The synthesis shows that AI is most mature for multimodal sensing, smoke/fire detection, susceptibility mapping, and short-horizon forecasting, but less mature for prospective decision-support validation, cross-ecosystem transfer, and operational accountability.
Abstract
Forest fire prevention increasingly depends on translating ecological monitoring into earlier, more reliable decisions about ignition risk, fuel condition, spread potential, and management intervention. This critical review evaluates artificial intelligence (AI) for forest fire prevention through full-text extraction of core studies and contextual synthesis of foundational fire-science literature. The evidence base contains 179 unique references, including an AI-focused corpus, classical deterministic and probabilistic fire-danger and spread models, global ignition and lightning studies, remote-sensing and fuel-moisture foundations, decision-support tools, and governance literature. We define prevention-facing AI as systems that support pre-ignition or pre-escalation decisions and compare studies by data source, model design, validation protocol, forecast horizon, transferability, interpretability, and management action. The synthesis shows that AI is most mature for multimodal sensing, smoke/fire detection, susceptibility mapping, and short-horizon forecasting, but less mature for prospective decision-support validation, cross-ecosystem transfer, and operational accountability. AI is therefore most useful when it is hybrid, interpretable, and deployment-aware: it should complement established fire-weather and spread-model baselines while converting ecological observations into timely and actionable prevention judgments.
AI-based decision support is moving into fire practice and governance, where it is used to prioritise inspections, analyse building and community risk, examine station coverage, support evacuation planning, interpret warnings, and explore fire scenarios. These tools can extend analytical capacity, but they also create a decision role migration problem: an output developed for prediction, prioritisation, warning, simulation, or planning may later be treated as clearance, justification, or authority. Existing fire model evaluation guidance recognises that validation is use-specific; AI systems add a further challenge because outputs can migrate across dashboards, reusable software components, interfaces, and institutional procedures. This article develops a role-sensitive framework for bounded deployment of AI decision support in urban fire risk management. The framework classifies AI outputs by epistemic role, decision proximity, validation basis, temporal coupling, consequence asymmetry, and governance explicitness. Its central synthesis is that evidence sufficient to warn may be insufficient to clear. Probabilistic outputs can support screening, investigation, prioritisation, and scenario analysis; permissive or safety-proximate claims require stronger assurance, uncertainty communication, fallback rules, and explicit authority allocation. The contribution is a governance logic for keeping exploratory, advisory, policy-shaping, and safety-proximate AI roles separate in urban fire management and policy formulation.
Natural disasters with tremendous impacts on human lives, infrastructure, and ecosystems are frequent all over the world, which calls for intelligent, data-driven decision support systems for early diagnosis and effective crisis management. This paper demonstrates a Natural Disaster Diagnosis and Crisis Management System design and development featuring real-time environmental sensing, technological monitoring, and pre-emptive response planning within a unified decision-support framework. The proposed system include predict disasters using machine learning. It follows a modular architecture that integrates analytical risk assessment, formulation of preventive strategy, and dynamic action planning through an interactive GUI. NDDCMS structures disaster management into five operational phases, namely disaster diagnosis, early warning indicators, response during the disaster, post-disaster recovery, and preventive planning. Each phase integrates stepwise risk indicators and decision inputs to support officials and community response teams. It relies on quantifiable environmental parameters (e.g., precipitation, soil moisture, wind speed, and temperature fluctuation) and corresponding technological sensing mechanisms for the classification of risk levels and the improvement of early warning reliability. By focusing on a user-centered design and data-driven workflow, the system advances situational awareness, hastens decision-making, and closes the gap between disaster prediction and effective response. This framework enhances national and local-level disaster resilience by supporting viable risk reduction and crisis management strategies.
EL-Alfy A.E, Esmat Mona, Sakr Hagar· International Journal of Sci...· 0 citations
Artificial Intelligence (AI) has become one of the disruptive technologies in environmental management, providing new ways of monitoring the ecosystem, optimising resources, and making environmentally friendly decisions. This review examines how AI can potentially be used in different environmental areas, such as biodiversity monitoring, ecological change predictions, and how natural resources can be better distributed. Thanks to the incorporation of AI technologies like machine learning, remote sensing, and Internet of Things sensors, real-time data acquisition and analysis can now be conducted, which allows for more informed and timely decisions related to the environment. Predictive models based on AI offer useful information on the possible effects of climate change, loss of biodiversity, and habitat degradation to enable policymakers to engage in proactive environmental management. Nonetheless, the use of AI in environmental governance does not come without its problems. Challenges like data quality and availability, the complexity of ecosystems, ethics, and technological constraints need to be resolved to guarantee the effectiveness and equity of AI-based solutions. This paper addresses these issues and outlines future research opportunities, focusing on the importance of interdisciplinary cooperation and building a strong data infrastructure to maximize the potential of AI. With these obstacles resolved, AI has the potential to become a key factor in sustainability and resilience in ecosystems around the world.
