A real-time prototype of a water level monitor and wide area early flood warning

Abstract

This article presents a real-time prototype of a water level monitor and wide area early flood warning. The prototype consists of two systems: the first is designed to provide real-time display and alarm alerts on a lab view program; the second is designed to measure water levels and display results via applications on a smartphone in real-time using the Blynk application, as well as to alert users of impending floods via the LINE application using Internet of Things (IoT) technology. The target user in this research study is Sado Phong Community, Khao Kho District, Phetchabun Province. This case study was designed for a stream with a water depth of 200 cm and a three-tiered alarm system: 140 cm, 150 cm and 170 cm of water height. The study results showed that when simulating each set level increase, the prototype could display real-time water level changes and give an alarm in both systems. The error value of the meter for the system displayed in the lab view was ± 1.11%. In comparison, the error value for the system displayed via an application on a smartphone for ultrasonic sensor 1 was ± 0.60% and ultrasonic sensor 2 was ± 0.80%, including the ability to notify a group of community members via the LINE application.

Water level identification with laser sensors, inertial units, and machine learning

“…A related analysis was made by Wannoi and Wannoi [45], who presented a prototype for early flood warning in a wide area. Two management interfaces were proposed to allow real-time monitoring and implement real-time alerts.…”

“…Nonetheless, these works primarily relied on direct sensor readings without additional implementation of error minimisation methodologies. Their focus was predominantly centred on presenting application-specific Internet of Things architectures [43,44] or directly evaluating sensor performance under varying conditions [14,45,17,18,19,46]. Notwithstanding, other studies concentrated on refining the predictive capabilities of these sensors through diverse techniques.…”