Members’ Publication

In response to Bangladesh's severe water shortages, this research delves into the intricate dynamics of groundwater vulnerability. Integrating often-overlooked factors such as topography, meteorology, socio-economic conditions, and land use & geology, the study employs advanced Random Forest (RF) modeling. Rigorously analyzing 200 strategically chosen sample points, the research uncovers critical areas like Rajshahi, Nawabganj, Naogaon, and Dhaka, constituting 21% of the land, as highly vulnerable. In contrast, regions like Rangpur, Mymensingh, and Barisal, encompassing 31% of the area, exhibit lower vulnerability. Topographic factors, accounting for 45% of the vulnerability, including aspect, drainage density, and slope, significantly influence susceptibility. Socio-economic elements contribute 22%, particularly population density and industrial activities. The RF model achieves exceptional accuracy (over 90%), emphasizing the complexity of groundwater dynamics. By integrating geological, social, and economic aspects, the study provides actionable insights for nuanced and sustainable management strategies. This research not only unveils a highly accurate groundwater vulnerability map, enriching scientific understanding, but also offers a unique approach by incorporating often-overlooked variables and leveraging machine learning. These insights empower policymakers and urban planners to craft targeted and sustainable groundwater management strategies, ensuring a resilient water supply system for Bangladesh's growing population. Through this work, the aim is to significantly contribute to the scientific community while providing data-driven solutions for the nation's pressing water challenges.

In this work, we analyzed the locations of existing bus stops in the Halifax Regional Municipality (HRM) area and then calculated the walking distance in time in order to understand the accessibility for pedestrians. In doing so, we employed geographic information systems (e.g., spatial and network analysis tools) to generate accessibility models from bus stops at 5, 10, 15, and 20 min of walking distance. After generating the service area of bus stops on maps, we overlaid socioeconomic variables (i.e., age group, income, and bus-stop accessibility) to better understand the HRM public transport network. We found that population density was an important consideration in providing the number of bus stops in specific communities, which may be related to the facilities offered by the urban hierarchy. Furthermore, we established a relationship between transit stops and accessibility for people in age groups of between 0 and 19 (e.g., school-going children) and 65+ (i.e., the older population) so as to obtain an understanding of the time required for them to access the bus stops on foot. Overall, 77% of the population in the HRM was served by public transport within 5 min of walking; however, for the 65+ cohort, this number was higher (82%). A significant amount of the young population (23%) was served over longer distances than 10′ of walking. We summarize that our findings are critical for planners, practitioners, and researchers in order for them to understand the present transit system in place based on accessibility to the bus stops within walking distance.

Hard rock systems are typically characterized by undulated landscapes, short flow path, dense drainage network, and low aquifer storage. If located in water-limited environments (WLE), then typically have variable rainfall characteristics, frequent floods and droughts, and savannah type of vegetation capable to uptake groundwater. In such environments, surface–groundwater interactions are of primary importance. In this study, surface–groundwater interactions are analyzed focusing on spatiotemporal net recharge dependence on hydrotopes, where a hydrotope is conceptualized as spatial unit (or object), which at a given scale of assessment can be assumed as internally homogeneous (e.g. tree canopy or rock outcrop) but hydro(geo)logically different than the adjacent area. The study is carried out in the small (7.6 ha) Trabadillo study area (TSA) in Spain, representative for hard rocks of WLE using 3D-GSFLOW integrated hydrological model (IHM) at very fine grid (5x5m), extracted from coarser grid (100x100m) of Sardon 3D-GSFLOW model (Hassan et al., 2014, Journal of Hydrology 517, 390–410). The IHM was calibrated using: (i) 20-year head observation; (ii) 8-year soil moisture observations at 4 profiles, each at different hydrotope and each at 4 different depths; and (iii) MODIS satellite earth observation of evapotranspiration. Despite applying spatially uniform rainfall as forcing, surface–groundwater interactions were highly spatially variable, primarily, because of hydrotope-differences in rainfall interception loss and secondarily, because of differences in transpiration, in subsurface evaporation and in hydrological properties of subsurface. The high temporal variability of rainfall, ranging yearly from 310 to 925 mm, resulted in high temporal variability of all water fluxes, including groundwater fluxes; e.g. gross recharge (Rg) varied annually from 2 to 445 mm, net recharge (Rn), from −50 mm to + 89 mm, while the 20-year mean Rn was close to zero, because Rg was counterbalanced by groundwater exfiltration and less by groundwater evapotranspiration. The high temporal variability of groundwater fluxes and many dry years with largely negative Rn, in combination with low aquifer storage, emphasized TSA dependence on rainfall and its vulnerability to droughts. The hydrotope modeling concept applied at the very fine scale, emphasized also large Rn dependence on land cover type, especially on density and species composition of trees, invisible at coarser scale modeling. As such, the proposed hydrotope modelling approach can be applied as a tool to test whether in a given environment planting (or cutting) trees will result in increase or decrease of water resources.

