Land use and transportation planning have a significant impact on the performance of cities’ traffic conditions and the quality of people’s lives. The changing characteristics of land use will affect and challenge how a city is able to manage, organize, and plan for new developments and transportation. These challenges can be better addressed with effective methods of monitoring and predicting, which can enable optimal efficiency in how a growing city like Calgary, Canada, can perform. Using ontology in land use planning is a new initiative currently being researched and explored. In this regard, ontology incorporates relationships between the various entities of land use. The aim of this study is to present Land Use Change Ontology (LUCO) with a deep neural network for traffic prediction. We present a Land Use Change Ontology (LUCO) approach, using expressions of how the semantics of land use changes relate to the integration of temporal land use information. This study examines the City of Calgary’s land use data from the years 2001, 2010, and 2015. In applying the LUCO approach to test data, experimental outcomes indicated that from 2001 to 2015 residential land use increased by 30% and open space decreased by 40%. Forecasting traffic is increasingly essential for successful traffic modelling, operations, and management. However, traditional means for predicting traffic flow have largely assumed restrictive model architectures that have not controlled for the amounts of land use change. Inspired by deep learning methods and effective data mining computing capabilities, this paper introduces the deep learning Recurrent Neural Network (RNN) to predict traffic while considering the impact of land use change. The RNN was successful in learning the features of traffic flow under various land use change situations. Experimental results indicated that, with the consideration of LUCO, the deep learning predictors had better accuracy when compared with other existing models. Success of our modeling approach indicates that cities could apply this modeling approach to make land use transportation planning more efficient.

Flash floods pose a significant threat to Bangladesh; in particular, on 20 August 2024, the Feni district experienced a major flash flood, affecting more than 550,000 people and causing widespread damage. To effectively mitigate the impacts of flash floods, it is essential to conduct a comprehensive flash flood vulnerability assessment, incorporating multiple triggering factors. This study aims to assess flash flood vulnerability in the Feni District through a unique approach, integrating various dimensions of vulnerability. The study utilizes a geospatial methodology, employing the formula of vulnerability developed by UNESCO-IHE. Four dimensions of vulnerability were analyzed: social, physical, economic, and environmental. For each dimension, specific variables were selected to assess exposure, susceptibility, and resilience. Principal Component Analysis (PCA) was used to assign weights to these variables. The geospatial layers of influencing vulnerability factors were integrated together to create flash flood vulnerability maps of four dimensions. These were then overlaid to generate a composite flash flood vulnerability map. The analysis revealed a distinct spatial distribution of vulnerability across Feni District. In terms of environmental vulnerability due to flash flood, about 14% of the total area falls into the very highly vulnerable zone, whereas 13%, 8% and 5% of the study area were found to be very highly vulnerable regarding social, economic and physical aspects, respectively. The composite flash flood vulnerability map identified key vulnerability hotspots, with the most vulnerable unions (the smallest administrative unit in Bangladesh) being Feni Pourashava (68% very high), Sonagazi Paurashava (40% very high), and Nawabpur (32% very high), while the least vulnerable areas were Jailashkara (58% very low), Anandapur (81% very low), and Darbarpur (82% very low). The results show that the Feni District’s flash flood susceptibility varies significantly throughout the region, which provide crucial insights for policymakers and local authorities in order to identify vulnerability hotspots, prioritize interventions in vulnerable areas, enhance flash flood resilience, and implement adaptive strategies.

Finding a balance between economic growth and environmental protection is a significant challenge for contemporary cities. In the last 30 years, many large cities have started integrating sustainability concepts into planning processes to better manage land, resources, and urban activities. In contrast, Dhaka—the capital of Bangladesh with a population of approximately 24.65 million—has experienced rapid growth with a stronger emphasis on economic expansion than on environmental concerns. This has resulted in significant challenges in land use, housing, transportation, governance, and environmental health and management. As these challenges intensify, understanding how planning systems incorporate or fail to incorporate sustainability principles becomes increasingly critical. Despite this urgency, relatively few studies have explored the integration of spatial planning (SP) and sustainable development (SD) in Dhaka’s urban growth. The purpose of this research is to address this knowledge gap by systematically reviewing the existing literature to assess the extent of integration between sustainable development and spatial planning practices, identify key thematic areas and institutional constraints, and highlight limitations and opportunities for integrated urban planning. Using qualitative method, this study examined 92 sources of white literature and gray literature, categorized into six main themes and 21 subthemes that cover land use, housing, transportation, governance, and environmental management. The findings reveal that while many studies reference SD and SP together, their integration remains largely conceptual and fragmented, with no clearly articulated operational framework guiding planning practice in Dhaka. Moreover, this review identifies weaknesses in previous studies, highlights opportunities to better connect sustainability and planning and provide insights for policymakers and planners. Rather than proposing an implementable policy model, this review develops a conceptual synthesis that highlights strengths, weaknesses, and opportunities for improving SD–SP integration.

