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IoT-based Lava Flood Early Warning System with Rainfall Intensity Monitoring and Disaster Communication Technology

Iswanto Suwarno, Alfian Ma’arif, Nia Maharani Raharja, Adhianty Nurjanah, Jazaul Ikhsan, Dyah Mutiarin

Abstract


A lava flood disaster is a volcanic hazard that often occurs when heavy rains are happening at the top of a volcano. This flood carries volcanic material from upstream to downstream of the river, affecting populous areas located quite far from the volcano peak. Therefore, an advanced early warning system of cold lava floods is inarguably vital. This paper aims to present a reliable, remote, Early Warning System (EWS) specifically designed for lava flood detection, along with its disaster communication system. The proposed system consists of two main subsystems: lava flood detection and disaster communication systems. It utilizes a modified automatic rain gauge; a novel configured vibration sensor; Fuzzy Tree Decision algorithm; ESP microcontrollers that support IoT, and disaster communication tools (WhatsApp, SMS, radio communication). According to the experiment results, the prototype of rainfall detection using the tipping bucket rain gauge sensor can measure heavy and moderate rainfall intensities with 81.5% accuracy. Meanwhile, the prototype of earthquake vibration detection using a geophone sensor can remove noise from car vibrations with a Kalman filter and measure vibrations in high and medium intensity with an accuracy of 89.5%. Measurements from sensors are sent to the webserver. The disaster mitigation team uses data from the webserver to evacuate residents using the disaster communication method. The proposed system was successfully implemented in Mount Merapi, Indonesia, coordinated with the local Disaster Deduction Risk (DDR) forum.

 

Doi: 10.28991/esj-2021-SP1-011

Full Text: PDF


Keywords


Fuzzy Decision Tree; Disaster Communication; Tipping Bucket; Flood; Kalman Filter; IoT.

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Full Text: PDF

DOI: 10.28991/esj-2021-SP1-011

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Copyright (c) 2020 Iswanto Iswanto, Adhianty Nurjanah, Jazaul Ikhsan, Dyah Mutiarin