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0000-0001-5092-5528
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University College London
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Publisher: MDPI AG
Date: 18-12-2014
DOI: 10.3390/IJGI3041412
Publisher: Frontiers Media SA
Date: 18-06-2019
Publisher: Springer Science and Business Media LLC
Date: 08-2015
Publisher: MDPI AG
Date: 28-01-2023
DOI: 10.3390/LAND12020352
Abstract: The influx of nearly a million refugees from Myanmar’s Rakhine state to Cox’s Bazar, Bangladesh, in August 2017 put significant pressure on the regional landscape leading to land degradation due to biomass removal to provide shelter and fuel energy and posed critical challenges for both host and displaced population. This article emphasizes geospatial applications at different stages of addressing land degradation in Cox’s Bazar. A wide range of data and methods were used to delineate land tenure, estimate wood fuel demand and supply, assess land degradation, evaluate land restoration suitability, and monitor restoration activities. The quantitative and spatially explicit information from these geospatial assessments integrated with the technical guidelines for sustainable land management and an adaptive management strategy was critical in enabling a collaborative, multi-disciplinary and evidence-based approach to successfully restoring degraded landscapes in a displacement setting.
Publisher: Elsevier BV
Date: 04-2019
Publisher: Public Library of Science (PLoS)
Date: 02-11-2021
DOI: 10.1371/JOURNAL.PONE.0259098
Abstract: Cyclone Amphan swept into Bangladesh’s southwestern coast at the end of May 2020, wreaking havoc on food security and economic stability, as well as possibly worsening mental health. We studied the prevalence of post-cyclone stressors in adults following the cyclone and its association with symptoms of psychological distress. We conducted a cross-sectional study in a coastal district of Bangladesh. A five-item brief symptom rating scale was used to measure the symptoms of psychological distress. Household food insecurity was measured using the USAID Household Food Insecurity Access Scale questionnaire. We estimated adjusted prevalence ratios (aPRs) using robust log-linear models adjusted for potential confounders. A total of 478 adults (mean [SD] age, 37.0[12.6] years 169[35.4%] women) participated in the study. The prevalence of moderate-to-severe psychological symptoms and suicidal ideation was 55.7% and 10.9%, respectively. Following the cyclone, 40.8% of the adults reported severe food insecurity, and 66% of them reported moderate-to-severe mental health symptoms. Also, 54.4% of women and 33.7% of men reported severe food insecurity in the households. Moreover, 25.5% of respondents reported no income or a significant income loss after the cyclone, and 65.5% of them had moderate-to-severe psychological symptoms. Also, 13.8% of respondents reported housing displacement because of severely damaged houses, and 68.2% of them reported moderate-to-severe psychological symptoms. The high prevalence of mental health symptoms was found in women (aPR = 1.41, 95% CI = 1.06–1.82), people with severe food insecurity (aPR = 1.63, 95% CI = 1.01–2.64), and people who lost jobs or lost a major income source (aPR = 1.25, 95% CI = 1.02–1.54). Following cyclone Amphan, many low-income in iduals saw their income drop drastically while others were unemployed and living with severe food insecurity. The result suggests gender inequalities in food-security after the cyclone. Immediate action is needed to ensure household food-security for reducing the burden of mental illness. Rising opportunities of paid-jobs and decreasing income-loss, especially for the poor people, can have a protective impact on psychological distress. However, due to the high prevalence of severe psychological symptoms, long-term mental health services are required among the population of coastal Bangladesh.
