Two of the most disruptive technologies of the 21st century are quantum computing and artificial intelligence. Their intersection has led to the emergence of a new discipline referred to as Quantum Machine Learning (QML), which aims to enhance the capabilities of classical machine learning by leveraging the computational advantages of quantum devices. This paper provides a survey of the most advanced Quantum Machine Learning (QML) algorithms, including Quantum Support Vector Machines (QSVMs), Quantum k-nearest Neighbours (QkNN), Quantum Principal Component Analysis (QPCA), Quantum Neural Networks (QNNs), and Quantum Reinforcement Learning (QRL). The theoretical and practical status, as well as the empirical performance, of these algorithms, were summarised using a structured review method. The findings reveal a potential for speed-ups in classification, clustering, and optimisation among a range of applications, particularly for perfect quantum systems. However, hardware constraints, software irregularities, and training issues, such as barren plateaus, have limited the practical utility of this approach. Applications of QML in areas such as disaster preparedness and management, drug discovery, environmental sustainability, urban planning methodology, NLP, and finance demonstrate both the potential and current limitations of QML, with most applications still at the proof-of-concept level. In this review, we conclude that QML could be revolutionary, but its feasibility ultimately relies on improvements in physical hardware, the robustness of algorithms, and the standardisation of benchmarks.

Nigeria’s economic diversification hinges on transforming its urban centers into aesthetically vibrant, sustainable hubs, moving beyond reliance on crude oil. Advanced GeoAI technologies—encompassing satellite imagery, predictive analytics, and real-time urban monitoring—offer transformative potential to redeem the environmental aesthetics of Nigerian cities, fostering tourism, employment, and foreign exchange. Cities like Lagos, once celebrated for their cultural and architectural heritage, now grapple with aesthetic decline driven by rapid urbanization, inadequate planning, environmental degradation, and socio-economic disparities. This article critically examines the challenges undermining urban environmental quality in Nigeria, focusing on hotspot cities like Lagos, Port Harcourt, Abuja, Kano, among others, as a case study, and identifies gaps in knowledge, infrastructure, and institutional support that exacerbate these issues. Key obstacles include limited geospatial data, high costs of GeoAI adoption, technical expertise deficits, and socio-cultural resistance to technology-driven urban interventions. Employing a multidisciplinary approach such as a systematic literature review, the study proposes novel contributions to innovative solutions, integrating sustainable urban planning, inclusive governance, public participation, and advanced geospatial technologies to restore and enhance urban beauty. It highlights the role of GeoAI in enabling precise urban design, green space optimization, and infrastructure monitoring, drawing on best practices from cities like Singapore and Kigali that have successfully revitalized their urban aesthetics. However, successful implementation in Nigeria demands robust policy frameworks, capacity building, and equitable technology access to bridge systemic inequalities. Through addressing these challenges, this article provides a new roadmap for policymakers, city planners, and stakeholders to leverage GeoAI for urban renewal, ensuring Nigerian cities reclaim their aesthetic allure while fostering economic and environmental resilience. This study's recommendations underscore that structuring Nigeria’s urban environmental aesthetics requires not only technological innovation but also a commitment to social equity and community-driven urban transformation.

Fire outbreaks have long posed significant threats to human life, property, and the environment, often resulting in substantial economic and social losses. In this study, we present the design and implementation of an Internet of Things (IoT)-based fire-fighting robot developed to enhance early fire detection and automated suppression. The system was built around a microcontroller unit and integrates a suite of sensors for autonomous operation, enabling real-time fire detection. A motor driver controls the robot’s mobility, while a relay circuit manages the actuation of a direct current (DC) pump connected to a water jet spray mechanism for extinguishing fires. The robot also supports manual override functionality through a web-based interface accessible via smartphones or personal computers, incorporating a live camera feed for remote navigation and control. The prototype design increased response time by 30%, significantly improving the speed of fire suppression interventions. This improvement directly translated to a higher chance of containing fires before they escalate, reducing potential damage and increasing safety for occupants and first responders. We recommended that future developments expand the system's capabilities to address other classes of fires (e.g., Class B and C), beyond the current focus on Class A fires. Additionally, incorporating cloud-based infrastructure can improve real-time data processing, storage, analytics, and multi-location deployment, enabling smarter and more coordinated fire management.

