Digital Twin (DT) technology has emerged as a transformative tool in urban flood risk management (UFRM), enabling real-time data integration, predictive modeling, and decision support. This systematic review synthesizes existing literature to evaluate the scientific impact, technological advancements, and practical applications of DTs in UFRM. Using the PRISMA 2020 framework, we retrieved 1085 records (Scopus = 85; Web of Science = 1000), merged and deduplicated them using DOI and fuzzy-matched titles, screened titles/abstracts, and assessed full texts. This process yielded 85 unique peer-reviewed studies published between 2018 and 2025. Key findings highlight the role of remote sensing (e.g., satellite imagery, IoT sensors) in enhancing DT accuracy, the integration of machine learning for predictive analytics, and case studies demonstrating reduced flood response times by up to 40%. Challenges such as data interoperability and computational demands are discussed, alongside future directions for scalable, AI-driven DT frameworks. This review identifies key technical and governance challenges while recommending the development of modular, AI-driven DT frameworks, particularly tailored for resource-constrained regions.

This study employs a logistic regression model to offer an in-depth understanding of disparities in the access to essential urban services, specifically focusing on electricity and Internet services, in Nouakchott, Mauritania. Through a comprehensive analysis of demographic, geographic, and socioeconomic data, we identify key determinants of access to these utilities. Our findings reveal that the geographic location within the city, particularly in the western regions, and home ownership, significantly bolster the likelihood of having electricity. Conversely, women are found to be disadvantaged in this regard. For Internet access, income level and education, particularly at the Bachelor’s level, emerged as significant predictors. This research not only sheds light on the intricate landscape of service provision in Nouakchott but also offers actionable insights for equitable development. These results empower both policymakers and citizens, marking a step toward transforming Nouakchott into a smarter, more inclusive urban environment.

Drought monitoring is a critical environmental challenge, particularly in regions where irrigated agricultural intensification and urban expansion pressure water resources. This study assesses the impact of these activities on drought dynamics in Morocco’s Oum Er-Rbia (OER) watershed from 2002 to 2022, using the newly developed Watershed Integrated Multi-Drought Index (WIMDI), through Google Earth Engine (GEE). WIMDI integrates several drought indices, including SMCI, ESI, VCI, TVDI, SWI, PCI, and SVI, via a localized weighted averaging model (LOWA). Statistical validation against various drought-type indices including SPI, SDI, SEDI, and SMCI showed WIMDI’s strong correlations (r-values up to 0.805) and lower RMSE, indicating superior accuracy. Spatiotemporal validation against aggregated drought indices such as VHI, VDSI, and SDCI, along with time-series analysis, confirmed WIMDI’s robustness in capturing drought variability across the OER watershed. These results highlight WIMDI’s potential as a reliable tool for effective drought monitoring and management across diverse ecosystems and climates.

The advancement to Urban 4.0 requires urban digitization and predictive maintenance of infrastructure to improve efficiency, durability, and quality of life. This study aims to integrate intelligent technologies for the predictive maintenance of photovoltaic panel systems, which serve as essential smart city renewable energy sources. In addition, we employ vision transformers (ViT), a deep learning architecture devoted to evolving image analysis, to detect anomalies in PV systems. The ViT model is pre-trained on ImageNet to exploit a comprehensive set of relevant visual features from the PV images and classify the input PV panel. Furthermore, the developed system was integrated into a web application that allows users to upload PV images, automatically detect anomalies, and provide detailed panel information, such as PV panel type, defect probability, and anomaly status. A comparative study using several convolutional neural network architectures (VGG, ResNet, and AlexNet) and the ViT transformer was conducted. Therefore, the adopted ViT model performs excellently in anomaly detection, where the ViT achieves an AUC of 0.96. Finally, the proposed approach excels at the prompt identification of potential defects detection, reducing maintenance costs, advancing equipment lifetime, and optimizing PV system implementation.

This paper examines how demographic characteristics, institutional structures, and livelihood strategies shape household resilience to climate variability and drought in West Pokot County, one of Kenya’s most climate-vulnerable arid and semi-arid lands (ASALs). Using a mixed-methods approach, it combines household survey data with three statistical techniques: Multinomial Logistic Regression (MLR) assesses the influence of gender, age, and education on livestock ownership and livelihood choices; Multiple Correspondence Analysis (MCA) reveals patterns in institutional access and adaptive practices; and Stepwise Linear Regression (SLR) quantifies the relationship between resilience strategies and agricultural productivity. Findings show that demographic factors, particularly gender and education, along with access to veterinary services, drought-tolerant inputs, and community-based organizations, significantly shape resilience. However, trade-offs exist: strategies improving livestock productivity may reduce crop yields due to resource and labor competition. This study recommends targeted interventions, including gender-responsive extension services, integration of indigenous and scientific knowledge, improved infrastructure, and participatory governance. These measures are vital for strengthening resilience not only in West Pokot but also in other drought-prone ASAL regions across sub-Saharan Africa.

Access to waste management services is crucial for urban sustainability, impacting public health, environmental well-being, and overall quality of life. This study employs logistic regression analysis on survey data collected from 1,032 household heads residing in Nouakchott, the capital of Mauritania. The survey investigated key household factors that determine access to waste management services. The findings reveal a significant interplay among waste service provision, the presence of cisterns, housing type and size, and access to electricity. Socioeconomic disparity in service access, with poorer housing formats like shacks receiving substandard services. In contrast, areas with robust electrification report better service access, although inconsistencies remain amid power outages. The research highlights the challenges faced by Riyadh municipality, particularly rapid growth and inadequate infrastructure, which hinder waste management efficiency. Overall, the results not only illuminate Nouakchott’s unique challenges in service provision but also propose actionable recommendations for a sustainable urban future. These recommendations aim to inform and guide targeted policies for improving living conditions and environmental sustainability in urban Mauritania.