Publisher: Elsevier BV
Date: 03-2018
Publisher: Elsevier BV
Date: 11-2020
Publisher: Copernicus GmbH
Date: 13-04-2023
Abstract: Abstract. In recent years, several catastrophic landslide events have been observed throughout the globe, significantly affecting the loss of lives, infrastructure, everyday life and livelihood. To minimize the impact of landslides and issue early warnings, landslide susceptibility maps (LSM) are essential. Aim to improve the accuracy of LSM, this study applied a random selection of non-landslide s les and low accuracy of in idual classifiers using machine learning (ML) techniques, coupled with ensemble learning and ML, for LSM. China's Zigui-Badong section of the Three Gorges Reservoir area (TGRA) was considered a case study. Twelve influencing factors were selected as inputs for modelling, and the relationship between each causal factor and landslide spatial development was quantitatively analyzed. A total of 179 landslides were identified in the present study. About 70 % of the landslide pixels were randomly considered for training, and the remaining 30 % were used for validation. The Logistic Regression model (LR) was applied to produce an initial susceptibility map, and the non-landslide s les were selected within the classified low-susceptibility area. Subsequently, two ML classifiers – the Classification and Regression Tree (CART), and the Multi-Layer Perceptron (MLP), and four coupling models – the CART-Bagging, CART-Boosting, MLP-Bagging, and MLP-Boosting, were utilized for LSM. Finally, the receiver operating characteristics (ROC) curve and statistical analysis were applied for accuracy assessment. The results show that elevation and distance to rivers were the main causal factors of landslide development in the study area. The modeling accuracy of LR-MLP was calculated approx. 0.901, which is higher than the LR-CART (0.889). The LR-MLP-Boosting performed the best with an accuracy of 0.986 followed by the LR-CART-Bagging (0.973), LR-CART-Boosting (0.981), and LR-MLP-Bagging (0.978). The accuracy has been improved compared with the NO-CART, NO-MLP, NO-CART-Bagging, NO-CART-Boosting, NO-MLP-Bagging, and NO-MLP-Boosting models. Four ensemble models outperformed their corresponding classifiers, while Boosting outperforms Bagging. Overall, the combination of ensemble learning and ML effectively improved the accuracy of LSM. The LR model can effectively constrain the selection range of non-landslide s les and enhance the quality of s le selection. Our results show promise to map susceptible landslides locations which will help to monitor for an early warning of the landside.
Publisher: Springer Science and Business Media LLC
Date: 02-08-2022
DOI: 10.1186/S40677-022-00219-0
Abstract: This article critically investigates a catastrophic rainfall-induced landslide event that occurred on 27 July 2021 in the Kutupalong Rohingya C (KRC) in Cox’s Bazar, Bangladesh, from geological and geomorphological perspectives. Large-scale anthropogenic interventions mainly caused the disastrous landslide event in the KRC in addition to intense rainfall. Before the landslide occurrence, about 300 mm of cumulative rainfall was recorded in the previous seven days and 120 mm of rainfall during the landslide event. A preliminary investigation was conducted to understand the extent, causative factors, and landslide characteristics. The landslide is of mud-flow type, but on the nearby slope, slumping was also visible. The landslide length was about 33 m, width 31 m, and area 612 m 2 . The approximate volume of slope materials displaced during the landslide event was about 2450 m 3 . The displaced slope materials mainly were silt and sand. The landslide event caused five fatalities and damaged nearly 5000 shelters in the KRC area. The devastation from such a small landslide event was attributed to dense households on the slope’s hilltop, slope, and toe. The c areas and host communities are subjected to frequent and fatal landslides in the years to come due to intense human interventions and climatic conditions. The modifications of the slopes have been reducing the cohesion and the shear strength of the slope materials. Therefore, it is recommended to undertake proper mitigation and preparedness measures, including developing and implementing a landslide early warning system to address the emerging humanitarian crisis in the KRC and its surroundings.
Publisher: Springer Science and Business Media LLC
Date: 09-06-2018
Publisher: MDPI AG
Date: 15-11-2013
DOI: 10.3390/RS5115969
Publisher: Penerbit UTM Press
Date: 07-12-2014
DOI: 10.11113/JT.V71.3763
Abstract: Over the next decade, developing countries like Bangladesh will experience an alarming increase in road accidents and this will continue to remain as a serious challenge. Developing countries are experiencing a very high growth rate of vehicles, which is doubling the vehicle fleet in some countries in even five years. The complexity of the road environment with mixed traffic is another reality of road transportation in Dhaka, Bangladesh, where road planning and designs are not appropriate for mixed traffic conditions. Of particular concern are the urban intersections where differential approach speed is a major problem. The heterogeneity of traffic exceeding the capacity, plying of modes with varying speed and maneuvering time make the road links as well as intersections of Dhaka even more complex. The objectives of the study are to determine the characteristics of the road traffic accidents of Dhaka city for the following parameters: a) Traffic accident trend b) Traffic Control Parameters c) Traffic Accident at Junctions d) Traffic Control and Road Dividers e) Traffic Accidents and Road Geometry. Traffic accident data for the period of year 2007-2011 were collected from the Police Stations of the Dhaka Metropolitan Police (DMP) area. The data were compiled from the Police Reports accident by accident and analyzed using an MS Access based database and additionally an ArcGIS software for the selected variables. All the roadway sections and intersections of Dhaka Metropolitan Police (DMP) were considered in the study. A total number of two thousand seven hundred twenty (2720) accidents that were recorded by police during the period of year 2007-2011 were analyzed. The study revealed that a) number of accidents in Dhaka is reducing by more than ten (10) percent every year b) Sixty three percent (63%) of the accidents took place where there was no traffic control c) Only twenty nine percent (29%) of the accidents took place at intersection areas and T-junctions were found to be the most vulnerable junction type d) Seventy three percent (73%) accidents occurred on ided roadways or where only one-way traffic movement existed e) Ninety seven percent (97%) of the accidents occurred on straight road sections. Improvement of the traffic accident data collection system in Dhaka is necessary. Detailed study on under-reporting of traffic accident is also recommended.
