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Article: Artificial neural network for flood susceptibility mapping in Bangladesh.

Rudra, Rhyme Rubayet / Sarkar, Showmitra Kumar

2023 Volume 9, Issue 6, Page(s) e16459

Abstract: The objective of the research is to investigate flood susceptibility in the Sylhet division of Bangladesh. Eight influential factors (i.e., elevation, slope, aspect, curvature, TWI, SPI, roughness, and LULC) were applied as inputs to the model. In this ... ...

Abstract	The objective of the research is to investigate flood susceptibility in the Sylhet division of Bangladesh. Eight influential factors (i.e., elevation, slope, aspect, curvature, TWI, SPI, roughness, and LULC) were applied as inputs to the model. In this work, 1280 samples were taken at different locations based on flood and non-flood characteristics; of these, 75% of the inventory dataset was used for training and 25% for testing. An artificial neural network was applied to develop a flood susceptibility model, and the results were plotted on a map using ArcGIS. According to the finding, 40.98% (i.e., 499433.50 hectors) of the study area is found within the very high-susceptibility zone, and 37.43% (i.e., 456168.76 hectors) are in the highly susceptible zone. Only 6.52% and 15% of the area were found in low and medium flood susceptibility zones, respectively. The results of model validation show that the overall prediction rate is around 89% and the overall model success rate is around 98%. The study's findings assist policymakers and concerned authorities in making flood risk management decisions in order to mitigate the negative impacts.
Language	English
Publishing date	2023-05-23
Publishing country	England
Document type	Journal Article
ZDB-ID	2835763-2
ISSN	2405-8440
ISSN	2405-8440
DOI	10.1016/j.heliyon.2023.e16459
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Spatio-temporal variability of vegetation and its relation to different hydroclimatic factors in Bangladesh.

Das, Swadhin / Sarkar, Showmitra Kumar

Heliyon

2023 Volume 9, Issue 8, Page(s) e18412

Abstract: Bangladesh, known for its remarkable ecological diversity, is faced with the pressing challenges of contemporary climate change. It is crucial to understand how vegetation dynamics respond to different climatic factors. Hence, this study aimed to ... ...

Abstract	Bangladesh, known for its remarkable ecological diversity, is faced with the pressing challenges of contemporary climate change. It is crucial to understand how vegetation dynamics respond to different climatic factors. Hence, this study aimed to investigate the spatio-temporal variations of vegetation and their interconnectedness with a range of hydroclimatic factors. The majority of the dataset used in this study relies on MODIS satellite imagery. The Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), precipitation (PPT), evapotranspiration (ET), and land surface temperature (LST) data from the years 2001 to 2020 have been obtained from Google Earth Engine (GEE). In this study, the temporal variations of the NDVI, EVI, PPT, ET, and LST have been investigated. The findings of the Mann-Kendall trend test indicate noticeable trends in both the NDVI and the EVI. Sen's slope value for NDVI and EVI is 0.00424/year and 0.00256/year, respectively. Compared to NDVI, EVI has shown a stronger connection with hydroclimatic factors. In particular, EVI exhibits a better relationship with ET, as indicated by a r
Language	English
Publishing date	2023-07-19
Publishing country	England
Document type	Journal Article
ZDB-ID	2835763-2
ISSN	2405-8440
ISSN	2405-8440
DOI	10.1016/j.heliyon.2023.e18412
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: COVID-19 Susceptibility Mapping Using Multicriteria Evaluation.

Sarkar, Showmitra Kumar

Disaster medicine and public health preparedness

2020 Volume 14, Issue 4, Page(s) 521–537

Abstract: Objective: The purpose of this research was to investigate coronavirus disease (COVID-19) susceptibility in districts of Bangladesh using multicriteria evaluation techniques.Methods: Secondary data were collected from different government organizations, ... ...