Meteorological drought, driven by inadequate precipitation, has significant repercussions for water resources, agriculture, and human well-being. This study conducted an extensive assessment of meteorological drought in northern Bangladesh, employing remote sensing indices and machine learning techniques. The main aim was to evaluate meteorological drought occurrences in northern Bangladesh from 2010 to 2019, utilizing seven drought parameters and a machine learning model. Utilizing a Random Forest (RF) model, this study employed the Standardized Precipitation Index (SPI) as the dependent variable and seven remote sensing indices as independent variables. Through this methodology, the study assessed the significance of these indices generated by the model and integrated them, culminating in the creation of a meteorological drought distribution map spanning 2010 to 2019. This approach offers novel insights by probing the interplay and collective impacts of these indices, shedding light on previously unexplored aspects of regional drought patterns of northern Bangladesh. The major findings showed that precipitation strongly influenced both short-term and long-term meteorological drought episodes. Moreover, land surface-related indices, such as Evapotranspiration (ET) and Normalized Difference Water Index (NDWI), exhibited a more pronounced impact on short-term drought occurrences, while vegetation-related indices like Normalized Multi-band Drought Index (NMDI) and Normalized Difference Vegetation Index (NDVI) demonstrated greater influence over long-term drought events. During this timeframe, the Rajshahi division experienced frequent extreme and severe drought events. Moderate droughts and abnormally dry conditions were widespread. The Barind tract area consistently faced moderate to extreme droughts, with exceptions in 2011, 2014, and 2019. On average, over 5% of the region had extreme droughts, while more than 12% experienced severe droughts during this decade. Long-term drought indicators (SPI 6 and SPI 9) consistently showed higher frequencies of extreme and severe droughts compared to short-term indicators (SPI 1 and SPI 3), emphasizing the influence of prolonged rainfall deficits on extreme droughts and the relevance of longer time frames for severe drought dynamics. The RF model demonstrated strong performance with accuracy ranging from 81% to 95%. Low prediction errors (RMSE 6% to 31%) and high out-of-bag (OOB) accuracy ranging from 76% to 98% highlighted its accuracy. The F1 score consistently exceeded 76%, indicating high precision and recall. Cross-validation values ranged from 78% to 94%, affirming reliable generalization to new data. Incorporating the main findings, this study contributes valuable insights for the formulation of targeted drought mitigation strategies in northern Bangladesh. It is imperative to note that the scope of this study is confined to the northern region of Bangladesh, and generalizing these findings to other regions should be exercised with caution. Nevertheless, the research methodology and approach can serve as a model for future studies in related fields, advancing knowledge of how to assess droughts using remote sensing and machine learning methods.

The rollout of driverless cars (DC) may reshape human mobility drastically. Urban roads may face a surge of car traffic if DC technologies continue to enhance the use of private cars. Shared DC use can remedy this by reducing the number of urban trips that non-shared-use DC could otherwise make. While recent research has proven the importance of socioeconomic factors on shared DC use, the influence of present sharing behaviour, personality traits, and social norms on shared DC use has not been extensively explored to date. To address this gap in this study, we employed a panel of 500 Edinburgh-based respondents through an online survey to examine the likelihood of accepting the non-shared-use and shared-use DC options for regular urban trips. We have collected data on respondents’ present carsharing and ridesharing behaviour, personality traits, social norms, and sociodemographic characteristics. To elicit the impact of these factors on the likelihood of accepting non-shared-use and shared-used DC options, ordered probit models were estimated. The model findings imply that frequent household car users and those influenced by social expectations to preserve the environment are willing to use non-shared-use DC. In contrast, city centre dwellers, cooperative and younger adults with sharing attitudes show a higher tendency towards shared DC. High-earning, working-aged and young respondents are more inclined to use a driverless taxi, whereas city-centre dwellers and those influenced by social expectations to share personal resources are more favourable towards ridesharing with a stranger in DC. These results can assist the policymakers and transport planners shape policies for promoting shared DC use and transport service providers to deliver and operate shared DC fleets efficiently.