Publisher: Springer Science and Business Media LLC
Date: 05-01-2021
DOI: 10.1007/S10346-020-01606-0
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.
Publisher: Springer Science and Business Media LLC
Date: 03-01-2022
DOI: 10.1007/S10346-021-01810-6
Abstract: The Forcibly Displaced Myanmar Nationals (FDMN), historically known as ‘Rohingya’ who fled the 2017 ethnic atrocities and genocide in the Northern Rakhine State of Myanmar, took shelter in Cox’s Bazar District of Bangladesh. The c network, known as Kutupalong Rohingya C (KRC), is situated in the tectonically active tertiary hilly terrain. The KRC has been experiencing hydrometeorological hazards, where landslides are frequent. This study investigated the slopes’ geological condition, engineering properties and human interventions, which influence the landslides. The exposed slopes were relatively high ( 10 m) and steep ranging from 40° to 60° that have numerous polygonal tension cracks and fissures. From the geological and geotechnical aspects, there are three successive units of slope materials: (1) residual soils of sandy silt with clay, (2) highly weathered silty sandstones and (3) shale/clay with silt and fine sand intercalations at the bottom of the slopes. Field observations revealed that most slope failures occurred in the residual soil and weathered silty sandstone units. The residual soils have a bulk density of 1.49–1.97 g/cm 3 , a liquid limit of 25–48%, a plasticity index of 5–16% and an undrained shear strength of 23–46 kPa. The silty sandstones have a bulk density of 1.44–1.94 g/cm 3 , an internal friction angle of 34°–40° and a cohesion of 0.5–13 kPa. The mineralogical composition determined by the X-ray diffraction shows low clay mineral content, which does not affect landslides. However, the slope geometry, low shear strength with strain softening properties and torrential rainfall accompanied by anthropogenic factors cause numerous landslides every year. This study will help take proper mitigation and preparedness measures for slope protection in the KRC area and surroundings.
Publisher: MDPI AG
Date: 23-02-2012
DOI: 10.3390/IJGI1010003
Publisher: Elsevier BV
Date: 05-2018
Publisher: Emerald
Date: 08-01-2018
DOI: 10.1108/IJCCSM-10-2016-0149
Abstract: “No climate change, no climate refugees”. On the basis of this theme, this paper aims to propose a method for undertaking the responsibility for climate refugees literally uprooted by liable climate polluting countries. It also considers the historical past, culture, geopolitics, imposed wars, economic oppression and fragile governance to understand the holistic scenario of vulnerability to climate change. This paper is organized around three distinct aspects of dealing with extreme climatic events – vulnerability as part of making the preparedness and response process fragile (past), climate change as a hazard driver (present) and rehabilitating the climate refugees (future). Bangladesh is used as an ex le that represents a top victim country to climatic extreme events from many countries with similar baseline characteristics. The top 20 countries accounting for approximately 82 per cent of the total global carbon dioxide (CO 2 ) emissions are considered for model development by analysing the parameters – per capita CO 2 emissions, ecological footprint, gross national income and human development index. Results suggest that under present circumstances, Australia and the USA each should take responsibility of 10 per cent each of the overall global share of climate refugees, followed by Canada and Saudi Arabia (9 per cent each), South Korea (7 per cent) and Russia, Germany and Japan (6 per cent each). As there is no international convention for protecting climate refugees yet, the victims either end up in detention c s or are refused shelter in safer places or countries. There is a dire need to address the climate refugee crisis as these people face greater political risks. This paper provides a critical overview of accommodating the climate refugees (those who have no means for bouncing back) by the liable countries. It proposes an innovative method by considering the status of climate pollution, resource consumption, economy and human development rankings to address the problem by bringing humanitarian justice to the ultimate climate refugees.