Zhenpeng Liu· Journal of Environmental &am...· 0 citations
Forest fires represent one of the most ecologically destructive and economically costly natural hazards, with global wildfire activity destroying an estimated 350 to 450 million hectares of vegetation annually and contributing significantly to greenhouse gas emissions, biodiversity loss, and air quality degradation. In India, the Forest Survey of India recorded over 2.23 lakh forest fire alert points during the 2023 fire season alone, with the Eastern Ghats, Western Ghats, and Central Indian forest belts being recurrently affected. Conventional forest fire monitoring relies predominantly on satellite-based thermal anomaly detection, which suffers from coarse temporal resolution (revisit intervals of 1–12 hours depending on satellite), cloud cover interference, and an inherent detection lag that allows fires to spread substantially before alerts reach ground response teams. This paper presents an integrated AI-powered forest fire detection and prediction system that combines a ground-based wireless sensor network for early smoke and thermal anomaly detection, a deep learning-based fire risk prediction model that forecasts fire probability up to 72 hours in advance using meteorological and vegetation moisture data, and a real-time alert dissemination pipeline for forest department response coordination. The detection module employs a lightweight CNN trained on multi-spectral sensor fusion data (temperature, humidity, CO concentration, and particulate matter) deployed across a wireless mesh network, achieving fire event detection within 4 minutes of ignition with 95.8% accuracy. The prediction module uses a Random Forest-LSTM ensemble trained on 12 years of Forest Survey of India fire occurrence records combined with IMD meteorological data, achieving fire risk forecasting accuracy of 89.4% for the 72-hour prediction window. Field-calibrated simulation results based on deployment parameters from the Sathyamangalam Tiger Reserve forest range in Tamil Nadu demonstrate that the proposed system reduces fire detection-to-alert latency by 91% compared to satellite-only monitoring and provides actionable early warning that extends the intervention window for forest department fire suppression teams by an average of 6.4 hours.
Keywords — Forest Fire Detection, Wildfire Prediction, Wireless Sensor Network, CNN, LSTM, Random Forest, IoT, Fire Risk Index, Remote Sensing, Environmental Monitoring
Agesta Jenifer A Agesta Jenifer A, Vignesh Perumal L S Vignesh Perumal L S, JaiSoorya J JaiSoorya J et al.· International Scientific Jou...· 0 citations
Traditional fire detection technologies for buildings are no longer adequate for the fire prevention and control needs of complex structures, while emerging artificial intelligence technologies have become the core path to break through the bottlenecks in this industry. Existing reviews suffer from non-standardized paradigms, one-sided scopes, insufficient methodological evaluation, incomplete time coverage, and a lack of engineering orientation, failing to meet the evidence-based research needs of the field. This study strictly followed the PRISMA 2020 systematic review guidelines, selected relevant SCI papers from the Web of Science Core Collections from 1 January 2016 to 30 March 2026, in JCR Q1 and Q2, and finally included 221 valid papers; it systematically carried out bibliometric analysis and technical system sorting. The results showed that (1) the number of publications in this field showed a significant exponential upward trend, with China accounting for 52% of the research output, ranking first in the world, and (2) convolutional neural networks and YOLO series algorithms are the mainstream application technologies in the field. The study clarified the performance differences, advantages, and disadvantages, and applicable scenarios of various artificial intelligence algorithms. This study identified the existing technical and methodological limitations in the field and explored the core research directions for the future, and provided evidence-based support for the academic research and engineering implementation of artificial intelligence in the field of building fire detection and prevention.
Flood intensity and frequency are expected to continue rising due to climate change, necessitating improved prevention measures. Despite the growing body of research, communities and economies remain severely affected by the consequences of floods, underscoring the need to examine how flood models are understood and translated into disaster preparedness and mitigation strategies. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews (PRISMA-ScR), a total of 824 articles published between 1980 and March 2026 were selected from the Web of Science, Scopus, and IEEEXplore. The review maps advance across several technologies, including hydrological modelling, geospatial analysis, multi-criteria decision analysis (MCDA), and artificial intelligence (AI), and explores how heuristic and metaheuristic approaches couple flood management and analysis. An exponential increase in the adoption of AI and MCDA was identified, driving an exponential increase in flood susceptibility and flood risk mapping across the reviewed literature. This review indicates that modern technologies play a decisive role in advancing flood applications; however, key methodological limitations persist. These limitations include a limited integration of urban infrastructure, stormwater drainage, and groundwater variability into flood prediction and assessment. A lack of coherent methodologies connecting discharge, surface runoff, urban flash floods, and prevention measures remains a significant gap. The translation of rainfall-runoff and discharge prediction into flood analysis is an ongoing challenge. Future research should therefore prioritise integrating runoff potential, discharge modelling, surface water drainage, and groundwater variability into flood applications. A decentralised methodological approach is recommended to empower state and local governments to implement adaptive, context-specific flood solutions, thereby enhancing early warning systems, urban planning, and emergency response. Furthermore, this review surfaces critical questions warranting focused investigation by global research communities: (i) relative importance of surface runoff as a flood-generating mechanism across different hydrological settings, and it weighting in flood susceptibility and risk mapping; (ii) hydrological distinctions and predictive possibilities between rainfall-runoff and observed streamflow modelling, and under what conditions do they best explain observed flooding, and (iii) do runoff calibration reliably explain flood extents and severity in urban settings and what methodological framework can make this operationally viable in data scarce environment?. Addressing these questions can advance the integration of hydrological processes and practical flood prediction and management.
Irvin D. Shandu, M. Gebreslasie, Sifiso Xulu et al.· Frontiers in Water· 0 citations