As extreme heat events increase in frequency, intensity, and duration due to climate change, forecasting these events has become vital for early warning systems, public health preparedness, and climate adaptation strategies, especially in parts of the world that are already subject to extreme heat, such as tropical regions. In recent years, machine learning (ML) has increasingly been applied to environmental and meteorological data to improve the prediction of heatwaves and extreme heat events. This scoping review examines global peer-reviewed literature on the application of ML techniques for extreme heat prediction using environmental variables. This includes heatwave prediction, environmental and meteorological predictors used in these models, and the geographical distribution of existing research. A total of 23 peer-reviewed studies meeting the inclusion criteria were included in the review, following the PRISMA-ScR guidelines. The findings indicate that artificial neural networks and random forest models were most frequently reported as high performing within individual studies. However, direct comparisons across studies are limited by heterogeneity in prediction targets, validation strategies, lead times, heatwave definitions, and performance metrics. Temperature-related variables, especially maximum temperature, were consistently identified as the most influential predictors across studies. Furthermore, the evidence base was heavily concentrated in Europe, Asia, and North America, with comparatively limited representation from low- and middle-income countries respective to population, despite these regions often experiencing disproportionate impacts of climate change and extreme heat exposure. By synthesising current evidence on ML-based heatwave prediction, associated environmental predictors, and geographical research trends, this review provides insights to support the development of more robust, context-aware, and globally representative heatwave forecasting frameworks.
Adam Ashford, Fahad Ayaz, M. Shakir et al.· Forecasting· 0 citations
Extreme rainfall events (ERES) are among the most challenging hydro-meteorological phenomena to forecast because the complex, nonlinear atmospheric processes involved span multiple spatial and temporal scales and are further intensified by rising climate variability. While Numerical Weather Prediction (NWP) models offer physically consistent representations of atmospheric dynamics, they still struggle to resolve localized convection, rapidly evolving storm systems, and rare, high-intensity precipitation events. This study systematically reviews recent advances in Artificial Intelligence (AI) for extreme rainfall prediction published between 2020 and 2026, using a transparent literature search strategy, predefined screening criteria, quality assessment, and a structured literature review matrix. The review synthesizes evidence from a final evidence base of 139 studies across several dimensions, including deep learning (DL) methodologies, multimodal data fusion, forecasting horizons, operational deployment, uncertainty quantification, and emerging intelligent forecasting paradigms. The analysis reveals a transition from deterministic DI models to hybrid, physics-informed, uncertainty-aware, and operational forecasting systems that increasingly integrate AI with physical knowledge and heterogeneous environmental observations. Despite notable progress, persistent challenges remain, including data scarcity, class imbalance, model generalization, physical consistency, uncertainty estimation, transferability, and the lack of standardized evaluation frameworks. This review provides a unified synthesis of current methodologies. It identifies key research gaps, highlighting the need for evaluation frameworks that address physical plausibility, uncertainty quantification, reliability, transferability, and operational relevance. The findings indicate that future extreme rainfall prediction systems should advance beyond accuracy-focused evaluation toward integrated, trustworthy, uncertainty-aware, and physically consistent forecasting approaches.
Braiton U. Mukhalela, S. Viriri, D. Ndzi et al.· Frontiers in Artificial Inte...· 0 citations
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
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