The increasing demand for clean and reliable water resources, coupled with the growing threat of water pollution, has made real-time water quality (WQ) monitoring and assessment a critical priority in many urban areas. Urban environments encounter substantial challenges in maintaining WQ, driven by factors such as rapid population growth, industrial expansion, and the impacts of climate change. Effective real-time WQ monitoring is essential for safeguarding public health, promoting environmental sustainability, and ensuring adherence to regulatory standards. The rapid advancement of Internet of Things (IoT) sensor technologies and smartphone applications presents an opportunity to develop integrated platforms for real-time WQ assessment. Advances in the IoT provide a transformative solution for WQ monitoring, revolutionizing the way we assess and manage our water resources. Moreover, recent developments in Location-Based Services (LBSs) and Global Navigation Satellite Systems (GNSSs) have significantly enhanced the accessibility and accuracy of location information. With the proliferation of GNSS services, such as GPS, GLONASS, Galileo, and BeiDou, users now have access to a diverse range of location data that are more precise and reliable than ever before. These advancements have made it easier to integrate location information into various applications, from urban planning and disaster management to environmental monitoring and transportation. The availability of multi-GNSS support allows for improved satellite coverage and reduces the potential for signal loss in urban environments or densely built environments. To harness this potential and to enable the seamless integration of the IoT and LBSs for sustainable WQ monitoring, a systematic literature review was conducted to determine past trends and future opportunities. This research aimed to review the limitations of traditional monitoring systems while fostering an understanding of the positioning capabilities of LBSs in environmental monitoring for sustainable urban development. The review highlights both the advancements and challenges in using the IoT and LBSs for real-time WQ monitoring, offering critical insights into the current state of the technology and its potential for future development. There is a pressing need for an integrated, real-time WQ monitoring system that is cost-effective and accessible. Such a system should leverage IoT sensor networks and LBSs to provide continuous monitoring, immediate feedback, and spatially dynamic insights, empowering stakeholders to address WQ issues collaboratively and efficiently.

The study proposes a novel method for modeling traffic volumes at the road segment level of large-scale urban road networks. This study has been placed in a milieu where existing methods on modeling vehicular traffic volume are hampered by data and cost constraints, especially in developing countries. Emerging traffic modeling methods, based on centrality and space syntax provides a technically-efficient approach to overcome the above-mentioned constraints. Nevertheless, those methods are yet to be popular among practitioners due to limited accuracy and validity. This study modifies the existing methods and validates in five case cities to make them practice-ready. Findings of this study indicated that the proposed method is competent enough to estimate traffic volume of road segments on a par with the internationally accepted standards. •The proposed method combines two network centrality measures abstracting the traffic volume on a road segment as the sum of origin-destination trips (i.e., Closeness-Centrality) and pass-by trips (i.e., Betweenness-Centrality).•The study modifies the 'distance' variable in the existing formula as 'path-distance' which captures topological and mobility characteristics of roads.•The method does not require extensive data and can be implemented by utilizing publicly available open-source network analysis software, hence, ideal for resource-scarce situations.

Assessing the influence of urban density on surface runoff volume is vital for guiding the built-form expansions toward flood-resilient cities. This paper attempts to develop a spatial simulation framework to assess the impact of urban density on the level of surface runoff (SR), at the scale of the micro-watershed. This paper proposes a spatial simulation framework that comprehensively captures the influence of urban density dynamics over surface runoff. The simulation model consists of 13 proxies of urban density that are identified through a systematic literature review. The model is formulated through three case applications in Colombo, Sri Lanka; and validated statistically and empirically with reference to flooding events that occurred in 2021–2022. The possible planning interventions for reducing urban flooding are analyzed through an AI-based application of Decision Tree Analysis. The model results indicated that impervious coverage, open space ratio, and road density have the most significant impact on surface runoff volumes in selected micro-watersheds. The decision-making process for planning the built environment for reducing urban flooding is demonstrated by three possible density control options with a prediction accuracy of 98.7%, 94.8%, and 93.5% respectively. This contributes a novel framework to capture the density dynamics of built form in surface runoff simulations by three density areas (3Ds): density, diversity, and design; and to demonstrate the decision-making process for controlling the density of built form in reducing urban flooding.

The study proposes a framework to model the three-dimensional relationship among density, land use, and accessibility in urban areas constructively contributing to overcome the limitations noted in the domains of urban planning and transport planning. First, most of the existing studies have focused on the topological characteristics in capturing the accessibility, but a limited attention has been given on measuring the accessibility by considering both topological and roadway characteristics. Second, the existing research studies have acknowledged the relationship among density, land use, and accessibility while a limited attention has been given to develop a modeling framework to capture the three-dimensional relationship. The modelling framework was tested in three urban areas in Sri Lanka. The research first analyzed the three-dimensional relationship among density, land use, and accessibility in the case studies. Then, the study developed a set of regression models to capture the density from the land use and accesability. The proposed model recorded a satisfactory level of accuracy (i.e., R² > 0.70) on a par with internationally accepted standards. The relationship was further elaborated through a decision tree analysis and 4D plot diagrams. Findings of the study can be utilized to model the density of a given land use and the correspondent accessibility scenarios. The proposed model is capable of quantifying the impact of the changes in the density correspondent to the accessibility and land use. Therefore, the study concludes that this will be an effective tool for decision-makers in the fields of land-use planning and transport planning for scenario building, impact analysis, and the formulation of land use zoning and urban development plans aiming at the overarching sustainability of future cities.