Evaluating Flood Damage using GIS and RADARSAT data-A case of the 1998 Catastrophe in Greater Dhaka, Bangladesh

The objective of this paper is to delineate flood prone areas and estimate damage in Greater Dhaka during the 1998 catastrophic flood using an integrated approach of GIS and remote sensing. Time series RADARSAT SAR data is acquired and used to demarcate flood boundaries for the 1998 flood event. This was accomplished by thresholding linear SAR imageries. Flood estimation demonstrated that flood areas steadily increased from early July 1998 and peaked on 25 August 1998 inundating 53% lands due to heavy monsoonal downpour and discharge from upstream points. Different thematic layers were combined with a derived flood map in order to assess flood damage for the same event. Flood damage analysis revealed that substantial damage has occurred in Greater Dhaka during the 1998 flood

Integrating satellite soil-moisture estimates and hydrological model products over Australia

Accurate soil-moisture monitoring is essential for water-resource management and agricultural applications, and is now widely undertaken using satellite remote sensing or terrestrial hydrological models’ products. While both methods have limitations, e.g. the limited soil depth resolution of space-borne data and data deficiencies in models, data-assimilation techniques can provide an alternative approach. Here, we use the recently developed data-driven Kalman–Takens approach to integrate satellite soil-moisture products with those of the Australian Water Resources Assessment system Landscape (AWRA-L) model. This is done to constrain the model’s soil-moisture simulations over Australia with those observed from the Advanced Microwave Scanning Radiometer-Earth Observing System and Soil-Moisture and Ocean Salinity between 2002 and 2017. The main objective is to investigate the ability of the integration framework to improve AWRA-L simulations of soil moisture. The improved estimates are then used to investigate spatiotemporal soil-moisture variations. The results show that the proposed model-satellite data integration approach improves the continental soil-moisture estimates by increasing their correlation to independent in situ measurements (∼10% relative to the non-assimilation estimates). Highlights Satellite soil-moisture measurements are used to improve model simulation. A data-driven approach based on Kalman–Takens is applied. The applied data-integration approach improves soil-moisture estimates

Remote Sensing of 1998 and 2000 Floods in Greater Dhaka, Bangladesh: Experiences from Catastrophic and Normal events

This paper is an attempt to develop a series of maps that precisely depict flood prone areas in Greater Dhaka, Bangladesh using remote sensing techniques. Multi-temporal RADARSAT SAR data were acquired and employed to delineate open water flood boundary during the floods of 1998 and 2000. Using a threshold algorithm, SAR data is segregated into water and non-water areas. The empirical threshold value was obtained by using visual interpretation technique, local knowledge of the study site and by deriving corresponding pixel values to land/water from each image. The result demonstrated that 53 percent of the study area was heavily inundated in 1998 flood which is the largest submerged area during a catastrophic scenario. In contrast, 35.32 percent area was flooded during the year 2000 which represents the area under water for a normal event. Using the reference data acquired from field visit, derived flood maps were further validated. Moderate accuracy is obtained for all flood maps, however, July 1998 image attained the highest overall accuracy (86%) in the dataset. The derived flood maps are expected to be useful to mitigate losses of lives and property from river water flooding in Greater Dhaka. Furthermore, this information would be worthwhile to develop an efficient flood disaster management system

Environmental factors associated with the distribution of visceral leishmaniasis in endemic areas of Bangladesh: Modeling the ecological niche

Background: Visceral leishmaniasis (VL) is a parasitic infection (also called kala-azar in South Asia) caused by Leishmania donovani that is a considerable threat to public health in the Indian subcontinent, including densely populated Bangladesh. The disease seriously affects the poorest subset of the population in the subcontinent. Despite the fact that the incidence of VL results in significant morbidity and mortality, its environmental determinants are relatively poorly understood, especially in Bangladesh. In this study, we have extracted a number of environmental variables obtained from a range of sources, along with human VL cases collected through several field visits, to model the distribution of disease which may then be used as a surrogate for determining the distribution of Phlebotomus argentipes vector, in hyperendemic and endemic areas of Mymensingh and Gazipur districts in Bangladesh. The analysis was carried out within an ecological niche model (ENM) framework using a maxent to explore the ecological requirements of the disease. Results: The results suggest that VL in the study area can be predicted by precipitation during the warmest quarter of the year, land surface temperature (LST), and normalized difference water index (NDWI). As P. argentipes is the single proven vector of L. donovani in the study area, its distribution could reasonably be determined by the same environmental variables. The analysis further showed that the majority of VL cases were located in mauzas where the estimated probability of the disease occurrence was high. This may reflect the potential distribution of the disease and consequently P. argentipes in the study area. Conclusions: The results of this study are expected to have important implications, particularly in vector control strategies and management of risk associated with this disease. Public health officials can use the results to prioritize their visits in specific areas. Further, the findings can be used as a baseline to model how the distribution of the disease caused by P. argentipes might change in the event of climatic and environmental changes that resulted from increased anthropogenic activities in Bangladesh and elsewhere

