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Northern Bangladesh, consisting of 16 administrative districts, has suffered from several drought phenomena in the last few decades. This study aims to formulate a drought susceptibility map considering three different types of droughts, which later, was referred to as composite drought, and was derived from three different indices. Standardized Precipitation Index (SPI) along with Vegetation Condition Index (VCI) from MODIS-NDVI, and Standardized Water Index (SWI) were considered for assessing the meteorological, agricultural, and hydrological drought, respectively. Drought condition was estimated for winter and pre-monsoon months (November to June) for 18 years, from 2000 through 2017. Individual drought maps for each month within the specified period were first prepared and then superimposed in the ArcGIS platform to produce an integrated drought map. Afterward, three different drought maps were incorporated to show the composite drought scenario. Then, extreme drought hot spots within the combined map were further identified using local indicators of spatial autocorrelation (LISA) method. Analyses showed that around 65% of the study area faced moderate to severe meteorological drought and mild to moderate agricultural and hydrological drought. The most extreme drought-prone month was March, and among 16 districts, Dinajpur and Naogaon faced the highest level of drought severity. This study is essentially important to unveil the individual drought susceptibility condition for a particular location. Detection of the composite drought hot spots will help in adopting short as well as long term adaptation and mitigation programs. At the same time, the methodology is reproducible for other drought-prone regions around the globe.

Accessibility of Hatiya, one of the most disaster-prone islands in Bangladesh, has many layers of challenges. Institutional arrangements guide the availability and quality of transports while islanders make travel decisions based on the geophysical character of the estuary and weather. These factors contribute to increasing disaster risks in transportation. The paper assesses accessibility using both primary and secondary data. Semi-structured interview responses helped in determining the Level of Services and Accessibility Indicators and generating Modal Suitability Matrix. Triangulating the findings with existing knowledge on geophysical dynamics and institutional arrangements illustrate, socially and politically constructed anthropogenic process to exacerbate risks arising from exposure to natural hazards in transportation. The supervision and monitoring of operation demand accountability and transparency. Thus, an integrated transport system can reduce disaster risks in the island's accessibility. The system needs to be culturally appropriate and use both technical and non-technical approaches.

A systematic review of the factors associated with pedestrian route choiceA systematic review of the factors associated with pedestrian route choice Transport Reviews · Dec 14, 2021Transport Reviews · Dec 14, 2021 Show publication Pedestrian route choice (PRC) is influenced by a wide range of factors, yet an understanding of the trends, patterns, and findings of PRC research is a gap in the literature. The present study applied the PRISMA framework to systematically identify published studies on PRC. The identified studies were synthesized by answering the systematic classification scheme (SCS) questions. Results show that prior studies have used a total of 105 factors associated with PRC. The identified factors were categorized under three groups (i.e. pedestrian socio-demographic factors, built environment factors, trip characteristics). The findings of this systematic review identified several critical research gaps for studying PRC behaviour. Future PRC research should aim to determine the influence of social, physical, and individual factors associated with PRC and the nature of the relationships of those factors with PRC.

Site-specific weed management (SSWM) may reduce herbicide use by identifying weed patches and weed-free areas. However, one major constraint is robust weed detection algorithms that are able to predict weed infestations outside of the training data. This study investigates the predictive power of regression models trained on drone imagery that are used within fields to predict infestations of annual grass weeds in the late growth stages of cereals. The main objective was to identify the optimum sampling strategy for training regression models based on aerial RGB images. The study showed that training based on sampling from the whole range of weed infestations or the extreme values in the field provided better prediction accuracy than random sampling. Prediction models based on vegetation indices (VIs) offered a useful alternative to a more complex random forest machine-learning algorithm. For binary decision-making, linear regression utilizing weed density information resulted in higher accuracy than a logistic regression approach that only relied on information regarding the presence/absence of weeds. Across six fields, the average balanced accuracy based on linear regression was in the range of 75–83%, with the highest accuracy found when the sampling was from the extreme values in the field, and with the lowest accuracy found for random sampling. For future work on training weed prediction models, choosing training sets covering the entire sample space is recommended in favor of random sampling.