Advanced SearchSearch Tips
Design and Implementation of IoT-Based Intelligent Platform for Water Level Monitoring
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Design and Implementation of IoT-Based Intelligent Platform for Water Level Monitoring
Park, Jihoon; Kang, Moon Seong; Song, Jung-Hun; Jun, Sang Min;
  PDF(new window)
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 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.
Internet of Things;Hyper-connected watershed;Intelligent Platform;Arduino;Water Level Monitoring;
 Cited by
기후변화 시나리오를 이용한 미래 읍면동단위 기준증발산량 데이터베이스 설계 및 구축,김태곤;서교;남원호;이제명;황세운;유승환;홍순욱;

농촌계획, 2016. vol.22. 4, pp.71-80 crossref(new window)
Arduino, 2015,, Accessed 15 Dec. 2015.

Baek, S.H. and Kim, Y.H., 2010, Design of multi position tracking system using ultrasonic sensor module, Proceedings of Symposium on Ultrasonic Electronics 31: 479-480.

Cha, D.W. and Jin, Y.H., 2015, Hyper-connected era, future of the sharing economy and Internet of Things, Seoul:Hans Media.

Cheng, C.-L., Shalabh, and Garg, G., 2014, Coefficient of determination for multiple measurement error models, Journal of Multivariate Analysis 126: 137-152. crossref(new window)

Ferdoush, S. and Li, X., 2014, Wireless sensor network system design using Raspberry Pi and Arduino for environmental monitoring applications, Procedia Computer Science 34: 103-110. crossref(new window)

International Telecommunication Union (ITU), 2005, The Internet of Things-ITU internet reports, 1-8.

IPCC, 2014, Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge and New York: Cambridge University Press.

Jeong, Y., Park, H.S., Kim, B.H., and Kim, Y., 2013, Combined filtering model using voting rule and median absolute deviation for travel time estimation, Journal of the Korea Institute of Intelligent Transport Systems 12(6): 10-21. crossref(new window)

Kanarachos, S. and Kanarachos, A., 2015, Intelligent road adaptive suspension system design using an experts' based hybrid genetic algorithm, Expert Systems with Applications 42: 8232-8242. crossref(new window)

Korea Institute of Civil Engineering and Building Technology (KICT), 2004, Technology for surface water resources investigation, 1483-1501.

Larue, G.S., Kim, I., Rakotonirainy, A., Haworth, N.L., and Ferreira, L., 2015, Driver's behavioural changes with new intelligent transport system interventions at railway level crossings-A driving simulator study, Accident Analysis and Prevention 81: 74-85. crossref(new window)

Miles, J., McKenzie, F., and Keen, K., 2011, The intelligent transport systems (its) practitioners' guide to EUROPE, 9-14, London: RTI Focus (UK) Ltd.

Moriasi, D.N., Arnold, J.G., Van Liew, M.W., Bingner, R.L., Harmel, R.D., and Veith, T.L., 2007, Model evaluation guidelines for systematic quantification of accuracy in watershed simulations, Transactions of the ASABE 50(3): 885-900. crossref(new window)

National Disaster Information Center (NDIC), 2015, Http://, Accessed 21 Jul. 2015.

NIC and SRI Consulting, 2008, Disruptive civil technologies: six technologies with potential impacts on US interests out to 2025, 27-31.

Saunders, L.J., Russell, R.A., and Crabb, D.P., 2012, The coefficient of determination: what determines a useful R2 statistic?, Investigative Ophthalmology & Visual Science October 53: 6830-6832. crossref(new window)

United Nations International Strategy for Disaster Reduction (UNISDR), 2015, Http://, Accessed 21 Jul. 2015.

World Economic Forum (WEF), 2012, The Global Information Technology Report 2012 Living in a Hyperconnected World, 3-4.

Yue, W., Guo, G., Wang, L., and Wang, W., 2015, Nonlinear platoon control of Arduino cars with range-limited sensors, International Journal of Control 88(5): 1037-1050.