Publisher: Elsevier BV
Date: 02-2020
DOI: 10.1016/J.SCITOTENV.2019.135360
Abstract: Bangladesh has a long history of devastating tropical cyclones. In view of the effects of the storms on the country, risk assessment is essential for devising the mitigation strategies at various levels. By way of bringing the conceptual structure of general risk model in practice, this work aims to examine the spatial patterns of cyclone risk in the Cox's Bazar district (I) and Rohingya refugee c s (II) located on the southeastern coast of Bangladesh. We use 14 parameters representing the hazard, exposure, and vulnerability as the components of risk. The selected parameters were analyzed and integrated though the complementary use of Analytic Hierarchy Process (AHP) and Geographic Information System (GIS) for depicting the cyclone risk situation comprehensively at both the spatial scales. The status of the cyclone risk was identified and quantified as very high (6.84%, 3.43%), high (45.78%, 27.82%), moderate (5.97%, 39.42%), low (40.62%, 28.70%), and very low (0.81%, 0.61%) for the spatial scale I and II respectively. In general, northwestern and southern peripheral areas exhibited higher risk than the central and northeastern parts of the Cox's Bazar district and in the refugee settlements, c number 1E, 1W, 7, and 13 revealed relatively higher levels of the risk. The results of the assessment (I) were correlated with experiential damage from the 1991 cyclone a reasonable consistency was noticed between the simulated scenario and the observed impacts. We assume that the deliverables of this spatial analysis could be useful to stakeholders while formulating the cyclone risk mitigation policies for the region. Furthermore, this work demonstrates that the applied method would deliver reliable results if tested in other coastal environments.
Publisher: Elsevier BV
Date: 09-2022
Publisher: MDPI AG
Date: 05-02-2020
DOI: 10.3390/S20030845
Abstract: The monitoring and prediction of the landslide groundwater level is a crucial part of landslide early warning systems. In this study, Tangjiao landslide in the Three Gorges Reservoir area (TGRA) in China was taken as a case study. Three groundwater level monitoring sensors were installed in different locations of the landslide. The monitoring data indicated that the fluctuation of groundwater level is significantly consistent with rainfall and reservoir level in time, but there is a lag. In addition, there is a spatial difference in the impact of reservoir levels on the landslide groundwater level. The data of two monitoring locations were selected for establishing the prediction model of groundwater. Combined with the qualitative and quantitative analysis, the influencing factors were selected, respectively, to establish the hybrid Genetic Algorithm-Support Vector Machine (GA-SVM) prediction model. The single-factor GA-SVM without considering influencing factors and the backpropagation neural network (BPNN) model were adopted to make comparisons. The results showed that the multi-factor GA-SVM performed the best, followed by multi-factor BPNN and single-factor GA-SVM. We found that the prediction accuracy can be improved by considering the influencing factor. The proposed GA-SVM model combines the advantages of each algorithm it can effectively construct the response relationship between groundwater level fluctuations and influencing factors. Above all, the multi-factor GA-SVM is an effective method for the prediction of landslides groundwater in the TGRA.
