Evaluating multi-hazard risk associated with tropical cyclones using the fuzzy analytic hierarchy process model

Supplementary data are available online at: https://www.sciencedirect.com/science/article/pii/S2666592123001178#appsec1 .Multi-hazard events have received attention globally due to their increasing frequency and severity in recent years. The coastal region of Bangladesh is particularly vulnerable to multi-hazard events induced by tropical cyclones (TC), including coastal flooding, extreme precipitation, extreme winds, and salinity intrusion. These events inflict substantial damage on human lives and property, yet there has been limited effort to quantitatively assess the associated risks. This study aims to investigate the spatial distribution of multi-hazard risks stemming from TC events, employing a Fuzzy Analytic Hierarchy Process (FAHP) approach. Risk is assessed in relation to hazard, exposure, vulnerability, and mitigation capacities in the study area. Various indicators are selected to define each of these four risk components, with weights determined through expert input for FAHP modeling. The results indicate that more than 50% of the area faces multi-hazard risks, with the hazard component exhibiting the highest degree of risk association, followed by exposure, vulnerability, and adaptive capacity. Storm surge-induced flooding is identified as the most prominent hazard during TC events, followed by intense precipitation, extreme winds, and salinity intrusion. Areas characterized by high population density, a large number of vulnerable populations (e.g., those under 15 years or over 65 years), low elevation, and underdevelopment are found to be the most risk prone. Notably, the presence of hospitals, cyclone centers, and effective warning systems in proximity to an area enhances its potential to withstand multi-hazard impacts. Among the 19 coastal districts, Cox's Bazar and Feni are identified as the most risk prone. The framework and findings presented in this study offer valuable insights for the development and prioritization of multi-hazard risk mitigation policies by identifying the most vulnerable zones and the associated risk factors

Dense prediction of label noise for learning building extraction from aerial drone imagery

Label noise is a commonly encountered problem in learning building extraction tasks; its presence can reduce performance and increase learning complexity. This is especially true for cases where high resolution aerial drone imagery is used, as the labels may not perfectly correspond/align with the actual objects in the imagery. In general machine learning and computer vision context, labels refer to the associated class of data, and in remote sensing-based building extraction refer to pixel-level classes. Dense label noise in building extraction tasks has rarely been formalized and assessed. We formulate a taxonomy of label noise models for building extraction tasks, which incorporates both pixel-wise and dense models. While learning dense prediction under label noise, the differences between the ground truth clean label and observed noisy label can be encoded by error matrices indicating locations and type of noisy pixel-level labels. In this work, we explicitly learn to approximate error matrices for improving building extraction performance; essentially, learning dense prediction of label noise as a subtask of a larger building extraction task. We propose two new model frameworks for learning building extraction under dense real-world label noise, and consequently two new network architectures, which approximate the error matrices as intermediate predictions. The first model learns the general error matrix as an intermediate step and the second model learns the false positive and false-negative error matrices independently, as intermediate steps. Approximating intermediate error matrices can generate label noise saliency maps, for identifying labels having higher chances of being mis-labelled. We have used ultra-high-resolution aerial images, noisy observed labels from OpenStreetMap, and clean labels obtained after careful annotation by the authors. When compared to the baseline model trained and tested using clean labels, our intermediate false positive-false negative error matrix model provides Intersection-Over-Union gain of 2.74% and F1-score gain of 1.75% on the independent test set. Furthermore, our proposed models provide much higher recall than currently used deep learning models for building extraction, while providing comparable precision. We show that intermediate false positive-false negative error matrix approximation can improve performance under label noise

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

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

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

Data availability: Data will be made available on request.Supplementary data are available online at: https://www.sciencedirect.com/science/article/pii/S2590123023000786#appsec1 .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.Ministry of Post, Telecommunication and Information Technology, Bangladesh through ICT Innovation Fund (2020–21) round 3: Grant Number 12

Cloud-to-Ground Lightning in Cities: Seasonal Variability and Influential Factors

Data availability: The raw data essential for reproducing the findings presented in this article are derived from proprietary commercial sources, and, unfortunately, the authors are not permitted to share this data due to contractual and authorization constraints. The restrictions imposed by the data providers prohibit the authors from making the raw data publicly available. However, the authors are committed to providing any necessary information or details about the data and methodology upon reasonable request to facilitate reproducibility and further scientific inquiry. Interested parties may contact the corresponding author for additional clarification or specific inquiries regarding the data used in this study.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.Open Access funding enabled and organized by CAUL and its Member Institutions. The authors declare that no funds, grants, or other support was received during the preparation of this manuscript

