Design Evaluation and Validation of a Resilient IoT-Based Flood Prediction Framework for Data-Scarce Environments in East Africa

Abstract: Many Internet of Things (IoT)-based flood prediction systems deployed in developing regions fail to deliver reliable early warnings due to unreliable sensors, fragmented datasets, and limited operational resilience. While numerous frameworks have been proposed, few studies systematically evaluate their design limitations or validate enhanced solutions under realistic failure conditions. This paper presents the design evaluation, enhancement, and validation of a resilient IoT-based flood prediction framework. Using Design Science Research principles, existing IoT flood prediction frameworks were evaluated using ITIL-aligned governance criteria to identify deficiencies in data reliability, service continuity, and system governance. An enhanced framework was then designed and validated through simulation using CHIRPS rainfall data and controlled sensor failure scenarios. Simulation results indicate that the enhanced framework maintains prediction accuracy between 82.4% and 91.6% under increasing data-loss conditions and improves alert timeliness compared to baseline approaches. The findings indicate that resilience-oriented, data-centric IoT design significantly improves flood prediction performance in resource-constrained environments.

Keywords: Flood prediction; Internet of Things; design science research; data reliability; sensor unreliability; early warning systems.