Publisher: Springer Science and Business Media LLC
Date: 06-11-2014
Publisher: Elsevier BV
Date: 08-2204
Publisher: American Society of Civil Engineers (ASCE)
Date: 08-2018
Publisher: Bangladesh Journals Online (JOL)
Date: 1970
Abstract: Dhaka City has undergone radical changes in its physical form, not only by territorial expansion, but also through internal physical transformations over the last decades. These have created entirely new kinds of fabric. With these changes, the elements of urban form have changed. Plots and open spaces have been transformed into building areas, open squares into car parks, low land and water bodies into reclaimed built-up lands etc. This research has its general interest in the morphologic change of Dhaka City. It focuses on the spatial dynamics of urban growth of Dhaka over the last 55 years from 1952-2007. In the research, the transformation of urban form has been examined through space syntax. The aim behind using this technique is to describe aspects of relationships between the morphological structure of man-made environments and social structures and events. To conduct this research, Wards 49 and 72 of Dhaka City Corporation were selected as the study areas, of which Ward 72 is an indigenous and Ward 49 is a planned type of settlement. Being a planned residential area, the syntactic measures from this morphological analysis are showing quite unchanged and high values in all phases for Ward 49 and the physical characteristics of Ward 72 (Old Dhaka) still represent the past. The syntactic values are found to be higher for Ward 72 and than Ward 49. Higher values indicate that the street network is highly connective among each other. Time affects differently the layout of cities and the architecture of buildings. Of the many human creations, street systems are among the most resistant to change. This has been emphasized in this study, thereby facilitating the comparison of urban layouts across space and time. The interpretation of history in the light of quantitative accounts, as demonstrated in this study, will be of value to urban planners and urban designers for the future planning of modern Dhaka City.DOI: 0.3329/jbip.v2i0.9554 Journal of Bangladesh Institute of Planners Vol. 2, December 2009, pp. 30-38
Publisher: Springer Science and Business Media LLC
Date: 18-04-2023
DOI: 10.1007/S11069-023-05947-6
Abstract: Rainfall-induced landslides seriously threaten hilly environments, leading local authorities to implement various mitigation measures to decrease disaster risk. However, there is a significant gap in the current literature regarding evaluating their effectiveness and the associated community risk perception. To address this gap, we used an interdisciplinary and innovative approach to analyse the slope stability of landslides, evaluate the effectiveness of existing structural mitigation measures, and assess the risk perception of those living in danger zones. Our case study focused on the Kutupalong Rohingya C (KRC) in Cox’s Bazar, Bangladesh, which is home to over one million Rohingya refugees from Myanmar. Although various structural and non-structural countermeasures were implemented in the KRC to mitigate the impact of landslides, many of them failed to prevent landslides from occurring. We utilised a variety of methods from the physical sciences, including the infinite slope, limit equilibrium (LEM), and finite element (FEM) approaches, to calculate the factor of safety (FoS) for specific slopes. Additionally, in the social sciences domain, we conducted a questionnaire survey of approximately 400 Rohingya participants to assess the community’s perception of the interventions and the degree of disaster risk. Our findings indicated that slopes with a gradient greater than 40° were unstable (FoS 1), which was present throughout the entire KRC area. The effectiveness of the LEM and FEM methods was evaluated for four dominant slope angles (40°, 45°, 50°, and 55°) under varying loads (0, 50, and 100 kN/m 2 ). The slopes were found to be stable for lower slope angles but unstable for higher slope angles ( 50°) and increased overburden loads (50–100 kN/m 2 ). Different mitigation measures were tested on the identified unstable slopes to assess their effectiveness, but the results showed that the countermeasures only provided marginal protection against landslides. Survey results revealed that at least 70% of respondents believed that concrete retaining walls are more effective in reducing landslide occurrence compared to other measures. Additionally, about 60% of the respondents questioned the reliability of the existing structural mitigation measures. The study also found that the cohesion and friction angle of lower sandstone and the cohesion of upper soil layers are important factors to consider when designing and implementing slope protection countermeasures in the KRC area.
Publisher: Elsevier BV
Date: 10-2018
Publisher: Informa UK Limited
Date: 2021
Publisher: MDPI AG
Date: 14-10-2020
DOI: 10.3390/RS12203347
Abstract: Despite yielding considerable degrees of accuracy in landslide predictions, the outcomes of different landslide susceptibility models are prone to spatial disagreement and therefore, uncertainties. Uncertainties in the results of various landslide susceptibility models create challenges in selecting the most suitable method to manage this complex natural phenomenon. This study aimed to propose an approach to reduce uncertainties in landslide prediction, diagnosing spatial agreement in machine learning-based landslide susceptibility maps. It first developed landslide susceptibility maps of Cox’s Bazar district of Bangladesh, applying four machine learning algorithms: K-Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP), Random Forest (RF), and Support Vector Machine (SVM), featuring hyperparameter optimization of 12 landslide conditioning factors. The results of all the four models yielded very high prediction accuracy, with the area under the curve (AUC) values range between 0.93 to 0.96. The assessment of spatial agreement of landslide predictions showed that the pixel-wise correlation coefficients of landslide probability between various models range from 0.69 to 0.85, indicating the uncertainty in predicted landslides by various models, despite their considerable prediction accuracy. The uncertainty was addressed by establishing a Logistic Regression (LR) model, incorporating the binary landslide inventory data as the dependent variable and the results of the four landslide susceptibility models as independent variables. The outcomes indicated that the RF model had the highest influence in predicting the observed landslide locations, followed by the MLP, SVM, and KNN models. Finally, a combined landslide susceptibility map was developed by integrating the results of the four machine learning-based landslide predictions. The combined map resulted in better spatial agreement (correlation coefficients range between 0.88 and 0.92) and greater prediction accuracy (0.97) compared to the in idual models. The modelling approach followed in this study would be useful in minimizing uncertainties of various methods and improving landslide predictions.
