Design and Development of a Prototype Water Level Control System for Early Flood Detection Based on The Internet of Things: a Case Study of Kambaniru Dam

Authors

  • Imaniaro Manutede Universitas Kristen Wira Wacana Sumba
  • Pingky Alfa Ray Leo Lede Universitas Kristen Wira Wacana Sumba
  • Hawu Yogia Pradana Uly Universitas Kristen Wira Wacana Sumba

DOI:

https://doi.org/10.59934/jaiea.v4i1.542

Keywords:

Control Systems, Dam, Floods, Internet of Things (IoT), Prototype

Abstract

Flooding can occur due to overflowing water during the rainy season, therefore early detection of the water level in dams as water runoff processing structures is needed. When heavy rain occurs it can result in flood disasters that are detrimental to communities, dam infrastructure and agricultural land. This research aims to monitor water levels online as initial information about impending flood disasters. Monitoring using Internet of Things (IoT) based technology is intended to obtain water level information in real time. In this device the HC-SR04 ultrasonic sensor is used as a water level reader, a waterflow sensor as a water discharge reader and a servo motor as a sluice gate controller. The results of this research are a prototype of a water level detection device that can provide water level information and can control sluice gates. automatically and can be controlled manually or remotely via the latest notifications in the Blynk application using the MQTT protocol. This research was carried out by taking information based on the results of direct observations and interviews with officers at Kambaniru Dam, Lambanapu Village, Kambera District. In this way, the prototype of this detection tool will be easy to use as initial information about the possibility of flooding.

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Published

2024-10-15

How to Cite

Manutede, I., Pingky Alfa Ray Leo Lede, & Hawu Yogia Pradana Uly. (2024). Design and Development of a Prototype Water Level Control System for Early Flood Detection Based on The Internet of Things: a Case Study of Kambaniru Dam. Journal of Artificial Intelligence and Engineering Applications (JAIEA), 4(1), 35–41. https://doi.org/10.59934/jaiea.v4i1.542