Modelling typhoid risk in Dhaka Metropolitan Area of Bangladesh: the role of socio-economic and environmental factors

BackgroundDeveloping countries in South Asia, such as Bangladesh, bear a disproportionate burden of diarrhoeal diseases such as Cholera, Typhoid and Paratyphoid. These seem to be aggravated by a number of social and environmental factors such as lack of access to safe drinking water, overcrowdedness and poor hygiene brought about by poverty. Some socioeconomic data can be obtained from census data whilst others are more difficult to elucidate. This study considers a range of both census data and spatial data from other sources, including remote sensing, as potential predictors of typhoid risk. Typhoid data are aggregated from hospital admission records for the period from 2005 to 2009. The spatial and statistical structures of the data are analysed and Principal Axis Factoring is used to reduce the degree of co-linearity in the data. The resulting factors are combined into a Quality of Life index, which in turn is used in a regression model of typhoid occurrence and risk.ResultsThe three Principal Factors used together explain 87% of the variance in the initial candidate predictors, which eminently qualifies them for use as a set of uncorrelated explanatory variables in a linear regression model. Initial regression result using Ordinary Least Squares (OLS) were disappointing, this was explainable by analysis of the spatial autocorrelation inherent in the Principal factors. The use of Geographically Weighted Regression caused a considerable increase in the predictive power of regressions based on these factors. The best prediction, determined by analysis of the Akaike Information Criterion (AIC) was found when the three factors were combined into a quality of life index, using a method previously published by others, and had a coefficient of determination of 73%.ConclusionsThe typhoid occurrence/risk prediction equation was used to develop the first risk map showing areas of Dhaka Metropolitan Area whose inhabitants are at greater or lesser risk of typhoid infection. This, coupled with seasonal information on typhoid incidence also reported in this paper, has the potential to advise public health professionals on developing prevention strategies such as targeted vaccination

Hydroclimatological variability and dengue transmission in Dhaka, Bangladesh: a time-series study

BackgroundWhile floods can potentially increase the transmission of dengue, only few studies have reported the association of dengue epidemics with flooding. We estimated the effects of river levels and rainfall on the hospital admissions for dengue fever at 11 major hospitals in Dhaka, Bangladesh.MethodsWe examined time-series of the number of hospital admissions of dengue fever in relation to river levels from 2005 to 2009 using generalized linear Poisson regression models adjusting for seasonal, between-year variation, public holidays and temperature.ResultsThere was strong evidence for an increase in dengue fever at high river levels. Hospitalisations increased by 6.9% (95% CI: 3.2, 10.7) for each 0.1 metre increase above a threshold (3.9 metres) for the average river level over lags of 0?5?weeks. Conversely, the number of hospitalisations increased by 29.6% (95% CI: 19.8, 40.2) for a 0.1 metre decrease below the same threshold of the average river level over lags of 0?19?weeks.ConclusionsOur findings provide evidence that factors associated with both high and low river levels increase the hospitalisations of dengue fever cases in Dhaka

Particle swarm optimization based LSTM networks for water level forecasting : a case study on Bangladesh river network

