Design and Implementation of IoT-Based Intelligent Platform for Water Level Monitoring

IoT 기반 지능형 수위 모니터링 플랫폼 설계 및 구현

  • Park, Jihoon (Department of Landscape Architecture and Rural System Engineering, Seoul National University) ;
  • Kang, Moon Seong (Department of Rural Systems Engineering, Seoul National University, Research Institute for Agricultural and Life Sciences, Seoul National University, Institute of Green Bio Science and Technology, Seoul National University) ;
  • Song, Jung-Hun (Department of Landscape Architecture and Rural System Engineering, Seoul National University) ;
  • Jun, Sang Min (Department of Landscape Architecture and Rural System Engineering, Seoul National University)
  • 박지훈 (서울대학교 생태조경.지역시스템공학부) ;
  • 강문성 (서울대학교 조경.지역시스템공학부, 서울대학교 농업생명과학연구원, 서울대학교 그린바이오과학기술연구원) ;
  • 송정헌 (서울대학교 생태조경.지역시스템공학부) ;
  • 전상민 (서울대학교 생태조경.지역시스템공학부)
  • Received : 2015.11.15
  • Accepted : 2015.12.22
  • Published : 2015.12.30


The main objective of this study was to assess the applicability of IoT (Internet of Things)-based flood management under climate change by developing intelligent water level monitoring platform based on IoT. In this study, Arduino Uno was selected as the development board, which is an open-source electronic platform. Arduino Uno was designed to connect the ultrasonic sensor, temperature sensor, and data logger shield for implementing IoT. Arduino IDE (Integrated Development Environment) was selected as the Arduino software and used to develop the intelligent algorithm to measure and calibrate the real-time water level automatically. The intelligent water level monitoring platform consists of water level measurement, temperature calibration, data calibration, stage-discharge relationship, and data logger algorithms. Water level measurement and temperature calibration algorithm corrected the bias inherent in the ultrasonic sensor. Data calibration algorithm analyzed and corrected the outliers during the measurement process. The verification of the intelligent water level measurement algorithm was performed by comparing water levels using the tape and ultrasonic sensor, which was generated by measuring water levels at regular intervals up to the maximum level. The statistics of the slope of the regression line and $R^2$ were 1.00 and 0.99, respectively which were considered acceptable. The error was 0.0575 cm. The verification of data calibration algorithm was performed by analyzing water levels containing all error codes in a time series graph. The intelligent platform developed in this study may contribute to the public IoT service, which is applicable to intelligent flood management under climate change.


Supported by : 한국연구재단


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