The root causes of landslide vulnerability in Bangladesh

Abstract

Communities living in the Chittagong Hill Districts (CHD) of Bangladesh recurrently observe landslide disasters during the monsoon season (June–September). CHD is primarily dominated by three distinct groups of hill communities, namely, urbanised hill (Bengali), indigenous tribal and stateless Rohingya refugees. Landslide vulnerability amongst them is complex and varies between physical, social, economic, environmental, institutional and cultural dimensions. This study aims to understand driving forces of landslide disasters in the region by emphasising human factors. Data from the three contrasting communities were collected through participatory workshops, in-depth interviews and fieldwork observation. The participants were local people and landslide experts who were purposefully selected from five case study communities in the CHD. They ranked different socio-economic problems, identified causes of landslides and proposed landslide mitigation action plans. Results suggest that the urbanised Bengali and Rohingya refugee communities are highly vulnerable to landslides. The urbanised hill communities largely deal with poverty, social injustice, lack of planning regulations and illegal hill cutting issues, whereas the Rohingya refugees’ predominant constraints are linked to the ongoing genocide and state-sponsored violence in Myanmar hindering their sustainable repatriation, and their protracted living conditions in Bangladesh. The indigenous tribal communities are comparatively resilient to landslides due to their unique history, traditional knowledge, cultural heritage and lifestyle. Landslides in the CHD should be characterised as socio-natural hazards since the components of landslide disasters are profoundly intertwined with the culture–conflict–corruption nexus.

Knowledge Discovery of Global Landslides Using Automated Machine Learning Algorithms

“…Traditionally, town planners and strategic decision-ma kers have relied on traditional statistical analyses of regional landslide databases for strategic planning and policy implementation [2], [15], [17], [35], [36]. Previous research into ML-based algorithms has also relied on regional and local datasets, where the data were manually prepared and ML models were manually created by expert data scientists, researchers, and engineers.…”

“…The ML-based knowledge discovery solution proposed in this study was implemented using Microsoft Power BI, whch is freely available for download from https://app.powerbi.com/. The user can download the complete source files (.pbix) along with global landslide data (.csv) files from the authors' GitHub source control site at https://github.com/DrSufi/GlobalLandslide [17]. After downloading and opening the entire solution using MS Power BI Desktop, the user can host the solution either in Microsoft Cloud or a local network to make it available to other researchers or strategic planners.…”

Bioengineering and Hydromechanics of Flora in Stabilization of Slopes

“…An overall definition of safety factor (F) of slopes result from the comparison of the down slopes and shear pressure with the shear strength of soils, alongside a known and assumed rupture surfaces. Based on this overall definition, B. Ahmed in [8] have grouped the causes of landslides into external causes resulting from the development of shear stress i.e. transition in the regime of water, drawdown, vibration or shocks, loads of slope crests, unloading slope toes and geometrical transitions, such as seepage erosion, weathering and progressive failures.…”

Geological and soil engineering properties of shallow landslides occurring in the Kutupalong Rohingya Camp in Cox’s Bazar, Bangladesh

“…During the 2020 monsoon season (June to October), around 5580 refugees were affected by 638 slope failures, another 710 wind/storm and 31 flooding events in the camp area (ISCG 2020). Landslides in this area/region have been studied in terms of spatial susceptibility zoning (Ahmed 2015;Tehrani and Husken 2019), geospatial mapping (Braun et al 2019;Hasan et al 2020), community vulnerability assessment (Ahmed 2021;Sultana 2020), risksensitive land use planning (CDMP-II 2012) and rainfall threshold determination and hydrometeorological early warning system development (Ahmed et al 2020). Low shear strength, dispersive behaviour of soil due to the high clay content and intense seasonal rainfall coupled with anthropogenic activities were identified as the leading causes of landslides in the region (Ahmed 2021;Highland and Bobrowsky 2008).…”