Development of a Novel Integrated Theoretical Framework for Extreme Rainfall-Induced Flash Flood Prediction and Automated Early Warning for Nigerian North East Communities Using Mechatronic Sensor Networks

Communities in the North East geopolitical zone of Nigeria, particularly in Borno, Adamawa, and Yobe States, face compounding humanitarian vulnerabilities from both armed conflict displacement and increasingly frequent extreme rainfall-induced flash flooding events that overwhelm existing manual early warning capabilities and result in preventable loss of life and displacement. This dissertation develops a novel integrated theoretical framework coupling a distributed mechatronic sensor network with data-driven flash flood prediction algorithms and automated community alert systems specifically calibrated to the hydrological characteristics and communication infrastructure constraints of the North East zone. A hydrogeomorphological flash flood susceptibility theory adapted for the Sahel-savanna transitional terrain of North East Nigeria is developed, integrating remote sensing-derived topographic wetness indices, soil hydraulic conductivity fields, and land cover change analysis to delineate flash flood initiation zones. A distributed wireless sensor network architecture incorporating ultrasonic stream flow sensors, tipping bucket rain gauges, and soil moisture probes is designed with communication protocols adapted for the intermittent cellular network coverage characteristic of the humanitarian operating environment. A long short-term memory neural network ensemble flash flood prediction model is developed and trained on historical rainfall and stream level data from the Hadejia-Nguru wetlands catchment system. Theoretical analysis of prediction uncertainty propagation through the integrated sensor-model-alert pipeline is conducted using Monte Carlo simulation, yielding calibrated probabilistic warning issuance criteria. Field validation of the integrated framework at three monitored catchments in Borno State over 24 months demonstrates a 4 to 8 hour warning lead time with a 91 percent probability of detection at a false alarm rate below 12 percent. Keywords: flash flood prediction, early warning system, mechatronic sensor network, North East Nigeria, LSTM hydrology