A GIS-based approach to evaluating environmental influences on active and public transport accessibility of university students

Many young adults are susceptible to obesity issues and the increased health risks associated with a lack of physical activity. Those who are prone to gaining weight include university students. An active transport system (walking and cycling), in combination with well-funded public transport, are essential components of a sustainable urban transport network, offering many benefits to the health of the individual, as well as the environment, economy, and society as a whole. The spatial association between active mobility (i.e. the physical activity of a human being for locomotion) of young adults and the environment, however, is poorly understood. This study presents a GIS-based model to determine association of various environmental (natural and built environment) factors with locational accessibility of active and public transport trips taken by university students. A GIS-based ensemble of Frequency Ratio (FR) and the Analytical Hierarchy Process (AHP) model was established. We analysed the characteristics of locations accessed by university students in relation to eight environmental factors including slope, elevation, land use, population density, travel time, building density, intersection density, and public transport service area. The model was applied to the Grenoble metropolitan region of France, an area well-known for policies which promote active transport. The results indicated that intersection density and land use are strongly associated with active and public transport accessibility, with weights of 0.17 and 0.16, respectively. The presence of infrastructure to support active travel, and regulation to limit vehicular speed, also improved accessibility. Approximately 50% of the area of the Grenoble metropolitan region was defined as accessible and suitable ('moderate' to 'very high' degree) for active mobility. The results of this study could allow city planners to monitor the existing status of active and public transport facilities, and identify areas that require additional work to improve accessibility

Have coastal embankments reduced flooding in Bangladesh?

From the 1960s, embankments have been constructed in south western coastal region of Bangladesh to provide protection against flooding, but the success of the polder programme is disputed. We present analysis of floods during the years 1988–2012, diagnosing whether the floods were attributable to monsoonal precipitation (pluvial flooding), high upstream river discharge into the tidal delta (fluvio-tidal flooding), or cyclone-induced storm surges. We find that pluvial flooding was the most frequent, but typically resulted in less flooded area (11.44% of the region on average)compared with the other forms of flooding. The greatest area of inundation (48% of total area)occurring in 2001 as a consequence of fluvio-tidal and surge flooding, whilst cyclone Sidr in 2007 flooded 35% of the area. We modelled these different forms of inundation to estimate what flooding might have been had the polders not been constructed. For the ‘no embankment’ counter-factual scenario, our model demonstrated that because of a combination of subsidence and inadequate drainage, construction of the polders has increased the pluvial flooded area by 6.5% on average (334 km2). However, during the 1998 fluvio-tidal flood, the embankments protected an estimated 54% of the area from flooding. During the cyclone Sidr storm surge event, embankment failure in several polders and pluvial inundation resulted in 35% area inundation, otherwise, the total inundation would have been 18% area. We conclude that whilst polders have provided protection against storm surges and fluvio-tidal events of moderate severity, they have exacerbated more frequent pluvial flooding and promoted potential flooding impacts during the most extreme storm surges

Pedestrian facilities and perceived pedestrian level of service (plos) : a case study of chittagong metropolitan area, Bangladesh

The promotion of active transport (a type of sustainable transportation) such as walking is a form of response against environmental pollution engendering from transport sector. Pedestrian level of service (PLOS) is a measurement tool to evaluate the degree of pedestrian accommodation on roadway to provide a comfortable and safe walking environment. The roadway characteristics-based model to measure PLOS has been widely applied since this approach is conceived as being transferable to different contexts. We present a comprehensive framework to measure the influence of pedestrian facilities on perceived PLOS qualitatively and quantitatively. We modeled triangular relationships among pedestrian facilities, perceived roadway conditions (accessibility, safety, comfort, and attractiveness), and perceived PLOS to identify pedestrian facilities, related to footpath, carriageway, and transit, influencing perceived PLOS. We developed these models for a case study of Chittagong Metropolitan Area in Bangladesh. Poor condition of pedestrian facilities in the region resulted in PLOS B as the highest tier of perceived PLOS. Findings of this study showed that accessibility and attractiveness influenced the perceived PLOS for footpath, carriageway, and transit, whereas safety is an important roadway condition for carriageway and transit facilities. We further measured the influence of 22 selected parameters of pedestrian facilities on roadway conditions and perceived PLOS. We concluded that achieving a better perceived PLOS is dependent on the availability, maintenance, and planning of different pedestrian facilities, as improper placement and poor condition of such facilities increased the probability that a lower level PLOS will be perceived

