ANALYZING, MODELLING AND DEVELOPMENT OF A FLOOD HAZARD PREDICTION SYSTEM USING HYBRID MACHINE LEARNING ALGORITHMS

Abstract

The impact of natural disasters on human existence cannot be overemphasized as it has remained a critical part of human history in Nigeria, In the last decades, there has experienced cases of perennial flooding across the various geopolitical zones with grave consequences. From the time Artificial Intelligence was first introduced to the field of hydrological modelling and prediction, it has produced enormous attention in research aspects for additional developments to hydrology and has the potential of mitigating the associated risk and losses occasioned by this natural disaster. This research therefore harnesses the application of hybrid machine learning models for the prediction of flooding using deep neuro-fuzzy inference system for the modelling, forecasting and analysis of flood risk in the Niger Delta region for early response and intervention in affected communities as it has proven to yield high predictive accuracy, specification, precision and low error margin. The simulation is done using the C++ programming language for Arduino software.