Webbläsaren som du använder stöds inte av denna webbplats. Alla versioner av Internet Explorer stöds inte längre, av oss eller Microsoft (läs mer här: * https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Var god och använd en modern webbläsare för att ta del av denna webbplats, som t.ex. nyaste versioner av Edge, Chrome, Firefox eller Safari osv.

Default user image.

Pengxiang Zhao

Forskare

Default user image.

A GIS-Based Landslide Susceptibility Mapping and Variable Importance Analysis Using Artificial Intelligent Training-Based Methods

Författare

  • Pengxiang Zhao
  • Zohreh Masoumi
  • Maryam Kalantari
  • Mahtab Aflaki
  • Ali Mansourian

Summary, in English

Landslides often cause significant casualties and economic losses, and therefore landslide susceptibility mapping (LSM) has become increasingly urgent and important. The potential of deep learning (DL) like convolutional neural networks (CNN) based on landslide causative factors has not been fully explored yet. The main target of this study is the investigation of a GIS-based LSM in Zanjan, Iran and to explore the most important causative factor of landslides in the case study area. Different machine learning (ML) methods have been employed and compared to select the best results in the case study area. The CNN is compared with four ML algorithms, including random forest (RF), artificial neural network (ANN), support vector machine (SVM), and logistic regression (LR). To do so, sixteen landslide causative factors have been extracted and their related spatial layers have been prepared. Then, the algorithms were trained with related landslide and non-landslide points. The results illustrate that the five ML algorithms performed suitably (precision = 82.43–85.6%, AUC = 0.934–0.967). The RF algorithm achieves the best result, while the CNN, SVM, the ANN, and the LR have the best results after RF, respectively, in this case study. Moreover, variable importance analysis results indicate that slope and topographic curvature contribute more to the prediction. The results would be beneficial to planning strategies for landslide risk management.

Avdelning/ar

  • Institutionen för naturgeografi och ekosystemvetenskap
  • Centrum för Mellanösternstudier (CMES)
  • MECW: The Middle East in the Contemporary World

Publiceringsår

2022-01-01

Språk

Engelska

Publikation/Tidskrift/Serie

Remote Sensing

Volym

14

Issue

1

Dokumenttyp

Artikel i tidskrift

Förlag

MDPI AG

Ämne

  • Engineering and Technology
  • Earth and Related Environmental Sciences

Nyckelord

  • Deep learning
  • Feature importance
  • Landslide causative factors
  • Landslide susceptibility mapping
  • Machine learning
  • Artificial intelligence (AI)
  • Geospatial Artificial Intelligence (GeoAI)

Status

Published

ISBN/ISSN/Övrigt

  • ISSN: 2072-4292