Climate threats to coastal infrastructure and sustainable development outcomes

Data availability: Data used in this study can be accessed at https://doi.org/10.5281/zenodo.10554713.Code availability: Code relevant to the analysis can be accessed at https://www.dropbox.com/scl/fi/tpjcxtl4j9m9ht0tl0ocq/NCLIM-23071599-code_final.zip?rlkey=ux7du7k4rkru352moob6quwwu&dl=0.Change history: 11 March 2024A Correction to this paper has been published: https://doi.org/10.1038/s41558-024-01974-8Acknowledgements: We acknowledge the Bangladesh Climate Change and Disaster Risk Management Team at the World Bank, in particular S. Kazi and I. Urrutia, for providing the synthetic household data and general support throughout the project. Any views expressed are not necessarily those of or endorsed by the World Bank. We also acknowledge support from the United Nations Office for Project Services (UNOPS), the Global Center on Adaptation (GCA), the Government of Bangladesh, and the Center for Environmental and Geographic Information Services (CEGIS) for assisting with access to data and in-country facilitation. We acknowledge imagery courtesy of the United Nations Sustainable Development Goals (https://www.un.org/sustainabledevelopment), although the content of this publication is not endorsed by the United Nations or its officials or the Member States.Climate hazards pose increasing threats to development outcomes across the world’s coastal regions by impacting infrastructure service delivery. Using a high-resolution dataset of 8.2 million households in Bangladesh’s coastal zone, we assess the extent to which infrastructure service disruptions induced by flood, cyclone and erosion hazards can thwart progress towards the Sustainable Development Goals (SDGs). Results show that climate hazards potentially threaten infrastructure service access to all households, with the poorest being disproportionately threatened in 69% of coastal subdistricts. Targeting adaptation to these climatic threats in one-third (33%) of the most vulnerable areas could help to safeguard 50–85% of achieved progress towards SDG 3, 4, 7, 8 and 13 indicators. These findings illustrate the potential of geospatial climate risk analyses, which incorporate direct household exposure and essential service access. Such high-resolution analyses are becoming feasible even in data-scarce parts of the world, helping decision-makers target and prioritize pro-poor development.Open access funding provided by Royal Institute of Technology

Nonmotorized Commuting Behavior of Middle-Income Working Adults in a Developing Country

Although nonmotorized transport (NMT) offers economic, environmental, and health benefits to individuals and communities, understanding nonmotorized travel behavior is a challenging task due to complex interactions of a wide range of factors. While behavioral models offer a conceptual framework to understand human behavior, their use in the study of travel behavior in developing countries is still in its infancy. This study uses three behavioral models—the theory of planned behavior, the theory of triadic influence, and the ecological model of health behavior—to identify potential factors influencing intentions and behavior toward the use of NMT by middle-income working adults, inhabiting the Chittagong City Corporation (CCC) area of Bangladesh. A total of 720 middle-income working adults (aged between 18 and 65 years) were randomly selected and interviewed at major commercial and retail business areas of the CCC. Multiple linear and binary logistic models were developed to quantify the extent of the influence of different factors on nonmotorized mode choice behavior. Results indicated that personal factors (proximal) such as attitude, subjective norm, and behavioral control influence respondents’ intentions and motivation in choosing NMT. However, the current use of NMT was less controlled by intention, while factors associated with the social, cultural, and built environment had (distal) significant influence. The findings of this study could assist urban planners in adopting structural and nonstructural measures to promote NMT use

Reclassifying historical disasters: From single to multi-hazards

Data availability: Data will be made available on request.Multi-hazard events, characterized by the simultaneous, cascading, or cumulative occurrence of multiple natural hazards, pose a significant threat to human lives and assets. This is primarily due to the cumulative and cascading effects arising from the interplay of various natural hazards across space and time. However, their identification is challenging, which is attributable to the complex nature of natural hazard interactions and the limited availability of multi-hazard observations. This study presents an approach for identifying multi-hazard events during the past 123 years (1900–2023) using the EM-DAT global disaster database. Leveraging the ‘associated hazard’ information in EM-DAT, multi-hazard events are detected and assessed in relation to their frequency, impact on human lives and assets, and reporting trends. The interactions between various combinations of natural hazard pairs are explored, reclassifying them into four categories: preconditioned/triggering, multivariate, temporally compounding, and spatially compounding multi-hazard events. The results show, globally, approximately 19 % of the 16,535 disasters recorded in EM-DAT can be classified as multi-hazard events. However, the multi-hazard events recorded in EM-DAT are disproportionately responsible for nearly 59 % of the estimated global economic losses. Conversely, single hazard events resulted in higher fatalities compared to multi-hazard events. The largest proportion of multi-hazard events are associated with floods, storms, and earthquakes. Landslides emerge as the predominant secondary hazards within multi-hazard pairs, primarily triggered by floods, storms, and earthquakes, with the majority of multi-hazard events exhibiting preconditioned/triggering and multivariate characteristics. There is a higher prevalence of multi-hazard events in Asia and North America, whilst temporal overlaps of multiple hazards predominate in Europe. These results can be used to increase the integration of multi-hazard thinking in risk assessments, emergency management response plans and mitigation policies at both national and international levels.The authors acknowledge the European COST Action DAMOCLES (CA17109). CJW, MSGA, JD, MJR and ET were supported by the European Union's Horizon Europe ‘Multi-hazard and risk informed system for Enhanced local and regional Disaster risk management (MEDiate)’ project under grant agreement no. 10049641. CJW also acknowledges support from the NERC Global Partnerships Seedcorn Fund ‘EMERGE’ project though grant no. NE/W003775/1. PJW received support from the MYRIAD-EU project, which received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement no. 101003276. MDM and JZ received support from the European Union's Horizon 2020 research and innovation programme under grant agreement no. 101003469 (XAIDA). AMR was supported by the Helmholtz ‘Changing Earth’ program and acknowledges the Portuguese Science Foundation (FCT) through the project AMOTHEC (DRI/India/0098/2020)

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