Landslide Inventory (2001–2017) of Chittagong Hilly Areas, Bangladesh

Abstract

Landslides are a frequent natural hazard in Chittagong Hilly Areas (CHA), Bangladesh, which causes the loss of lives and damage to the economy. Despite this, an official landslide inventory is still lacking in this area. In this paper, we present a landslide inventory of this area prepared using the visual interpretation of Google Earth images (Google Earth Mapping), field mapping, and a literature search. We mapped 730 landslides that occurred from January 2001 to March 2017. Different landslide attributes including type, size, distribution, state, water content, and triggers are presented in the dataset. In this area, slide and flow were the two dominant types of landslides. Out of the five districts (Bandarban, Chittagong, Cox’s Bazar, Khagrachari, and Rangamati), most (55%) of the landslides occurred in the Chittagong and Rangamati districts. About 45% of the landslides were small (<100 m2) in size, while the maximum size of the detected landslides was 85202 m2. This dataset will help to understand the characteristics of landslides in CHA and provide useful guidance for policy implementation.

Impact of Land Use/Land Cover Change on Landslide Susceptibility in Rangamati Municipality of Rangamati District, Bangladesh

“…Unfortunately, some of the landslides (11) were old and it was difficult to determine their type. In the study area, slide (18) is the most dominant type of landslide, followed by flow (16) and complex (7).…”

“…The random forest model was used for landslide susceptibility mapping. Random forest is a widely used model in landslide susceptibility mapping since it shows better prediction capability [7,14,16,17,26]. Breiman (2001) [49] proposed a random forest model that improves the decision tree model.…”

“…Landslide inventory shows the locations of landslides, while landslide casual factors create suitable conditions for landslides [4,9]. Various statistical and machine learning models, including logistic regression, linear discriminant analysis, random forest, support vector machines, decision tree, extreme gradient boosting (XGBoost), frequency ratio, and certainty factor, have been used for landslide susceptibility mapping [7,[9][10][11][12][13][14][15][16][17]. These models explore the relationships between landslide occurrences and causal factors to determine the spatial probability over the area [8,18,19].…”

A Sustainable Way Forward: Systematic Review of Transformer Technology in Social-Media-Based Disaster Analytics

“…Traditionally, scholarly investigations into natural and anthropogenic disasters, such as landslides [1,2], have relied upon machine learning (ML) and artificial intelligence (AI) algorithms, which necessitated access to meticulously curated and validated data sources (e.g., [3][4][5]) that were often challenging to procure. The widespread adoption of social media platforms by billions of users has emerged as an alternative and sustainable avenue for acquiring data pertaining to disaster, crisis, and emergency scenarios.…”

Knowledge Discovery of Global Landslides Using Automated Machine Learning Algorithms

“…Landslides are natural events that have adverse effects on human life, infrastructure, the economy, and society [1]. To reduce the negative effects of landslides and increase the level of disaster preparedness, in-depth research on global landslides is essential [2].…”