The potential of Tidal River Management for flood alleviation in South Western Bangladesh

Reduced sediment deposition, land subsidence, channel siltation, and salinity intrusion has been an unintended consequence of the construction of polders in the south western delta of Bangladesh in the 1960s. Tidal River Management (TRM) is a process that is intended to temporarily reverse these processes and restore sediment deposition and land elevation at the low-lying sites, known as ‘beels’, where TRM is carried out. However, there is limited evidence to prioritise sites for TRM on the basis of its potential effectiveness at alleviating flooding. In this study, the south western delta of Bangladesh was classified according to different flood susceptible zones. In south western Bangladesh, the major portion of agricultural and aquaculture land is located within flood susceptible zones (65% and 81%, respectively). 44.5% of the total population in embanked regions live in areas classified as being flood susceptible. This study identified 106 ‘beels’ suitable for TRM. Modelling of potential sediment deposition predicted that the consequent increase in land elevation could be up to 1.4 m in five years, which would alleviate land subsidence and modify several geomorphological factors such as aspect, slope, curvature, and Stream Power Index (SPI). Implementation of TRM at these sites could potentially reduce the probability of annual flooding from 0.86 (on average) to 0.57 (on average). Therefore, TRM could lower the flood susceptible area by 35% in suitable ‘beels’. Whilst during the implementation of TRM agriculture has to cease for a few years, a systematic programme of TRM could result in a long-term increase in agricultural production by reducing flood susceptibility of agricultural lands in delta regions

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

The use of watershed geomorphic data in flash flood susceptibility zoning : a case study of the Karnaphuli and Sangu river basins of Bangladesh

The occurrence of heavy rainfall in the south-eastern hilly region of Bangladesh makes this area highly susceptible to recurrent flash flooding. As the region is the commercial capital of Bangladesh, these flash floods pose a significant threat to the national economy. Predicting this type of flooding is a complex task which requires a detailed understanding of the river basin characteristics. This study evaluated the susceptibility of the region to flash floods emanating from within the Karnaphuli and Sangu river basins. Twenty-two morphometric parameters were used. The occurrence and impact of flash floods within these basins are mainly associated with the volume of runoff, runoff velocity, and the surface infiltration capacity of the various watersheds. Analysis showed that major parts of the basin were susceptible to flash flooding events of a ‘moderate’-to-‘very high’ level of severity. The degree of susceptibility of ten of the watersheds was rated as ‘high’, and one was ‘very high’. The flash flood susceptibility map drawn from the analysis was used at the sub-district level to identify populated areas at risk. More than 80% of the total area of the 16 sub-districts were determined to have a ‘high’-to-‘very-high’-level flood susceptibility. The analysis noted that around 3.4 million people reside in flash flood-prone areas, therefore indicating the potential for loss of life and property. The study identified significant flash flood potential zones within a region of national importance, and exposure of the population to these events. Detailed analysis and display of flash flood susceptibility data at the sub-district level can enable the relevant organizations to improve watershed management practices and, as a consequence, alleviate future flood risk

The effects of changing land use and flood hazard on poverty in coastal Bangladesh

