Abstract

Flood monitoring systems commonly depend on measured water level and fixed threshold rules. The uploaded study describes an Arduino-based design using water-level sensing, a servo-operated gate, GSM alerts, LCD display, LEDs and a buzzer. This proposal reframes that foundation into a new research project: an Adaptive AIoT Flood Early Warning and Community Risk Mapping System. The proposed system combines water level, rainfall, flow tendency and soil-moisture observations with an edge-based machine-learning model. Instead of treating every threshold crossing as the same event, the system estimates a short-term flood-risk class and updates the warning level according to recent trends. A low-power long-range communication layer is proposed for distributed sensor nodes, while a gateway provides local storage and optional cloud synchronization [1]. A community-facing dashboard can display station status, risk level, recent trends and the affected zone. The research will evaluate prediction quality, warning lead time, communication reliability, energy use and false-alarm rate. The project is deliberately designed as a research prototype rather than a claim of a fully validated operational flood-control system.