Search

Urban sewer system management is challenging due to its higher vulnerability to flooding caused by rapid urbanization and climate change. For local decision-makers, storm water management is essential for urban planning and development. Therefore, the main objective of this study is to develop a numerical model for the sewerage network of the central catchment area of Algiers since it has experienced frequent overflows during the winter season. For this purpose, to model the sewerage networks, the model was built by coupling ArcGIS with MIKE URBAN. Its calibration and validation were performed using real-time measurements with a time step of 15 min. The model was evaluated by several statistical indicators, such as the coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), and percent bias (PBIAS). The model results showed acceptable model performance, with an NSE superior to 0.50, R2 of approximately 0.63, RMSE of 7%, and PBIAS of 10% during the validation of the model. The performance parameters prove the reliability of the developed model. The employed model can be applied in other regions and could be helpful for policymakers and managers to improve flood mitigation measures based on the model prediction of the sewerage network. HIGHLIGHT Facilitate management by utilizing modeling to protect people and their property.; Ensure that decisions are made promptly within the framework of emergency response plans in case of an alert.; Develop a reliable digital model for studying the impact of climate change on the rainwater drainage system. Haut du formulaire.;

The contagious COVID-19 has recently emerged and evolved into a world-threatening pandemic outbreak. After pursuing rigorous prophylactic measures two years ago, most activities globally reopened despite the emergence of lethal genetic strains. In this context, assessing and mapping activity characteristics-based hot spot regions facilitating infectious transmission is essential. Hence, our research question is: How can the potential hotspots of COVID-19 risk be defined intra-cities based on the spatial planning of commercial activity in particular? In our research, Zayed and October cities, Egypt, characterized by various commercial activities, were selected as testbeds. First, we analyzed each activity's spatial and morphological characteristics and potential infection risk based on the Centre for Disease Control and Prevention (CDCP) criteria and the Kriging Interpolation method. Then, using Google Mobility, previous reports, and semi-structured interviews, points of interest and population flow were defined and combined with the last step as interrelated horizontal layers for determining hotspots. A validation study compared the generated activity risk map, spatial COVID-19 cases, and land use distribution using logistic regression (LR) and Pearson coefficients (rxy). Through visual analytics, our findings indicate the central areas of both cities, including incompatible and concentrated commercial activities, have high-risk peaks (LR = 0.903, rxy = 0.78) despite the medium urban density of districts, indicating that urban density alone is insufficient for public health risk reduction. Health perspective-based spatial configuration of activities is advised as a risk assessment tool along with urban density for appropriate decision-making in shaping pandemic-resilient cities.

AbstractThis study aims to examine three machine learning (ML) techniques, namely random forest (RF), LightGBM, and CatBoost for flooding susceptibility maps (FSMs) in the Vietnamese Vu Gia-Thu Bon (VGTB). The results of ML are compared with those of the rainfall-runoff model, and different training dataset sizes are utilized in the performance assessment. Ten independent factors are assessed. An inventory map with approximately 850 flooding sites is based on several post-flood surveys. The inventory dataset is randomly split between training (70%) and testing (30%). The AUC-ROC results are 97.9%, 99.5%, and 99.5% for CatBoost, LightGBM, and RF, respectively. The FSMs developed by the ML methods show good agreement in terms of an extension with flood inundation maps developed using the rainfall-runoff model. The models’ FSMs showed 10–13% of the total area to be highly susceptible to flooding, consistent with RRI's flood map. The FSMs show that downstream areas (both urbanized and agricultural) are under high and very high levels of susceptibility. Additionally, different sizes of the input datasets are tested to determine the least number of data points having acceptable reliability. The results demonstrate that the ML methods can realistically predict FSMs, regardless of the number of training samples.

Increasing flood risk due to rapid urbanization and climate change calls for improved integration between flood risk management and spatial planning processes to enhance the resilience of cities, including in Egypt. Although much work has been conducted on flood impact in Egypt, the gap in integrating flood risk assessment with spatial planning practices has not been discussed in academia. In practice, flood risk assessment is not mandatory for local-level spatial planning projects in Egypt, resulting in increased flood risk. This study examines the causes of this gap and proposes possible solutions that increase flood resiliency. A mixed-method approach was utilized based on a questionnaire survey with local stakeholders in academic and professional categories. The results reveal reasons for the gap, including issues related to the realization of the role of spatial planning in flood risk reduction, lack of coordination between the authorities responsible for developing the flood hazard maps and the spatial planning authority, availability and accessibility of the required data, and subjectivity of conducted flood analyses. Four key recommendations pertain to building an operational framework for integrating flood risk assessment in spatial planning, improving stakeholder awareness and collaboration, strengthening risk communication, and improving both quality and access to data. These measures will help to overcome the identified difficulties and enhance the integration between spatial planning and flood risk assessment, effectively increasing their flood resilience.