The construction of polders in the coastal region of Bangladesh has significantly modified the patterns of flooding, as well as leading to significant land use/land cover (hereinafter, LULC) changes. The impact of LULC change and flooding on poverty is complex and poorly understood. This study presents a spatiotemporal appraisal of poverty in relation to LULC change and pluvial flood risk in the south western embanked area of Bangladesh. A combination of logistic regression (LR), cellular automata (CA), and Markov Chain models were utilised to predict future LULC based on historical data. Flood risk assessment was performed at present and for future LULC scenarios. A spatial regression model was developed, incorporating multiple parameters to estimate the wealth index (WI) for present-day and future scenarios. In the study area, agricultural lands reduced from 34 % in 2005 to 8% in 2010, while aquaculture land cover increased from 17 % to 39 % during the same time. The rate of LULC change was relatively low between 2010 and 2019. Based on the recent trend, LULC was predicted for the year 2030. Flood risk was positively correlated with LULC and the expected annual damage (EAD) was estimated at $903 million in 2005, which is likely to increase to$2096 million by 2030, considering changes in LULC scenarios. The analysis further showed that the EAD and LULC change were negatively associated with the WI. Despite consistent national GDP growth in Bangladesh in recent years, the rate of increase of WI is likely to be low in the future because flood risk and patterns of LULC change have a negative effect on WI

Improving spatial agreement in machine learning-based landslide susceptibility mapping

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 individual models. The modelling approach followed in this study would be useful in minimizing uncertainties of various methods and improving landslide predictions

Spatial distribution of heatwave vulnerability in a coastal city of Bangladesh

While extreme heat events received wider attention in recent years, understanding heatwave vulnerability is still a challenging task that requires a clear understanding of a range of location-dependent climatic, socio-economic, physiological, and environmental parameters. This study investigates the spatial distribution of heatwave vulnerability in Chattogram City Corporation (CCC) — the commercial capital of Bangladesh. A heatwave vulnerability index (HVI) was developed, including various parameters related to three latent variables — exposure, sensitivity, and adaptive capacity — derived using remote sensing and socio-economic data. Factor analysis was performed to assess all parameters related to HVI. Besides, spatial autocorrelation, cluster and outlier analyses, and hot spot analysis were carried out to investigate the spatial distribution of HVI. This study shows a spatial distribution of HVI in CCC, which is spatially associated with various individual parameters. A total of seven wards (smallest administrative zone of CCC) were found to be very highly vulnerable to the heatwave. This study further reveals that heatwave vulnerability is randomly distributed throughout the city, although the high land surface temperature is concentrated in the existing built-up areas. It also identified three major hot spots of heatwave vulnerability in CCC. The methodology and findings of this study will be of interest to the policymakers and city planners to prepare mitigation plans, policies, and strategies to mitigate this hazard

Spatio-temporal patterns of land use/land cover change in the heterogeneous coastal region of Bangladesh between 1990 and 2017

Although a detailed analysis of land use and land cover (LULC) change is essential in providing a greater understanding of increased human-environment interactions across the coastal region of Bangladesh, substantial challenges still exist for accurately classifying coastal LULC. This is due to the existence of high-level landscape heterogeneity and unavailability of good quality remotely sensed data. This study, the first of a kind, implemented a unique methodological approach to this challenge. Using freely available Landsat imagery, eXtreme Gradient Boosting (XGBoost)-based informative feature selection and Random Forest classification is used to elucidate spatio-temporal patterns of LULC across coastal areas over a 28-year period (1990-2017). We show that the XGBoost feature selection approach effectively addresses the issue of high landscape heterogeneity and spectral complexities in the image data, successfully augmenting the RF model performance (providing a mean user's accuracy > 0.82). Multi-temporal LULC maps reveal that Bangladesh's coastal areas experienced a net increase in agricultural land (5.44%), built-up (4.91%) and river (4.52%) areas over the past 28 years. While vegetation cover experienced a net decrease (8.26%), an increasing vegetation trend was observed in the years since 2000, primarily due to the Bangladesh government's afforestation initiatives across the southern coastal belts. These findings provide a comprehensive picture of coastal LULC patterns, which will be useful for policy makers and resource managers to incorporate into coastal land use and environmental management practices. This work also provides useful methodological insights for future research to effectively address the spatial and spectral complexities of remotely sensed data used in classifying the LULC of a heterogeneous landscape