Flood prediction has advanced significantly in terms of technique and capacity to achieve policymakers’ objectives of accurate forecast and identification of flood-prone and impacted areas. Flood prediction tools are critical for flood hazard and risk management. However, numerous reviews on flood modelling have focused on individual models. This study presents a state-of-the-art review of flood prediction tools with a focus on analyzing the chronological growth of the research in the field of flood prediction, the evolutionary trends in flood prediction, analysing the strengths and weaknesses of each tool, and finally identifying the significant gaps for future studies. The article conducted a review and meta-analysis of 1101 research articles indexed by the Scopus database in the last five years (2017–2022) using Biblioshiny in r. The study drew an up-to-date picture of the recent developments, emerging topical trends, and gaps for future studies. The finding shows that machine learning models are widely used in flood prediction, while Probabilistic models like Copula and Bayesian Network (B.N.) play significant roles in the uncertainty assessment of flood risk, and should be explored since these events are uncertain. It was also found that the advancement of the remote sensing, geographic information system (GIS) and cloud computing provides the best platform to integrate data and tools for flood prediction. However, more research should be conducted in Africa, South Africa and Australia, where less work is done and the potential of the probabilistic models in flood prediction should be explored.

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Like low-lying sandy coasts around the world, the Ghanaian coast is experiencing increasingly frequent coastal flooding due to climate change, putting important socioeconomic infrastructure and people at risk. Our study assesses the major factors contributing to extreme coastal water levels (ECWLs) from 1994 to 2015. ECWLs are categorized into low, moderate, and severe levels corresponding to the 30th, 60th, and 98th percentiles, respectively. Using these three levels over the Pleiades satellite-derived digital elevation model topography, potential flood extent zones are mapped. ECWLs have the potential to flood more than 40% of the study area, including socioeconomically important sites such as tourist beach resorts, Cape St. Paul lighthouse, and Fort Prinzenstein. In this study, all coastal flooding events recorded by the municipality of Keta fall within the 98th percentile category. Our results show a gradual increase in the frequency of flooding over the years. Flooding events are caused by a compound effect of the tide, sea level anomaly, waves, and atmospheric conditions. Finally, while wave run-up is the major contributor to coastal flooding, the tide is the one varying most, which facilitates a simple early warning system based on waves and tide but adds uncertainty and complicates long-term predictability.