Abstract	Objective: The purpose of this research was to investigate coronavirus disease (COVID-19) susceptibility in districts of Bangladesh using multicriteria evaluation techniques.Methods: Secondary data were collected from different government organizations, 120 primary surveys were conducted for calculating weights, and results were validated through 12 key people's interviews. Pairwise comparison matrixes were calculated for 9 factors and subfactors. The analytic hierarchy process used for calculating the susceptibility index and map was prepared based on the results.Results: According to the results, multiple causal factors might be responsible for COVID-19 spreading in Bangladesh. Dhaka might be vulnerable to COVID-19 due to a higher population, population density, and international collaboration. According to the pairwise comparison matrix, the consistency ratio for subfactors and factors was in the permissible limit (ie, less than 0.10). The highest factor weight of 0.2907 was found for the factors type of port. The maximum value for the susceptibility index was 0.435219362 for Chittagong, and the minimum value was 0.076174 for Naogaon.Conclusions: The findings of this research might help the communities and government agencies with effective decision-making.
MeSH term(s)	Bangladesh/epidemiology ; COVID-19/epidemiology ; COVID-19/transmission ; Decision Support Techniques ; Disease Susceptibility/diagnosis ; Disease Susceptibility/epidemiology ; Geographic Mapping ; Humans ; Surveys and Questionnaires
Keywords	covid19
Language	English
Publishing date	2020-06-25
Publishing country	United States
Document type	Journal Article
ZDB-ID	2375268-3
ISSN	1938-744X ; 1935-7893
ISSN (online)	1938-744X
ISSN	1935-7893
DOI	10.1017/dmp.2020.175
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Cyclone vulnerability assessment in the coastal districts of Bangladesh

Showmitra Kumar Sarkar / Rhyme Rubayet Rudra / Md. Mehedi Hasan Santo

Heliyon, Vol 10, Iss 1, Pp e23555- (2024)

1481

Abstract: This research aims to assess the vulnerability to cyclones in the coastal regions of Bangladesh, employing a comprehensive framework derived from the Intergovernmental Panel on Climate Change (IPCC, 2007). The study considers a total of eighteen factors, ...

Abstract	This research aims to assess the vulnerability to cyclones in the coastal regions of Bangladesh, employing a comprehensive framework derived from the Intergovernmental Panel on Climate Change (IPCC, 2007). The study considers a total of eighteen factors, categorized into three critical dimensions: exposure, sensitivity, and adaptive capacity. These factors are crucial in understanding the potential impact of cyclones in the region. In order to develop a cyclone vulnerability map, Principal Component Analysis (PCA) was applied, primarily focusing on the dimensions of sensitivity and adaptive capacity. The findings of this analysis revealed that sensitivity and adaptive capacity components accounted for a significant percentage of variance in the data, explaining 90.00 % and 90.93 % of the variance, respectively. Despite the lack of details about data collection, the study identified specific factors contributing significantly to each dimension. Notably, proximity to the coastline emerged as a highly influential factor in determining cyclone exposure. The results of this research indicate that certain areas, such as Jessore, Khulna, Narail, Gopalgonj, and Bagerhat, exhibit low exposure to cyclones, whereas regions like Chandpur and Lakshmipur face a high level of exposure. Sensitivity was found to be high in most areas, with Noakhali, Lakshmipur, and Chandpur being the most sensitive regions. Adaptive capacity was observed to vary significantly, with low values near the sea, particularly in locations like Cox's Bazar, Shatkhira, Bagerhat, Noakhali, and Bhola, and high values in regions farther from the coast. Overall, vulnerability to cyclones was found to be very high in Noakhali, Lakshmipur, Chandpur, and Bhola, low in Jessore and Khulna, and moderate in Barisal, Narail, Gopalgonj, and Jhalokati. These findings are expected to provide valuable insights to inform decision-makers and authorities tasked with managing the consequences of cyclones in the region.
Keywords	Disaster risk ; Emergency response ; GIS ; Coastal communities ; Disaster preparedness ; Science (General) ; Q1-390 ; Social sciences (General) ; H1-99
Subject code	910
Language	English
Publishing date	2024-01-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Soil erosion susceptibility mapping in Bangladesh

Halima Sadia / Showmitra Kumar Sarkar / Mafrid Haydar

Ecological Indicators, Vol 156, Iss , Pp 111182- (2023)

2023

Abstract: This study aims to draw a scientific framework for plotting soil erosion susceptibility in the Chittagong Hill Tracts of Bangladesh by comparing existing approaches. Data-driven machine learning techniques (including Classification and Regression Tree ( ... ...