The increasing frequency, intensity, and complexity of natural and human-induced disasters have exposed critical limitations in conventional emergency response systems, creating an urgent need for rapid, intelligent, and resilient disaster management solutions. Recent advances in autonomous technologies, including unmanned aerial vehicles (drones), autonomous ground vehicles, marine robots, and artificial intelligence (AI)-enabled decisionsupport systems, have transformed disaster operations by enhancing real-time situational awareness, search and rescue, infrastructure assessment, medical supply delivery, and humanitarian logistics. This narrative review critically synthesizes the current evidence on the evolution and applications of autonomous systems across the disaster management cycle, encompassing preparedness, emergency response, recovery, and humanitarian logistics. It examines recent technological advances while evaluating the technical, operational, regulatory, ethical, and economic barriers that continue to limit large scale implementation. Building on these insights, the review proposes the Adaptive Autonomous Disaster Response Ecosystem (AADRE) Framework, a novel conceptual model that integrates AI-driven decision support, heterogeneous autonomous platforms, human expertise, humanitarian logistics, and continuous learning into a unified disaster response ecosystem. The framework provides a scalable roadmap for improving coordination, interoperability, adaptive decision-making, and disaster resilience. The findings highlight the need for harmonized governance, interoperable digital infrastructure, multidisciplinary collaboration, and implementation-focused research to facilitate the safe and effective integration of autonomous technologies into future disaster management systems, with important implications for policymakers, emergency management agencies, humanitarian organizations, and researchers.

Flooding is the most common and damaging natural disaster in Illinois, largely due to climate change and human activities. This study explores the integration of Geographic Information Systems (GIS) in flood risk management across the state, focusing on the mapping of high-risk areas along major river corridors such as the Mississippi and Illinois Rivers. By utilizing survey data drawn from federal and state agencies participating in the Illinois Silver Jackets program, the research develops an interactive GIS-based flood map that visualizes vulnerable communities and supports dynamic spatial analysis. The GIS application/tool serves several functions: identifying flood-prone zones, enhancing emergency preparedness, informing land use and infrastructure planning, and improving public risk communication. The GIS tool uses real-time, user-friendly interface enables both technical and non-technical stakeholders to easily engage with flood data, fostering community resilience and informed decision-making. The findings highlight the value of collaborative, data-driven approaches in advancing proactive flood mitigation strategies throughout Illinois.

Flooding is a major and persistent risk to cities and riverine communities worldwide. We conducted a systematic review to assess how remote sensing is being applied in flood control and stormwater management in operational contexts. Following PRISMA 2020 guidelines, we screened studies published between 2000 and 2024 and identified 20 empirically rigorous articles that reported quantitative validation metrics using satellite, airborne, or UAV-based data. Across these studies, synthetic aperture radar (SAR), particularly Sentinel-1, emerged as the most reliable platform for all-weather flood extent mapping, with typical accuracies above 85%. Optical sensors such as Landsat and Sentinel-2 performed well under cloud-free conditions and were especially useful for surface water detection, vegetation dynamics, and monitoring green stormwater infrastructure through indices such as NDVI. LiDAR provided the most precise topographic information, generating centimeter-level digital elevation models that significantly improved urban flood modeling. UAV platforms offered ultra-high spatial resolution (< 10 cm), supporting localized damage assessment, infrastructure inspection, and post-event validation. We found that machine learning approaches, including convolutional neural networks and vision transformers, consistently outperformed traditional threshold-based methods, often achieving F1-scores above 0.90. However, challenges remain, particularly limited validation data, weak uncertainty quantification, scale mismatches, and difficulties with operational integration. Overall, our review shows that remote sensing has become a practical and indispensable tool for flood and stormwater management when sensor platforms are strategically combined.