A Novel Integrated Approach of Relevance Vector Machine Optimized by Imperialist Competitive Algorithm for Spatial Modeling of Shallow Landslides

Clicks: 462
ID: 118096
2018
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #4 of 170 articles by views in remote sensing

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 170 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
This research aims at proposing a new artificial intelligence approach (namely RVM-ICA) which is based on the Relevance Vector Machine (RVM) and the Imperialist Competitive Algorithm (ICA) optimization for landslide susceptibility modeling. A Geographic Information System (GIS) spatial database was generated from Lang Son city in Lang Son province (Vietnam). This GIS database includes a landslide inventory map and fourteen landslide conditioning factors. The suitability of these factors for landslide susceptibility modeling in the study area was verified by the Information Gain Ratio (IGR) technique. A landslide susceptibility prediction model based on RVM-ICA and the GIS database was established by training and prediction phases. The predictive capability of the new approach was evaluated by calculations of sensitivity, specificity, accuracy, and the area under the Receiver Operating Characteristic curve (AUC). In addition, to assess the applicability of the proposed model, two state-of-the-art soft computing techniques including the support vector machine (SVM) and logistic regression (LR) were used as benchmark methods. The results of this study show that RVM-ICA with AUC = 0.92 achieved a high goodness-of-fit based on both the training and testing datasets. The predictive capability of RVM-ICA outperformed those of SVM with AUC = 0.91 and LR with AUC = 0.87. The experimental results confirm that the newly proposed model is a very promising alternative to assist planners and decision makers in the task of managing landslide prone areas.
Reference Key
bui2018remotea Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Dieu Tien Bui;Himan Shahabi;Ataollah Shirzadi;Kamran Chapi;Nhat-Duc Hoang;Binh Thai Pham;Quang-Thanh Bui;Chuyen-Trung Tran;Mahdi Panahi;Baharin Bin Ahmad;Lee Saro;Tien Bui, Dieu;Shahabi, Himan;Shirzadi, Ataollah;Chapi, Kamran;Hoang, Nhat-Duc;Pham, Binh Thai;Bui, Quang-Thanh;Tran, Chuyen-Trung;Panahi, Mahdi;Bin Ahmad, Baharin;Saro, Lee;
Journal remote sensing
Year 2018
DOI
10.3390/rs10101538
URL
https://dx.doi.org/10.3390/rs10101538
Keywords
gis vietnam shallow landslide relevance vector machine hybrid intelligence imperialist competitive algorithm

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.