Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

remove_circle_outline

Article Types

Countries / Regions

Search Results (1)

Search Parameters:
Keywords = Gorzineh-khil region

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
22 pages, 42266 KiB  
Article
Landslide Susceptibility Assessment by Using Convolutional Neural Network
by Shahrzad Nikoobakht, Mohammad Azarafza, Haluk Akgün and Reza Derakhshani
Appl. Sci. 2022, 12(12), 5992; https://doi.org/10.3390/app12125992 - 13 Jun 2022
Cited by 108 | Viewed by 5459
Abstract
This study performs a GIS-based landslide susceptibility assessment using a convolutional neural network, CNN, in a study area of the Gorzineh-khil region, northeastern Iran. For this assessment, a 15-layered CNN was programmed in the Python high-level language for susceptibility mapping. In this regard, [...] Read more.
This study performs a GIS-based landslide susceptibility assessment using a convolutional neural network, CNN, in a study area of the Gorzineh-khil region, northeastern Iran. For this assessment, a 15-layered CNN was programmed in the Python high-level language for susceptibility mapping. In this regard, as far as the landside triggering factors are concerned, it was concluded that the geomorphologic/topographic parameters (i.e., slope curvature, topographical elevation, slope aspect, and weathering) and water condition parameters (hydrological gradient, drainage pattern, and flow gradient) are the main triggering factors. These factors provided the landside dataset, which was input to the CNN. We used 80% of the dataset for training and the remaining 20% for testing to prepare the landslide susceptibility map of the study area. In order to cross-validate the resulting map, a loss function, and common classifiers were considered: support vector machines, SVM, k-nearest neighbor, k-NN, and decision tree, DT. An evaluation of the results of the susceptibility assessment revealed that the CNN led the other classes in terms of 79.0% accuracy, 73.0% precision, 75.0% recall, and 77.0% f1-score, and, hence, provided better accuracy and the least computational error when compared to the other models. Full article
(This article belongs to the Special Issue Geotechnical Engineering Hazards)
Show Figures

Figure 1

Back to TopTop