Publisher: Springer Science and Business Media LLC
Date: 08-05-2018
DOI: 10.1038/S41598-018-25567-6
Abstract: Landslide displacement prediction is considered as an essential component for developing early warning systems. The modelling of conventional forecast methods requires enormous monitoring data that limit its application. To conduct accurate displacement prediction with limited data, a novel method is proposed and applied by integrating three computational intelligence algorithms namely: the wavelet transform (WT), the artificial bees colony (ABC), and the kernel-based extreme learning machine (KELM). At first, the total displacement was decomposed into several sub-sequences with different frequencies using the WT. Next each sub-sequence was predicted separately by the KELM whose parameters were optimized by the ABC. Finally the predicted total displacement was obtained by adding all the predicted sub-sequences. The Shuping landslide in the Three Gorges Reservoir area in China was taken as a case study. The performance of the new method was compared with the WT-ELM, ABC-KELM, ELM, and the support vector machine (SVM) methods. Results show that the prediction accuracy can be improved by decomposing the total displacement into sub-sequences with various frequencies and by predicting them separately. The ABC-KELM algorithm shows the highest prediction capacity followed by the ELM and SVM. Overall, the proposed method achieved excellent performance both in terms of accuracy and stability.
Publisher: MDPI AG
Date: 15-08-2016
DOI: 10.3390/SU8080805
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-1027
Abstract: Rainfall-induced landslides are a common natural hazard in hilly/mountainous regions worldwide. Almost every year, catastrophic landslides cause severe loss and damage to human lives, livelihoods, and critical infrastructure during the monsoon season in Bangladesh. Yet, Bangladesh has no operational landslide early warning systems (LEWS), and most risk mitigation works still focus on community-based preparedness and adaptation activities that are insufficient to confront the devastating nature of the hazard. Over the past ten years, we tried to develop and introduce a scientifically valid, automated and dynamic landslide forecasting system in Bangladesh, which is the fundamental and most critical component for building community trust and introducing a sustainable LEWS. However, several overarching challenges were identified, including a lack of real-time, continuous and historical data collection practice for landslide inventory and precipitation, institutional and stakeholders coordination, skilled human resources and funding and the absence of policy-driven research and application of cutting-edge technologies such as drones, citizen science/apps, automated weather stations/rain gauges, and slope displacement monitoring sensors quality, reliability and spatial and temporal representativeness of data and quantifying uncertainties and regularly evaluating the different aspects (verification, calibration and prototypes) of the forecasting framework. In addition, most people involved in developing LEWSs fail to blend interdisciplinary and multidisciplinary approaches in a multi-hazard (earthquakes, flash floods) and cascading disaster setting, address site-specific unique environments/needs and capture dynamic social vulnerability magnitudes, for ex le & #8211 conflict and humanitarian crisis hill cutting, unplanned urbanisation and deforestation and governance and cultural features. It is highly recommended to develop a novel state-of-the-art to tackle such underlying drawbacks in forecasting mechanisms before institutionalising a reliable, people-centred, end-to-end, and effective LEWS in a developing country context.& &
Publisher: MDPI AG
Date: 16-10-2020
DOI: 10.3390/RS12203385
Abstract: Landslides are a common natural hazard that causes casualties and unprecedented economic losses every year, especially in vulnerable developing countries. Considering the high cost of in-situ monitoring equipment and the sparse coverage of monitoring points, the Sentinel-1 images and Interferometric Synthetic Aperture Radar (InSAR) technique were used to conduct landslide monitoring and analysis. The Muyubao landslide in the Three Gorges Reservoir area in China was taken as a case study. A total of 37 images from March 2016 to September 2017 were collected, and the displacement time series were extracted using the Stanford Method for Persistent Scatterer (StaMPS) small baselines subset method. The comparison to global positioning system monitoring results indicated that the InSAR processing of the Muyubao landslide was accurate and reliable. Combined with the field investigation, the deformation evolution and its response to triggering factors were analyzed. During this monitoring period, the creeping process of the Muyubao landslide showed obvious spatiotemporal deformation differences. The changes in the reservoir water level were the trigger of the Muyubao landslide, and its deformation mainly occurred during the fluctuation period and high-water level period of the reservoir.