Abstract	This study aims to draw a scientific framework for plotting soil erosion susceptibility in the Chittagong Hill Tracts of Bangladesh by comparing existing approaches. Data-driven machine learning techniques (including Classification and Regression Tree (CART), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Random Forest (RF)) and a knowledge-based approach (AHP) are used in this study to pinpoint areas of Chittagong that are particularly susceptible to soil erosion while taking into account 18 soil erosion-regulating parameters. Furthermore, the effectiveness of the selected data-driven machine learning models and knowledge-based models was assessed by utilizing soil erosion and non-erosion sites. When evaluating the fidelity of each model using the ROC and AUC, the RF model was shown to be the most accurate and predictive. There is no poor performer among these models; all have AUCs greater than 67 % (RF = 0.86, ANN = 0.73, SVM = 0.67, CART = 0.67, and AHP = 0.82). According to the findings of the Random Forest model, approximately 71.55 percent of the area exhibited a moderate level of susceptibility to soil erosion. In relation to the land area, the high and low zones accounted for 16.91 percent and 11.54 percent, respectively. The specific area shares of 2256.25, 9548.08, and 1539.67 square kilometers were attributed to the high, moderate, and low danger zones, respectively. The best models' results after comparing models of data-driven and knowledge-based approaches can help to estimate soil erosion risk zones and provide insight into establishing appropriate policies to minimize this issue. In addition, the methods used in this research might be applicable to assessing the vulnerability and risk of soil erosion events in other areas. As they begin long-term planning to reduce soil erosion, local authorities and policymakers will find the study's results on practical policies and management options quite helpful.
Keywords	Data driven approach ; Knowledge based approach ; Machine learning ; Remote sensing ; Soil erosion ; Ecology ; QH540-549.5
Subject code	669
Language	English
Publishing date	2023-12-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Navigating nature’s toll

Showmitra Kumar Sarkar / Mustafa Saroar / Tanmoy Chakraborty

Heliyon, Vol 9, Iss 7, Pp e18255- (2023)

Assessing the ecological impact of the refugee crisis in Cox’s Bazar, Bangladesh

2023

Abstract: The Rohingya crisis in Myanmar’s Rakhine state has resulted in a significant influx of refugees into Cox’s Bazar, Bangladesh. However, the ecological impact of this migration has received limited attention in research. This study aimed to address this ... ...

Abstract	The Rohingya crisis in Myanmar’s Rakhine state has resulted in a significant influx of refugees into Cox’s Bazar, Bangladesh. However, the ecological impact of this migration has received limited attention in research. This study aimed to address this gap by utilizing remote sensing data and machine learning techniques to model the ecological quality (EQ) of the region before and after the refugee influx. To quantify changes in land use and land cover (LULC), three supervised machine learning classification methods, namely artificial neural networks (ANN), support vector machines (SVM), and random forests (RF), were applied. The most accurate LULC maps obtained from these methods were then used to assess changes in ecosystem service valuation and function resulting from the land use changes. Furthermore, fuzzy logic models were employed to examine the EQ conditions before and after the Rohingya influx. The findings of the study indicate that the increased number of Rohingya refugees has led to a 9.58% decrease in forest area, accompanied by an 8.25% increase in settlement areas. The estimated total ecosystem services value (ESV) in the research area was $67.83 million in 2017 and $67.78 million in 2021, respectively. The ESV for forests experienced a significant decline of 21.97%, equivalent to a decrease of $5.33 million. Additionally, the reduction in forest lands has contributed to a 13.58% decline in raw materials and a 14.57% decline in biodiversity. Furthermore, utilizing a Markovian transition probability model, our analysis reveals that the EQ conditions in the area have deteriorated from “very good” or “good” to “bad” or “very bad” following the Rohingya influx. The findings of this study emphasize the importance of integrating ecological considerations into decision-making processes and developing proactive measures to mitigate the environmental impact of such large-scale migrations.
Keywords	Ecosystem ; Machine learning ; LULC ; Ecology ; Rohingya refugees ; Science (General) ; Q1-390 ; Social sciences (General) ; H1-99
Subject code	910
Language	English
Publishing date	2023-07-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article: Cyclone vulnerability assessment in the coastal districts of Bangladesh.

