, Q.B. and Gigovi�, L. and Drobnjak, S. and Aina, Y.A. and Panahi, M. and Yekeen, S.T. and Lee, S.  (2021) Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression (SVR) with GWO, BAT and COA algorithms.  Geoscience Frontiers, 12 (3).   ISSN 16749871