Floods are one of the most catastrophic natural disasters. Water level forecasting is an essential method of avoiding floods and disaster preparedness. In recent years, models for predicting water levels have been developed using artificial intelligence techniques like the artificial neural network (ANN). It has been demonstrated that more advanced and sequenced-based deep learning techniques, like long short-term memory (LSTM) networks, are superior at forecasting hydrological data. However, historically, most LSTM hyperparameters were based on experience, which typically did not produce the best outcomes. The Particle Swarm Optimization (PSO) method was utilized to adjust the LSTM hyperparameter to increase the capacity to learn data sequence characteristics. Utilizing water level observation data from stations along Bangladesh's Brahmaputra, Ganges, and Meghna rivers, the model was utilized to estimate flood dynamics. The Nash Sutcliffe efficiency (NSE) coefficient, root mean square error (RMSE), and MAE were used to assess the model's performance, where PSO-LSTM model outperforms the ANN, PSO-ANN, and LSTM models in predicting water levels in all stations. The PSO-LSTM model provides improved prediction accuracy and stability and improves water level forecasting accuracy at varying lead times. The findings may aid in sustainable flood risk mitigation in the study region in the future

Cloud-to-ground lightning in cities : seasonal variability and influential factors

Urban-induced land use changes have a significant impact on local weather patterns, leading to increased hydro-meteorological hazards in cities. Despite substantial threats posed to humans, understanding atmospheric hazards related to urbanisation, such as thunderstorms, lightning, and convective precipitation, remains unclear. This study aims to analyse seasonal variability of cloud-to-ground (CG) lightning in the five large metropolitans in Bangladesh utilising six years (2015–2020) of Global Lightning Detection Network (popularly known as GLD360) data. It also investigates factors influencing CG strokes. The analysis revealed substantial seasonal fluctuations in CG strokes, with a noticeable increase in lightning activity during the pre-monsoon months from upwind to metropolitan areas across the five cities. Both season and location appear to impact the diurnal variability of CG strokes in these urban centres. Bivariate regression analysis indicated that precipitation and particulate matter (PM) significantly influence lightning generation, whilst population density, urban size, and mean surface temperature have negligible effects. A sensitivity test employing a random forest (RF) model underscored the pivotal role of PM in CG strokes in four of the five cities assessed, highlighting the enduring impact of extreme pollution on lightning activity. Despite low causalities from CG lightning, the risk of property damage remains high in urban environments. This study provides valuable insights for shaping public policies in Bangladesh, a globally recognised climate hotspot

A novel framework for addressing uncertainties in machine learning-based geospatial approaches for flood prediction

Globally, many studies on machine learning (ML)-based flood susceptibility modeling have been carried out in recent years. While majority of those models produce reasonably accurate flood predictions, the outcomes are subject to uncertainty since flood susceptibility models (FSMs) may produce varying spatial predictions. However, there have not been many attempts to address these uncertainties because identifying spatial agreement in flood projections is a complex process. This study presents a framework for reducing spatial disagreement among four standalone and hybridized ML-based FSMs: random forest (RF), k-nearest neighbor (KNN), multilayer perceptron (MLP), and hybridized genetic algorithm-gaussian radial basis function-support vector regression (GA-RBF-SVR). Besides, an optimized model was developed combining the outcomes of those four models. The southwest coastal region of Bangladesh was selected as the case area. A comparable percentage of flood potential area (approximately 60% of the total land areas) was produced by all ML-based models. Despite achieving high prediction accuracy, spatial discrepancy in the model outcomes was observed, with pixel-wise correlation coefficients across different models ranging from 0.62 to 0.91. The optimized model exhibited high prediction accuracy and improved spatial agreement by reducing the number of classification errors. The framework presented in this study might aid in the formulation of risk-based development plans and enhancement of current early warning systems

National-scale flood risk assessment using GIS and remote sensing-based hybridized deep neural network and fuzzy analytic hierarchy process models : a case of Bangladesh

Assessing flood risk is challenging due to complex interactions among flood susceptibility, hazard, exposure, and vulnerability parameters. This study presents a novel flood risk assessment framework by utilizing a hybridized deep neural network (DNN) and fuzzy analytic hierarchy process (AHP) models. Bangladesh was selected as a case study region, where limited studies examined flood risk at a national scale. The results exhibited that hybridized DNN and fuzzy AHP models can produce the most accurate flood risk map while comparing among 15 different models. About 20.45% of Bangladesh are at flood risk zones of moderate, high, and very high severity. The northeastern region, as well as areas adjacent to the Ganges–Brahmaputra–Meghna rivers, have high flood damage potential, where a significant number of people were affected during the 2020 flood event. The risk assessment framework developed in this study would help policymakers formulate a comprehensive flood risk management system