Sarkar, Showmitra Kumar / Rudra, Rhyme Rubayet / Santo, Md Mehedi Hasan

Heliyon

2023 Volume 10, Issue 1, Page(s) e23555

Abstract	This research aims to assess the vulnerability to cyclones in the coastal regions of Bangladesh, employing a comprehensive framework derived from the Intergovernmental Panel on Climate Change (IPCC, 2007). The study considers a total of eighteen factors, categorized into three critical dimensions: exposure, sensitivity, and adaptive capacity. These factors are crucial in understanding the potential impact of cyclones in the region. In order to develop a cyclone vulnerability map, Principal Component Analysis (PCA) was applied, primarily focusing on the dimensions of sensitivity and adaptive capacity. The findings of this analysis revealed that sensitivity and adaptive capacity components accounted for a significant percentage of variance in the data, explaining 90.00 % and 90.93 % of the variance, respectively. Despite the lack of details about data collection, the study identified specific factors contributing significantly to each dimension. Notably, proximity to the coastline emerged as a highly influential factor in determining cyclone exposure. The results of this research indicate that certain areas, such as Jessore, Khulna, Narail, Gopalgonj, and Bagerhat, exhibit low exposure to cyclones, whereas regions like Chandpur and Lakshmipur face a high level of exposure. Sensitivity was found to be high in most areas, with Noakhali, Lakshmipur, and Chandpur being the most sensitive regions. Adaptive capacity was observed to vary significantly, with low values near the sea, particularly in locations like Cox's Bazar, Shatkhira, Bagerhat, Noakhali, and Bhola, and high values in regions farther from the coast. Overall, vulnerability to cyclones was found to be very high in Noakhali, Lakshmipur, Chandpur, and Bhola, low in Jessore and Khulna, and moderate in Barisal, Narail, Gopalgonj, and Jhalokati. These findings are expected to provide valuable insights to inform decision-makers and authorities tasked with managing the consequences of cyclones in the region.
Language	English
Publishing date	2023-12-14
Publishing country	England
Document type	Journal Article
ZDB-ID	2835763-2
ISSN	2405-8440
ISSN	2405-8440
DOI	10.1016/j.heliyon.2023.e23555
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Navigating nature's toll: Assessing the ecological impact of the refugee crisis in Cox's Bazar, Bangladesh.

Sarkar, Showmitra Kumar / Saroar, Mustafa / Chakraborty, Tanmoy

Heliyon

2023 Volume 9, Issue 7, Page(s) e18255

Abstract: The Rohingya crisis in Myanmar's Rakhine state has resulted in a significant influx of refugees into Cox's Bazar, Bangladesh. However, the ecological impact of this migration has received limited attention in research. This study aimed to address this ... ...

Abstract	The Rohingya crisis in Myanmar's Rakhine state has resulted in a significant influx of refugees into Cox's Bazar, Bangladesh. However, the ecological impact of this migration has received limited attention in research. This study aimed to address this gap by utilizing remote sensing data and machine learning techniques to model the ecological quality (EQ) of the region before and after the refugee influx. To quantify changes in land use and land cover (LULC), three supervised machine learning classification methods, namely artificial neural networks (ANN), support vector machines (SVM), and random forests (RF), were applied. The most accurate LULC maps obtained from these methods were then used to assess changes in ecosystem service valuation and function resulting from the land use changes. Furthermore, fuzzy logic models were employed to examine the EQ conditions before and after the Rohingya influx. The findings of the study indicate that the increased number of Rohingya refugees has led to a 9.58% decrease in forest area, accompanied by an 8.25% increase in settlement areas. The estimated total ecosystem services value (ESV) in the research area was $67.83 million in 2017 and $67.78 million in 2021, respectively. The ESV for forests experienced a significant decline of 21.97%, equivalent to a decrease of $5.33 million. Additionally, the reduction in forest lands has contributed to a 13.58% decline in raw materials and a 14.57% decline in biodiversity. Furthermore, utilizing a Markovian transition probability model, our analysis reveals that the EQ conditions in the area have deteriorated from "very good" or "good" to "bad" or "very bad" following the Rohingya influx. The findings of this study emphasize the importance of integrating ecological considerations into decision-making processes and developing proactive measures to mitigate the environmental impact of such large-scale migrations.
Language	English
Publishing date	2023-07-13
Publishing country	England
Document type	Journal Article
ZDB-ID	2835763-2
ISSN	2405-8440
ISSN	2405-8440
DOI	10.1016/j.heliyon.2023.e18255
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Spatial priority for COVID-19 vaccine rollout against limited supply.