Recently a comprehensive traffic safety study for the two main corridors was conducted in Eastern Province of Saudi Arabia. The main aim of the study was to analyze the causes of various traffic safety related issues in the Eastern Province. This was done with regards to traffic crashes with fatalities in-particular; some of the methods employed included collecting data on traffic volumes, highway speeds, travel times, traffic crashes, and conducting the road safety audit. These methods helped develop several countermeasures which targeted to reduce crashes and the severity levels associated with them, and hence improve the overall traffic safety situation in the area. This paper explains a sketch planning approach for the application of Highway Safety Manual (HSM) based crash prediction methods using road safety audit data, to help estimate reduction in crashes due to proposed improvements. The selection of the two corridors was to best represent the transportation network in the Eastern Province which consists of several different types of facilities including urban, suburban, and rural roads. Comprehensive road safety audit was performed on two corridors and the data collected thereof was archived into reporting templates. After thorough evaluation of the data collected in the safety audit process, improvements to root causes of traffic safety concerns were recommended; the HSM based crash prediction methods were applied to see the effectiveness of the improvements suggested. Reports were prepared for each of the data collection items. While the results show reduction in traffic crashes based on improvements to the corridors, this approach could contribute in its true essence when applied in combination with several other traffic safety improvement strategies.

Safety experts and transportation departments are focused on reducing road accidents and their societal and economic effects. The most crucial step in establishing a successful road safety practice is identifying dangerous highway zones through the study of crashes and looking at how the location of accidents relates to surrounding geography and other factors. Using the latest cutting-edge GIS analytical methods, this study aims to map the locations of accident hot spots and evaluate the severity and spatial extent of crash occurrences in Ohio. Road traffic crash (RTC) data has been analyzed using sophisticated GIS-based hot spot analysis for decades by safety researchers. Using four years’ worth of crash data from the state of Ohio and spatial autocorrelation analysis, this study aims to show how a GIS technique can be used to find places where accidents are likely to happen (2017–2020). The study analyzed and ranked crash hotspot areas using the matching severity levels of RTCs. Cluster zones of high and low crash severity were discovered using the spatial autocorrelation tool and the Getis Ord Gi* statistics tool to evaluate the distribution of RTCs. The analysis used Getis Ord Gi*, the crash severity index, and Moran’s I spatial autocorrelation of accident events. The findings indicated that these techniques were useful for identifying and rating crash hotspot locations. Since the sites of the identified accident hotspots are located in significant cities in the state of Ohio, such as Cleveland, Cincinnati, Toledo, and Columbus, the organizations in charge of traffic management should make it their top priority to minimize the negative socioeconomic impact that RTCs have and should also conduct a thorough investigation. This study’s contribution is the incorporation of crash severity into hot spot analysis using GIS, which could lead to better-informed decision-making in the realm of highway safety

Predominant water scarcity and environmental degradation in Bangladesh's coastal communities arise due to extreme meteorological conditions, unrestrained water usage, and extensive saline intrusion into both surface and groundwater. This study leverages both quantitative data from a household survey and qualitative data from a Participatory Rural Appraisal to explore the perspectives of 150 official experts and 240 coastal public respondents regarding the implementation of a "Community-Based Rainwater Harvesting System" (CBRWHS) as a sustainable water supply technology in six major coastal districts of Bangladesh. Though CBRWHS surpasses the benefits of an individual harvesting system (IHS) due to its collective approach, high storage capacity, social cohesiveness, environmental benefits, and potential for scalability, findings suggest that the experts identify financial insolvency, lack of technical skills, and insufficient education as principal barriers against adopting CBRWHS. They propose that providing technical instructions, education, and establishing a CBRWHS pilot could serve as effective incentives. While expressing a sufficient understanding of IHS, respondents are willing to adopt CBRWHS only if financial support is provided. Given the residents' experience with IHS, they are cognizant of the financial and technical challenges intrinsic to such systems. CBRWHS, unlike IHS, operates as a shared system with collective property rights and regulations. Majority of respondents perceive CBRWHS as a potential significant mitigation tool against the water shortage situation since the challenges associated with harvesting, maintaining, and using rainwater could be addressed collectively by the user community. Similar to the official experts, public respondents identify a lack of technical expertise, financial constraints, and regulations as the most significant obstacles to IHS adoption. The findings of this study hold substantial implications not only for the local region but also for similar coastal regions worldwide, suggesting a crucial need for the prioritization of community-based solutions to water scarcity within the wider global discourse on sustainable water management.