GIS-based landslide susceptibility assessment and mapping in Ajloun and Jerash governorates in Jordan using genetic algorithm-based ensemble models

Mabdeh, Ali Nouh; Al, Fugara A’kif; Ahmadlou, Mohammad; Al, Adamat Rida; Al, Shabeeb Abdel Rahman

Artykuł - szczegóły

Tytuł artykułu

GIS-based landslide susceptibility assessment and mapping in Ajloun and Jerash governorates in Jordan using genetic algorithm-based ensemble models

Autorzy

Mabdeh Ali Nouh , Al Fugara A’kif , Ahmadlou Mohammad , Al Adamat Rida , Al Shabeeb Abdel Rahman

Wybrane pełne teksty z tego czasopisma

https://www.springer.com/journal/11600

Identyfikatory

Warianty tytułu

Języki publikacji

Abstrakty

Landslides are a geological phenomenon that is causing considerable economic and human losses annually in various regions of the world. In some cases, the complex behaviors of some such phenomena cause that single machine learning models fail in modeling them well. To overcome this issue, this paper presents two novel genetic-algorithm (GA)-based ensemble models constructed with the decision tree (DT), k-nearest neighbors (KNN), and Naive Bayes (NB) models based on the bagging and random sub-space (RS) methods for landslide susceptibility assessment and mapping in Ajloun and Jerash governorates in Jordan. Sixteen factors, including topographic, climatic, human, and geological factors were used as possible factors that influence landslide occurrence in the study area. In addition to this, one hundred and ninety two landslide locations were employed for training and testing the models. The GA was used in this study for feature selection based on three models: DT, KNN, and NB. Model performance evaluation based on the area under the receiver operating characteristic (AUROC) curve indicated that the ensemble models outperform the standalone ones. The values of the AUROC curves in the validation phases for the five models, namely, the GA-based DT, KNN, NB, bagging-based, and RS-based ensemble model, were 0.63, 0.69, 0.63, 0.89, and 0.95, respectively. The results of this study suggest that simple models can be combined using the bagging and RS methods to produce integrated models that have higher accuracy than that of any of the individual simple models.

Słowa kluczowe

landslide susceptibility mapping ensemble model bagging random sub-space genetic algorithm

mapowanie podatności na osuwiska model regresji bagging podprzestrzeń losowa algorytm genetyczny

Wydawca

Instytut Geofizyki PAN
Springer

Czasopismo

Acta Geophysica

Rocznik

2022

Tom

Vol. 70, no 3

Strony

1253--1267

Opis fizyczny

Bibliogr. 62 poz.

Twórcy

autor

Mabdeh Ali Nouh

alinouh@aabu.edu.jo

Department of GIS and Remote Sensing, Institute of Earth and Environmental Sciences, Al Al-Bayt University, Mafraq 25113, Jordan

https://orcid.org/0000-0003-3947-5284

autor

Al Fugara A’kif

akifmohd@aabu.edu.jo

Department of Surveying Engineering, Faculty of Engineering, Al Al-Bayt University, Mafraq 25113, Jordan

autor

Ahmadlou Mohammad

mahmadlou@email.kntu.ac.ir

GIS Department, Geodesy and Geomatics Faculty, K. N. Toosi University of Technology, Tehran, Iran

autor

Al Adamat Rida

Department of Surveying Engineering, Faculty of Engineering, Al Al-Bayt University, Mafraq 25113, Jordan
Department of GIS and Remote Sensing, Institute of Earth and Environmental Sciences, Al Al-Bayt University, Mafraq 25113, Jordan

autor

Al Shabeeb Abdel Rahman

Department of GIS and Remote Sensing, Institute of Earth and Environmental Sciences, Al Al-Bayt University, Mafraq 25113, Jordan

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).

Typ dokumentu

Bibliografia

Identyfikator YADDA

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