Sarkar, Showmitra Kumar / Morshed, Md Manjur

Heliyon

2021 Volume 7, Issue 11, Page(s) e08419

Abstract: The COVID-19 vaccines are limited in supply which requires vaccination by priority. This study proposes a spatial priority-based vaccine rollout strategy for Bangladesh. Demographic, economic and vulnerability, and spatial connectivity - these four types ...

Abstract	The COVID-19 vaccines are limited in supply which requires vaccination by priority. This study proposes a spatial priority-based vaccine rollout strategy for Bangladesh. Demographic, economic and vulnerability, and spatial connectivity - these four types of factors are considered for identifying the spatial priority. The spatial priority is calculated and mapped using a GIS-based analytic hierarchy process. Our findings suggest that both demographic and economic factors are keys to the spatial priority of vaccine rollout. Secondly, spatial connectivity is an essential component for defining spatial priority due to the transmissibility of COVID-19. A total of 12 out of 64 districts were found high-priority followed by 22 medium-priorities for vaccine rollout. The proposed strategy by no means suggests ending mass vaccination by descending age groups but an alternative against limited vaccine supply. The spatial priority of the vaccine rollout strategy proposed in this study might help to curb down COVID-19 transmission and to keep the economy moving. The inclusion of granular data and contextual factors can significantly improve the spatial priority identification which can have wider applications for other infectious and transmittable diseases and beyond.
Language	English
Publishing date	2021-11-17
Publishing country	England
Document type	Journal Article
ZDB-ID	2835763-2
ISSN	2405-8440
ISSN	2405-8440
DOI	10.1016/j.heliyon.2021.e08419
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Artificial neural network for flood susceptibility mapping in Bangladesh

Rudra, Rhyme Rubayet / Sarkar, Showmitra Kumar

Heliyon. 2023 June, v. 9, no. 6 p.e16459-

2023

Abstract	The objective of the research is to investigate flood susceptibility in the Sylhet division of Bangladesh. Eight influential factors (i.e., elevation, slope, aspect, curvature, TWI, SPI, roughness, and LULC) were applied as inputs to the model. In this work, 1280 samples were taken at different locations based on flood and non-flood characteristics; of these, 75% of the inventory dataset was used for training and 25% for testing. An artificial neural network was applied to develop a flood susceptibility model, and the results were plotted on a map using ArcGIS. According to the finding, 40.98% (i.e., 499433.50 hectors) of the study area is found within the very high-susceptibility zone, and 37.43% (i.e., 456168.76 hectors) are in the highly susceptible zone. Only 6.52% and 15% of the area were found in low and medium flood susceptibility zones, respectively. The results of model validation show that the overall prediction rate is around 89% and the overall model success rate is around 98%. The study's findings assist policymakers and concerned authorities in making flood risk management decisions in order to mitigate the negative impacts.
Keywords	data collection ; inventories ; model validation ; neural networks ; prediction ; risk management ; roughness ; Bangladesh ; Flood ; Machine learning ; Geographic information system ; Remote sensing
Language	English
Dates of publication	2023-06
Publishing place	Elsevier Ltd
Document type	Article ; Online
Note	Use and reproduction
ZDB-ID	2835763-2
ISSN	2405-8440
ISSN	2405-8440
DOI	10.1016/j.heliyon.2023.e16459
Database	NAL-Catalogue (AGRICOLA)

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