Recent Advances in Flood Monitoring & Prediction Methods: A Systematic Review

Floods are one of the most dreaded hazards adversely affecting life and property which demands and necessitates singular attention. This paper aims at documenting the trajectory of the recent trends and advancements in monitoring and prediction, including probabilities of associated uncertainties from a “flood” point of view. The primary focus is to convey a comparative vision of vulnerability and risk assessment bringing a viable grasp in approaching problems associated with the sudden extremities of floods. Over a timeline spanning 17 years (2007–2024), a systematic review is conducted utilising Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, dividing the findings into two sections: monitoring and forecasting techniques. The monitoring section encompasses a range of technologies, from ground-level observations to advanced tools, involving global scales to community levels, in perceiving impacts of flood events. Monitoring is enhanced by drone technology, sensor cameras, remote sensing and digital image analysis allowing spatial risk visualisation. The forecasting section pays attention to the utilisation of geospatial guides, GIS tools and machine learning algorithms in predicting the temporal probabilities of flood. The prediction often leverages computer training ensemble models, Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN). This review article serves as an efficacious resource for researchers and decision-makers, streamlined to enhance understanding of the entire flood hazard management continuum—from initiation and response to mitigation and recovery. By scientifically compiling relevant techniques discussed here, the present work will steer future studies on flood hazards and actively promote adaptive strategies to mitigate their disastrous effects.