Publisher: MDPI AG
Date: 08-10-2024
DOI: 10.3390/RS12203363
Abstract: Agriculture is one of the fundamental economic activities in most countries however, this sector suffers from various natural hazards including flood and drought. The determination of drought-prone areas is essential to select drought-tolerant crops in climate sensitive vulnerable areas. This study aims to enhance the detection of agricultural areas with vulnerability to drought conditions in a heterogeneous environment, taking Bangladesh as a case study. The normalized difference vegetation index (NDVI) and land cover products from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images have been incorporated to compute the vegetation index. In this study, a modified vegetation condition index (mVCI) is proposed to enhance the estimation of agricultural drought. The NDVI values ranging between 0.44 to 0.66 for croplands are utilized for the mVCI. The outcomes of the mVCI are compared with the traditional vegetation condition index (VCI). Precipitation and crop yield data are used for the evaluation. The mVCI maps from multiple years (2006–2018) have been produced to compute the drought hazard index (DHI) using a weighted sum overlay method. The results show that the proposed mVCI enhances the detection of agricultural drought compared to the traditional VCI in a heterogeneous environment. The “Aus” rice-growing season (sown in mid-March to mid-April and harvested in mid-July to early August) receives the highest average precipitation ( mm), and thereby this season is less vulnerable to drought. A comparison of crop yields reveals the lowest productivity in the drought year (2006) compared to the non-drought year (2018), and the DHI map presents that the north-west region of Bangladesh is highly vulnerable to agricultural drought. This study has undertaken a large-scale analysis that is important to prioritize agricultural zones and initiate development projects based on the associated level of vulnerability.
Publisher: Emerald
Date: 06-2018
Abstract: The purpose of this paper is to connect the theoretical idea of warning systems as social processes with empirical data of people’s perceptions of and actions for warning for cyclones in Bangladesh. A case study approach is used in two villages of Khulna district in southwest Bangladesh: Kalabogi and Kamarkhola. In total, 60 households in each village were surveyed with structured questionnaires regarding how they receive their cyclone warning information as well as their experiences of warnings for Cyclone Sidr in 2007 and Cyclone Aila in 2009. People in the two villages had a high rate of receiving cyclone warnings and accepted them as being credible. They also experienced high impacts from the cyclones. Yet evacuation rates to cyclone shelters were low. They did not believe that significant cyclone damage would affect them and they also highlighted the difficulty of getting to cyclone shelters due to poor roads, leading them to prefer other evacuation options which were implemented if needed. Theoretical constructs of warning systems, such as the First Mile and late warning, are rarely examined empirically according to people’s perceptions of warnings. The case study villages have not before been researched with respect to warning systems. The findings provide empirical evidence for long-established principles of warning systems as social processes, usually involving but not relying on technical components.
Publisher: Informa UK Limited
Date: 25-05-2022
Publisher: MDPI AG
Date: 19-12-2018
DOI: 10.3390/IJGI7120485
Abstract: This article aims to develop a Web-GIS based landslide early warning system (EWS) for the Chittagong Metropolitan Area (CMA), Bangladesh, where, in recent years, rainfall-induced landslides have caused great losses of lives and property. A method for combining static landslide susceptibility maps and rainfall thresholds is proposed by introducing a purposely-build hazard matrix. To begin with, eleven factor maps: soil permeability surface geology landcover altitude slope aspect distance to stream fault line hill cut road cut and drainage network along with a detailed landslide inventory map were produced. These maps were used, and four methods were applied: artificial neural network (ANN) multiple regressions principal component analysis and support vector machine to produce landslide susceptibility maps. After model validation, the ANN map was found best fitting and was classified into never warning, low, medium, and high susceptibility zones. Rainfall threshold analysis (1960–2017) revealed consecutive 5-day periods of rainfall of 71–282 mm could initiate landslides in CMA. Later, the threshold was classified into three rainfall rates: low rainfall (70–160 mm), medium rainfall (161–250 mm), and high rainfall ( mm). Each landslide was associated with a hazard class (no warning vs. warning state) based on the assumption that the higher the susceptibility, the lower the rainfall. Finally, the EWS was developed using various libraries and frameworks that is connected with a reliable online-based weather application programming interface. The system is publicly available, dynamic, and replicable to similar contexts and is able to disseminate alerts five days in advance via email notifications. The proposed EWS is novel and the first of its kind in Bangladesh, and can be applied to mitigate landslide disaster risks.
