PASSENGER TRAVEL BEHAVIOR BEFORE &amp; DURING THE COVID-19 OUTBREAK: A COMPARATIVE ANALYSIS

Mahi, Mahdi Mansur; Sharif, Md. Shahriar; Rudra, Rhyme Rubayet

doi:10.53808/kus.2022.icstem4ir.0024-se

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…Bangladesh is one of the most over populated countries in the world facing a lot of problems like climate change [ 10 , [45] , [46] , [47] , [48] , [49] ], deforestation [ 50 , 51 ], unplanned urbanization [ 50 , 52 ], pollution [ 34 , 38 , [53] , [54] , [55] ], manmade and natural disasters [ 15 , 56 ]. Bangladesh's coastline region has a more diversified and dynamic physical landscape than is often recognized.…”

Section: Introductionmentioning

confidence: 99%

Cyclone vulnerability assessment in the coastal districts of Bangladesh

Sarkar,

Rudra,

Santo

2024

Heliyon

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Section: Introductionmentioning

confidence: 99%

Cyclone vulnerability assessment in the coastal districts of Bangladesh

Sarkar,

Rudra,

Santo

2024

Heliyon

View full text Add to dashboard Cite

Future groundwater potential mapping using machine learning algorithms and climate change scenarios in Bangladesh

Sarkar,

Rudra,

Talukdar

et al. 2024

Sci Rep

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The aim of the study was to estimate future groundwater potential zones based on machine learning algorithms and climate change scenarios. Fourteen parameters (i.e., curvature, drainage density, slope, roughness, rainfall, temperature, relative humidity, lineament density, land use and land cover, general soil types, geology, geomorphology, topographic position index (TPI), topographic wetness index (TWI)) were used in developing machine learning algorithms. Three machine learning algorithms (i.e., artificial neural network (ANN), logistic model tree (LMT), and logistic regression (LR)) were applied to identify groundwater potential zones. The best-fit model was selected based on the ROC curve. Representative concentration pathways (RCP) of 2.5, 4.5, 6.0, and 8.5 climate scenarios of precipitation were used for modeling future climate change. Finally, future groundwater potential zones were identified for 2025, 2030, 2035, and 2040 based on the best machine learning model and future RCP models. According to findings, ANN shows better accuracy than the other two models (AUC: 0.875). The ANN model predicted that 23.10 percent of the land was in very high groundwater potential zones, whereas 33.50 percent was in extremely high groundwater potential zones. The study forecasts precipitation values under different climate change scenarios (RCP2.6, RCP4.5, RCP6, and RCP8.5) for 2025, 2030, 2035, and 2040 using an ANN model and shows spatial distribution maps for each scenario. Finally, sixteen scenarios were generated for future groundwater potential zones. Government officials may utilize the study’s results to inform evidence-based choices on water management and planning at the national level.

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Delineating the drought vulnerability zones in Bangladesh

Sarkar,

Das,

Rudra

et al. 2024

Sci Rep

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The research aims to explore the vulnerability of Bangladesh to drought by considering a comprehensive set of twenty-four factors, classified into four major categories: meteorological, hydrological, agricultural, and socioeconomic vulnerability. To achieve this, the study utilized a knowledge-based multi-criteria method known as the Analytic Hierarchy Process (AHP) to delineate drought vulnerability zones across the country. Weight estimation was accomplished by creating pairwise comparison matrices for factors and different types of droughts, drawing on relevant literature, field experience, and expert opinions. Additionally, online-based interviews and group discussions were conducted with 30 national and foreign professionals, researchers, and academics specializing in drought-related issues in Bangladesh. Results from overall drought vulnerability map shows that the eastern hills region displays a notably high vulnerability rate of 56.85% and an extreme low vulnerability rate of 0.03%. The north central region shows substantial vulnerability at high levels (35.85%), while the north east exhibits a significant proportion (41.68%) classified as low vulnerability. The north west region stands out with a vulnerability rate of 40.39%, emphasizing its importance for drought management strategies. The River and Estuary region displays a modest vulnerability percentage (38.44%), suggesting a balanced susceptibility distribution. The south central and south east regions show significant vulnerabilities (18.99% and 39.60%, respectively), while the south west region exhibits notable vulnerability of 41.06%. The resulting model achieved an acceptable level of performance, as indicated by an area under the curve value of 0.819. Policymakers and administrators equipped with a comprehensive vulnerability map can utilize it to develop and implement effective drought mitigation strategies, thereby minimizing the losses associated with drought.

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PASSENGER TRAVEL BEHAVIOR BEFORE & DURING THE COVID-19 OUTBREAK: A COMPARATIVE ANALYSIS

Cited by 3 publications

References 24 publications

Cyclone vulnerability assessment in the coastal districts of Bangladesh

Cyclone vulnerability assessment in the coastal districts of Bangladesh

Future groundwater potential mapping using machine learning algorithms and climate change scenarios in Bangladesh

Delineating the drought vulnerability zones in Bangladesh

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