Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms

Clicks: 250
ID: 261697
2019
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 #134 of 275 articles by views in sustainability

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 275 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
Landslides have multidimensional effects on the socioeconomic as well as environmental conditions of the impacted areas. The aim of this study is the spatial prediction of landslide using hybrid machine learning models including bagging (BA), random subspace (RS) and rotation forest (RF) with alternating decision tree (ADTree) as base classifier in the northern part of the Pithoragarh district, Uttarakhand, Himalaya, India. To construct the database, ten conditioning factors and a total of 103 landslide locations with a ratio of 70/30 were used. The significant factors were determined by chi-square attribute evaluation (CSEA) technique. The validity of the hybrid models was assessed by true positive rate (TP Rate), false positive rate (FP Rate), recall (sensitivity), precision, F-measure and area under the receiver operatic characteristic curve (AUC). Results concluded that land cover was the most important factor while curvature had no effect on landslide occurrence in the study area and it was removed from the modelling process. Additionally, results indicated that although all ensemble models enhanced the power prediction of the ADTree classifier (AUCtraining = 0.859; AUCvalidation = 0.813); however, the RS ensemble model (AUCtraining = 0.883; AUCvalidation = 0.842) outperformed and outclassed the RF (AUCtraining = 0.871; AUCvalidation = 0.840), and the BA (AUCtraining = 0.865; AUCvalidation = 0.836) ensemble model. The obtained results would be helpful for recognizing the landslide prone areas in future to better manage and decrease the damage and negative impacts on the environment.
Reference Key
pham2019sustainabilitylandslide Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Binh Thai Pham;Ataollah Shirzadi;Himan Shahabi;Ebrahim Omidvar;Sushant K. Singh;Mehebub Sahana;Dawood Talebpour Asl;Baharin Bin Ahmad;Nguyen Kim Quoc;Saro Lee;Thai Pham, Binh;Shirzadi, Ataollah;Shahabi, Himan;Omidvar, Ebrahim;Singh, Sushant K.;Sahana, Mehebub;Talebpour Asl, Dawood;Bin Ahmad, Baharin;Kim Quoc, Nguyen;Lee, Saro;
Journal sustainability
Year 2019
DOI
10.3390/su11164386
URL
https://dx.doi.org/10.3390/su11164386
Keywords
performance india gis goodness-of-fit landslide meta classifier

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.