Know to Predict, Forecast to Warn: A Review of Flood Risk Prediction Tools

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