IoT-Based Solar-Powered Flood Early Warning System: Design and Evaluation at Alau Dam, Nigeria

• Haris A. DANLADI

  Author

• Umar Y. MUHAMMAD

  Author

• Idris Y. IDRIS

  Author

• Zaharaddeen MUSA

  Author

Flooding remains one of the most destructive and recurrent natural disasters in sub-Saharan Africa. Communities surrounding Alau Dam in Borno State, Nigeria, lack adequate automated flood early warning, a gap that contributed to severe displacement and loss of life when the dam collapsed in September 2024. This paper presents the design, hardware implementation, and empirical performance evaluation of a solar-powered, IoT-based Flood Early Warning System (FEWS) built around an ESP32 microcontroller. The system integrates an HC-SR04 ultrasonic water-level sensor, a SIM900A GSM module for dual-channel alert delivery (SMS and automated voice call), an I2C LCD for real-time local display, and a solar panel with battery management system for off-grid operation. Three threshold zones (Safe: 40–45 cm, Watch: 25–39 cm, Danger: 0–24 cm) trigger progressive alerts and automated channel valve actuation. Laboratory and field-analogue testing across five repeated measurement cycles yielded a mean detection accuracy of 98.5% (σ = 0.33%) and a mean alert response latency of 1.80 s (range: 1.4–2.1 s). SMS delivery achieved 100% success across threshold-crossing events. Among six comparable published systems, this is the only design integrating dual-channel communication, solar-powered off-grid operation, and actuated flow control in a single deployable unit calibrated to an African dam-risk context. The system is positioned as foundational infrastructure for a multi-node IoT early warning network at Alau Dam.

• 13_Danladi et al. (2026)

Copyright (c) 2026 Haris A. DANLADI, Umar Y. MUHAMMAD, Idris Y. IDRIS, Zaharaddeen MUSA (Author)

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.