Water Level Monitoring and Flood Warning System using Light Detection and Ranging (LiDAR) Sensor with Hybrid Renewable Solar-Wind Power

Leon, Angelica F. De; Cruz, Febus Reidj G.

doi:10.1109/hnicem54116.2021.9731998

Cited by 2 publications

(1 citation statement)

References 5 publications

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“…[6]. Various research has been conducted on water level measurement for flood disaster prediction and early warning systems [7]- [11]. The difference between these systems lies in the method of flood prediction, the way of data communication as an early warning system, and the data communication network that must be passed by flood warning messages to the general public [12].…”

Section: Introductionmentioning

confidence: 99%

Design and implementation of an IoT-based Automatic Water Level Recorder (AWLR) as a tool to mitigate the risk of tidal flood disasters in the coastal area of Jakarta

Pianto,

Akbar,

Sutejo

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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Jakarta’s land continues to sink, and rising sea levels pose a high risk of tidal flooding in Jakarta Bay. From 2022 until May 2023, three tidal floods in Jakarta have been recorded due to high-intensity rainfall, causing the sea level to rise above normal. This threat is of public concern due to its material and immaterial losses. To address this issue, a real-time monitoring system that can provide accurate information on sea level heights on the coast of Jakarta needs to be developed. The research aims to develop a web-based Automatic Water Level Recorder (AWLR) system tool to support flood disaster mitigation in Jakarta’s coastal areas. This system integrates several sensors to measure sea level heights, temperature, humidity, and data transmission through a telemetry system. Based on running tests conducted from December 2022 to February 2023, the AWLR recording had been validated with BMKG’s Tide Gauge data installed in the same waters. The results show the data recording performance with high accuracy (98%). The successful performance of the sensors in this device is supported by monitoring devices involving temperature and humidity sensors. The initial calibration stage also shows significant accuracy test results with an error range of 2 -5%. The measurement accuracy of the pressure, temperature, and humidity sensors are 98%, 97%, and 95%, respectively, so the data obtained is reliable. This IoT-based AWLR data recording can be an input for coastal management decision-making, especially for monitoring coastal disasters.

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