Flood disaster prediction using multi-scale deep learning and neuro-fuzzy inference

A new algorithmic framework that can predict flooding could help save lives and reduce the devastation as climate change drives more intense and unpredictable rainfall.
The model described in the International Journal of Information and Communication Technology uses the Multi-Scale Adaptive Neuro-Fuzzy Inference System (MS-ANFIS) and combines deep learning with a form of fuzzy logic that quantifies uncertainty; features that were missing from earlier data-driven flood models.
Flood prediction usually focuses on hydrologic models that simulate how rainfall moves across landscapes and into rivers. These are grounded in environmental science but depend on detailed land-surface information and can be computationally expensive, limiting their usefulness for rapid or large-scale forecasting.
Attempts to reduce the computing demands as well as speed up predictions using statistical and early machine-learning approaches have proved useful but still struggle to cope with diverse data sources or respond to highly localized events.
Even cutting-edge deep-learning models, which can spot patterns in vast datasets, treat river systems as deterministic in behavior and do not take into account the inherent variability that arises because of extreme weather.
MS-ANFIS might plug the holes in earlier approaches. It uses a feature pyramid network. This is a deep-learning architecture that extracts information at multiple scales. In doing so, it can capture detailed runoff patterns and broader rainfall trends visible in satellite data.
The fuzzy layer then interprets the data and expresses uncertainty in a structured, interpretable way. The result is flood prediction with a measure of confidence in the prediction built in.
The researchers have tested their system on data from five major river basins, covering markedly different weather patterns and hydrologic behavior.