Publisher: MDPI AG
Date: 23-03-2017
DOI: 10.3390/RS9040304
Publisher: Springer Science and Business Media LLC
Date: 17-05-2021
DOI: 10.1007/S11069-021-04789-4
Abstract: Disaster risk perception and risk appraisal are essential in formulating an appropriate disaster risk reduction policy. This study examines the actual vs perceived drought risks by constructing risk indices at the household and expert levels using survey data from the lower Teesta River Basin in northern Bangladesh. The survey data were collected from 450 farmers using a structured questionnaire conducted between August and September 2019. A composite drought risk index was developed to understand households’ perceived and actual risks in the designated areas. The results show that the actual and perceived risk values differ significantly among the three case study sites locally known as Ganai, Ismail, and Par Sekh Sundar. The risk levels also differ significantly across the households’ gender, income, occupation, and educational attainment. People with insolvent socioeconomic status are more prone to drought risk compared to others. Results also reveal that the mean level of perceived risk agrees well with the actual risk, whereas females perceive comparatively higher risk than their male counterparts. Expert views on drought risk are similar to the in idual household level perceived risk. The outcomes of this study would assist the policymakers and disaster managers to understand the concrete risk scenarios and take timely disaster risk reduction actions for ensuring a drought-resistant society.
Publisher: MDPI AG
Date: 26-06-2013
DOI: 10.3390/IJGI2030577
Publisher: Springer Science and Business Media LLC
Date: 22-03-190728634
Publisher: Elsevier BV
Date: 03-2019
Publisher: Penerbit UTM Press
Date: 15-10-2001
DOI: 10.11113/JT.V65.2147
Abstract: Bangladesh has one of the highest fatality rates in road accidents and to address the safety problem is a serious concern. Dhaka is the most vulnerable city of the country. Bangladesh Road Transport Authority maintains a database of accidents using outdated software that lacks in geo-referencing facility. This makes the analysis of accident locations a challenging task. The area for this study was the Dhaka Metropolitan Police area where the concerned forty one police stations are responsible for collecting traffic accident data. The Highway Safety Manual identifies the “Network Screening” as the first step of the Roadway Safety Management Process. This study focuses on locating the accidents on urban roadways in Dhaka and identifies thirty corridors and ranks them using geo-referenced data through developing and using a GIS database. Dhaka-Mymensing Road was found to be the most vulnerable road corridor followed by Airport Road and Mirpur Road respectively. The study recommended special attention and special “Diagnostic” studies as explained in the Highway Safety Manual for the high-risk corridors and to put emphasis on the accident data collection and reporting system. Adoption of modern technologies like GPS and GIS in collecting and reporting of the traffic accident data was emphasized.
Publisher: Informa UK Limited
Date: 10-2021
Publisher: MDPI AG
Date: 09-02-2017
Publisher: Elsevier BV
Date: 12-2018
Publisher: Informa UK Limited
Date: 2020
Publisher: Elsevier BV
Date: 02-2021
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: Bangladesh
Location: United Kingdom of Great Britain and Northern Ireland
Location: Bangladesh
Start Date: 2013
End Date: 2016
Funder: Commonwealth Scholarship Commission
View Funded ActivityStart Date: 2019
End Date: 2023
Funder: Royal Society
View Funded ActivityStart Date: 2018
End Date: 2021
Funder: British Academy
View Funded ActivityStart Date: 2018
End Date: 2019
Funder: British Academy
View Funded ActivityStart Date: 2018
End Date: 2019
Funder: Newton Fund
View Funded ActivityStart Date: 2017
End Date: 2017
Funder: International Center for Collaborative Research on Disaster Risk Reduction (ICCR-DRR)
View Funded ActivityStart Date: 2009
End Date: 2011
Funder: European Commission
View Funded Activity