Under rapid urbanization-induced global warming and resource depletion, growing interest in zero-energy building (ZEB) and zero-emission building (ZEB) technologies have emerged globally to improve energy performance in homes and shape sustainable cities. Although several countries have released ZEB-enhanced strategies and set national standards and policies to promote ZEBs, construction projects are still limited to demonstration projects. This paper reviews global ZEB activities and state-of-the-art technologies for energy-efficient residential building technologies [based on an evaluation of 40 residential buildings]. Over 40 residential buildings on different continents were reviewed, and their technical details and performance were evaluated. Our results show that 62.5% of the buildings achieved the +ZEB standard, 25% of the buildings were net-zero energy buildings, and only 12.5% of the buildings were near-zero energy buildings. Solar PV is the most widely used renewable energy source in the studied cases, while in warmer climates, advanced cooling technologies and heat pumps are the preferred technologies. A building envelope and thermal ventilation with heat recovery are essential in cold climates. Our systematic analysis reveals that the thermal performance of the building envelope and solar energy are the most effective mechanisms for achieving energy efficiency and shaping sustainable cities.

Flood risk mapping forms the basis for disaster risk management and the associated decision-making systems. The effectiveness of this process is highly dependent on the quality of the input data of both hazard and vulnerability maps and the method utilized. On the one hand, for higher-quality hazard maps, the use of 2D models is generally suggested. However, in ungauged regions, such usage becomes a difficult task, especially at the microscale. On the other hand, vulnerability mapping at the microscale suffers limitations as a result of the failure to consider vulnerability components, the low spatial resolution of the input data, and the omission of urban planning aspects that have crucial impacts on the resulting quality. This paper aims to enhance the quality of both hazard and vulnerability maps at the urban microscale in ungauged regions. The proposed methodology integrates remote sensing data and high-quality city strategic plans (CSPs) using geographic information systems (GISs), a 2D rainfall-runoff-inundation (RRI) simulation model, and multicriteria decision-making analysis (MCDA, i.e., the analytic hierarchy process (AHP)). This method was implemented in Hurghada, Egypt, which from 1996 to 2019 was prone to several urban flood events. Current and future physical, social, and economic vulnerability maps were produced based on seven indicators (land use, building height, building conditions, building materials, total population, population density, and land value). The total vulnerability maps were combined with the hazard maps based on the Kron equation for three different return periods (REPs) 50, 10, and 5 years to create the corresponding flood risk maps. In general, this integrated methodology proved to be an economical tool to overcome the scarcity of data, to fill the gap between urban planning and flood risk management (FRM), and to produce comprehensive and high-quality flood risk maps that aid decision-making systems.

Urban growth, extreme climate, and mismanagement are crucial controlling factors that affect flood vulnerability at wadi catchments. Therefore, this study attempts to understand the impacts of these three factors on the flash flood vulnerability in different climatic regions in Egypt. An integrated approach is presented to evaluate the urban growth from 1984 to 2019 by using Google Images and SENTINEL-2 data, and to develop hazard maps by using a rainfall-runoff-inundation model (RRI). Annual rainfall trend analysis was performed to evaluate the temporal variability trend. The hazard maps that were created were classified into three categories (low, medium, and high) and integrated with the urban growth maps to evaluate the impacts on the flood-vulnerable areas. The results show a significant increase in urban growth resulting in an increase of prone areas for flood hazards over time. However, the degree of this hazard is mainly related to growth directions. Mismanagement affects urban growth directions in both planned and unplanned growth, whether by loss of control over unplanned growth or by deficiencies in approved plans. The rainfall analysis showed that there is no explicit relationship to increases or decreases in the flood vulnerable areas. An urban planning approach is recommended for risk reduction management based on a comprehensive study considering such factors.