Extreme weather events (EWEs)-Related health complications in Bangladesh: A gender-based analysis on the 2017 catastrophic floods

Jerin, Tasnim; Chowdhury, Md. Arif; Azad, M. Abul Kalam; Zaman, Sabrina; Mahmood, Swarnali; Islam, Syed Labib Ul; Mohammad Jobayer, Hossain

doi:10.1016/j.nhres.2023.10.006

Cited by 3 publications

(2 citation statements)

References 64 publications

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“…Repetitive floods in the Ganges River Delta negatively impact social-natural systems. They cause losses of lives and damage to infrastructure [63], as well as fragmentation and disruption in natural landscapes [64] and increased salinity due to the intrusion of oceanic water during storms. Moreover, floods affect crop agriculture [65,66] and negatively influence the sustainability of coastal ecosystems [67][68][69].…”

Section: Problem Formulationmentioning

confidence: 99%

Deep Learning Methods of Satellite Image Processing for Monitoring of Flood Dynamics in the Ganges Delta, Bangladesh

Lemenkova

2024

Water

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Mapping spatial data is essential for the monitoring of flooded areas, prognosis of hazards and prevention of flood risks. The Ganges River Delta, Bangladesh, is the world’s largest river delta and is prone to floods that impact social–natural systems through losses of lives and damage to infrastructure and landscapes. Millions of people living in this region are vulnerable to repetitive floods due to exposure, high susceptibility and low resilience. Cumulative effects of the monsoon climate, repetitive rainfall, tropical cyclones and the hydrogeologic setting of the Ganges River Delta increase probability of floods. While engineering methods of flood mitigation include practical solutions (technical construction of dams, bridges and hydraulic drains), regulation of traffic and land planning support systems, geoinformation methods rely on the modelling of remote sensing (RS) data to evaluate the dynamics of flood hazards. Geoinformation is indispensable for mapping catchments of flooded areas and visualization of affected regions in real-time flood monitoring, in addition to implementing and developing emergency plans and vulnerability assessment through warning systems supported by RS data. In this regard, this study used RS data to monitor the southern segment of the Ganges River Delta. Multispectral Landsat 8-9 OLI/TIRS satellite images were evaluated in flood (March) and post-flood (November) periods for analysis of flood extent and landscape changes. Deep Learning (DL) algorithms of GRASS GIS and modules of qualitative and quantitative analysis were used as advanced methods of satellite image processing. The results constitute a series of maps based on the classified images for the monitoring of floods in the Ganges River Delta.

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Section: Problem Formulationmentioning

confidence: 99%

Deep Learning Methods of Satellite Image Processing for Monitoring of Flood Dynamics in the Ganges Delta, Bangladesh

Lemenkova

2024

Water

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“…The risk of fires is increasing in some of the less fire-prone regions, such as Europe, Poland, and Slovakia [33][34][35]. Significant greenhouse gas emissions and forest loss from wildfires could further accelerate climate change, with more people and animals suffering the impacts [36][37][38]. As a result, scientific analysis of wildfire risk and forecast of fire probability is critical for wildfire prevention, fighting, and management.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Wildfire Danger Level Using Logistic Regression Model in Sichuan Province, China

Peng,

Wei,

Chen

et al. 2023

Forests

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Sichuan Province preserves numerous rare and ancient species of plants and animals, making it an important bio-genetic repository in China and even the world. However, this region is also vulnerable to fire disturbance due to the rich forest resources, complex topography, and dry climate, and thus has become one of main regions in China needing wildfire prevention. Analyzing the main driving factors influencing wildfire incidence can provide data and policy guidance for wildfire management in Sichuan Province. Here we analyzed the spatial and temporal distribution characteristics of wildfires in Sichuan Province based on the wildfire spot data during 2010–2019. Based on 14 input variables, including climate, vegetation, human factors, and topography, we applied the Pearson correlation analysis and Random Forest methods to investigate the most important factors in driving wildfire occurrence. Then, the Logistic model was further applied to predict wildfire occurrences. The results showed that: (1) The southwestern Sichuan Province is a high-incidence area for wildfires, and most fires occurred from January to June. (2) The most important factor affecting wildfire occurrence is monthly average temperature, followed by elevation, monthly precipitation, population density, Normalized Difference Vegetation Index (NDVI), NDVI in the previous month, and Road kernel density. (3) The Logistic wildfire prediction model yielded good performance, with the area under curve (AUC) values higher than 0.94, overall accuracy (OA) higher than 86%, true positive rate (TPR) values higher than 0.82, and threat score (TS) values higher than 0.71. The final selected prediction model has an AUC of 0.944, an OA of 87.28%, a TPR of 0.829, and a TS of 0.723. (4) The results of the prediction indicate that extremely high danger of wildfires (probability of fire occurrence higher than 0.8) is concentrated in the southwest, which accounted for about 1% of the area of the study region, specifically in Panzhihua and Liangshan. These findings demonstrated the effectiveness of the Logistic model in predicting forest fires in Sichuan Province, providing valuable insights regarding forest fire management and prevention efforts in this region.

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Climate change induced risks assessment of a coastal area: A “socioeconomic and livelihood vulnerability index” based study in coastal Bangladesh

Chowdhury,

Ali,

Chowdhury

et al. 2024

Natural Hazards Research

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Extreme weather events (EWEs)-Related health complications in Bangladesh: A gender-based analysis on the 2017 catastrophic floods

Cited by 3 publications

References 64 publications

Deep Learning Methods of Satellite Image Processing for Monitoring of Flood Dynamics in the Ganges Delta, Bangladesh

Deep Learning Methods of Satellite Image Processing for Monitoring of Flood Dynamics in the Ganges Delta, Bangladesh

Analysis of Wildfire Danger Level Using Logistic Regression Model in Sichuan Province, China

Climate change induced risks assessment of a coastal area: A “socioeconomic and livelihood vulnerability index” based study in coastal Bangladesh

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