Comprehensive Analysis of an Internet of Things-Based Rainfall Measurement Instrumentation System for Coastal Extreme Weather Mitigation in Padang

Abstract

Coastal fishing operations are vulnerable to rapidly changing weather conditions, yet locally resolved rainfall observations are often insufficient to support timely operational decisions. This study evaluates the performance of an Internet of Things (IoT)-based rainfall measurement and warning system developed to support coastal extreme-weather mitigation in Padang. An engineering and experimental approach was employed to develop a prototype integrating a tipping bucket rain gauge (TBRG), ESP32 microcontroller, RTC DS3231 module, 20 × 4 LCD, local buzzer, Blynk IoT platform, and Telegram Bot. System performance was evaluated through comparisons with reference measurements to determine accuracy, repeated controlled-input tests to assess precision, and threshold-response tests to verify the warning function. The TBRG achieved an average measurement accuracy of 98.18%, with a mean relative error of 1.82%. Ten repeated measurements under controlled input yielded a precision of 95.789%. The warning subsystem responded correctly under all tested threshold conditions, producing a functional response rate of 100%. These findings demonstrate the prototype’s capacity to measure localized rainfall quantitatively and translate the observations into local and remote warning signals. The study provides empirical evidence supporting the integration of rainfall instrumentation, microcontroller-based processing, and IoT communication for localized coastal monitoring. Further development should prioritize dynamic calibration across varying rainfall intensities, autonomous power supply, and resilient communication mechanisms.