Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms

Clicks: 318
ID: 115014
2020
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
Emerging

Ranked #324 of 862 articles by views in International journal of environmental research and public health

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 862 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
Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing landslide susceptibility maps that can be used by land-use managers to reduce injury and damage. This paper contributes to this effort by comparing the power and effectiveness of five machine learning, benchmark algorithms—Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine—in creating a reliable shallow landslide susceptibility map for Bijar City in Kurdistan province, Iran. Twenty conditioning factors were applied to 111 shallow landslides and tested using the One-R attribute evaluation (ORAE) technique for modeling and validation processes. The performance of the models was assessed by statistical-based indexes including sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). Results indicate that all the five machine learning models performed well for shallow landslide susceptibility assessment, but the Logistic Model Tree model (AUC = 0.932) had the highest goodness-of-fit and prediction accuracy, followed by the Logistic Regression (AUC = 0.932), Naïve Bayes Tree (AUC = 0.864), ANN (AUC = 0.860), and Support Vector Machine (AUC = 0.834) models. Therefore, we recommend the use of the Logistic Model Tree model in shallow landslide mapping programs in semi-arid regions to help decision makers, planners, land-use managers, and government agencies mitigate the hazard and risk.
Reference Key
nhu2020internationalshallow Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Viet-Ha Nhu;Ataollah Shirzadi;Himan Shahabi;Sushant K. Singh;Nadhir Al-Ansari;John J. Clague;Abolfazl Jaafari;Wei Chen;Shaghayegh Miraki;Jie Dou;Chinh Luu;Krzysztof Górski;Binh Thai Pham;Huu Duy Nguyen;Baharin Bin Ahmad;Nhu, Viet-Ha;Shirzadi, Ataollah;Shahabi, Himan;Singh, Sushant K.;Al-Ansari, Nadhir;Clague, John J.;Jaafari, Abolfazl;Chen, Wei;Miraki, Shaghayegh;Dou, Jie;Luu, Chinh;Górski, Krzysztof;Thai Pham, Binh;Nguyen, Huu Duy;Ahmad, Baharin Bin;
Journal International journal of environmental research and public health
Year 2020
DOI
10.3390/ijerph17082749
URL
https://www.mdpi.com/1660-4601/17/8/2749
Keywords
artificial intelligence Iran goodness-of-fit prediction accuracy logistic model tree shallow landslide

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.