Rapid urbanization in South Asian coastal cities is systematically dismantling natural cooling infrastructure, driving unprecedented urban heat island (UHI) intensification with severe consequences for human health, energy systems, and urban livability. Despite growing research attention, comprehensive frameworks that simultaneously capture temporal UHI dynamics, machine learning-based thermal projections, and community-grounded validation remain scarce, particularly for secondary coastal cities in tropical developing regions. This study addresses these gaps by investigating UHI dynamics in Chattogram City Corporation (CCC), Bangladesh, through three integrated methodological pillars: (1) multi-temporal remote sensing analysis using Landsat 5 and 8 imagery spanning 2005–2025; (2) comparative evaluation of five machine learning algorithms (LightGBM, Random Forest, XGBoost, SVM, and MLP) for land use/land cover (LULC) classification and land surface temperature (LST) regression, with iterative scenario projections for 2029, 2033, and 2037; and (3) a structured public perception survey of 384 residents validated through participatory mapping and focus group discussions. Landsat analysis revealed dramatic LULC transformations: built-up areas expanded 88% (12,649 to 23,719 acres), while waterbodies declined 53.1% and vegetation decreased 21.9%. Mean LST increased by 9.09 °C (from 30.94 °C to 40.03 °C), with mean UHI intensity rising from 19.59 to 33.88 standardized units over two decades. LightGBM achieved optimal LULC classification (F1-weighted: 0.765) while Random Forest best predicted LST (RMSE: 1.51, R²: 0.809). Projections indicate continued thermal escalation, with mean LST reaching 43.64 °C and UHI intensity exceeding 37.41 standardized units by 2037. Persistent thermal hotspots were identified in the southwestern coastal corridor, western industrial belt, and central business district. Community survey data corroborated satellite-derived patterns, with 73.44% of respondents observing environmental degradation, yet only 22% aware of formal heat mitigation policies, and 87% supporting vegetation-based cooling interventions. This integrated framework advances urban thermal monitoring in tropical coastal cities and provides spatially targeted, community-endorsed evidence for climate-responsive urban planning.

Sediment management is defined as the strategic monitoring and control of erosion, transport, and deposition processes to maintain environmental and infrastructural stability. Terrestrial laser scanning (TLS) has emerged as a critical high-precision technology for monitoring sediment dynamics, erosion processes, and geomorphic change detection across diverse environments, including riverine, coastal, watershed, and infrastructure-related landscapes. While the field of TLS technology has seen significant advancements in recent years, including improvements in data accuracy, enhanced operational performance, artificial intelligence (AI), machine learning-based processing, and integration with other remote sensing tools such as unmanned aerial vehicles (UAVs) and satellite light detection and ranging (LiDAR), the study has focused on these developments. These advancements have further extended the application prospects of TLS technology. Despite these advancements, there remains a crucial need to systematically identify global research trends to identify the effectiveness, limitations, and knowledge gaps of TLS in sediment management. The methodological advantages and challenges of TLS applications provide insights into its gradual development role in enhancing sediment monitoring and environmental resilience. The objective of this study is to synthesize the current state of sediment management by conducting a systematic review of 108 peer-reviewed research papers retrieved from academic databases, including Google Scholar, ResearchGate, ScienceDirect, Scopus, and Web of Science, from 28 countries, published between 2000 and 2025. The study will evaluate the effectiveness of TLS methodologies in comparison to conventional techniques and management procedures, following the PRISMA 2020 guidelines. It will examine their capacity to enhance measurement accuracy, reduce error margins, and improve structural guidelines, particularly by advancing TLS technology through the integration of AI and machine learning (ML) algorithms. The findings of the study indicate that TLS and Iterative Closest Point (ICP) techniques can enhance the analysis of 3D models of dam deformation, ensuring improved structural monitoring and safety. The findings offer insights into the evolving role of TLS in sediment monitoring, emphasizing its potential for enhancing environmental management and climate resilience strategies. Furthermore, this review identifies future research directions to optimize TLS applications in sediment management through interdisciplinary approaches.