Development of measured hydrodynamic information-based flood early warning system for small streams

In general, small stream basins, characterized by narrow channels and steep slopes, face heightened vulnerability to climate change-induced flooding, posing challenges for evacuation procedures. With the increasing intensity of floods and typhoons in recent years, urgent measures are necessary to mitigate damage in such areas. This research endeavors to address these challenges by developing a novel small stream flood early warning system (SSFEWS) tailored to small streams and piloting its application. The proposed system integrates real-time hydrodynamic data collection, flood probability forecasting, and proactive warning issuance through an amalgamation of IoT-based sensor networks, statistical models leveraging measurement data, a robust constrained nonlinear optimization algorithm (RCNOA), and four-parameter logistic method (4PL). Moreover, system accuracy and reliability are enhanced by an automated iterative process that continuously refines forecasting model parameters via a user-defined rainfall-discharge nomograph and rating curve using RCNOA and 4PL. The developed SSFEWS is expected to contribute to the safety of the community as well as prevent possible small stream-related casualties by enabling efficient disaster response. © 2024 Elsevier Ltd. All rights reserved.