An artificial intelligence based framework to analyze the landside risk of a mountainous highway

被引：2

作者：

Sharma, Amol ^{[1
]}

Prakash, Chander ^{[1
]}

Goshu, Estifanos Lemma ^{[2
]}

Sharma, Rajat ^{[1
]}

机构：

[1] Natl Inst Technol, Dept Civil Engn, Hamirpur, India

[2] Debre Berhan Univ, Dept Nat Resource Management, Debre Birhan, Ethiopia

来源：

GEOCARTO INTERNATIONAL | 2023年

关键词：

Landslide risk; machine learning; deep learning; artificial intelligence; remote sensing; LANDSLIDE SUSCEPTIBILITY; NEURAL-NETWORK; LOGISTIC-REGRESSION; SPATIAL PREDICTION; CERTAINTY FACTOR; MACHINE; FUZZY; MODEL; VULNERABILITY; ENTROPY;

D O I：

10.1080/10106049.2023.2186494

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

The present study entails an artificial intelligence-based framework for landslide risk analysis of a highway infrastructure in the Himalayan region. In total, 241 landslide polygons that were inventoried for the study area. The spatial component of landslide susceptibility map was prepared by incorporating drainage density, TWI, geology, elevation and slope gradient as major contributing factors, in the certainty factor-random forest (CF-RF) hybrid model with accuracy of 0.928. The landslide hazard analysis was carried out by multiplying landslide spatial and temporal probabilities. The landslide vulnerability analysis of the highway stretch was carried out by integrating the elements at risk. The built-up area was extracted by using U-Net deep learning algorithm with an accuracy of 0.964. The landslide risk map of the highway stretch prepared by the multiplication of landslide hazard and vulnerability maps depicts that 16.78% and 6.25% of the study area falls in high and very high-risk zones, respectively.

引用

页数：20

共 60 条

[1] Landslide susceptibility mapping using analytic hierarchy process and information value methods along a highway road section in Constantine, Algeria [J].