Journal of Hydraulic Structures

Journal of Hydraulic Structures

Placement of data transmission equipment in the flood warning system based on the Internet of Things (case study : Jiroft Dam Basin)

Document Type : Research Paper

Authors
Marzieh Mohseni 1
Amineh Naseri 2
leila sheikhmamoo 1
1 Department of Civil Engineering, Sirjan University of Technology, Sirjan, Iran.
2 Department of Computer Engineering, Sirjan University of Technology, Sirjan, Iran.
10.22055/jhs.2025.48451.1331
Abstract
Floods are one of the most damaging natural disasters that have increased in frequency, severity and resulting damages in recent years in Iran and around the world. One solution to reducing flood damages is to develop a flood monitoring and management system to quickly and widely communicate information and warnings to residents. Conventional methods of flood monitoring cannot provide rapid and widespread community notification. As a solution, this study focuses on an Internet of Things-based flood warning system, which is an intelligent technology capable of sending real-time information using smartphones and web services. Jiroft dam basin has been chosen for the implementation of this system. A key challenge in establishing Internet of Things-based flood warning systems lies in the strategic distribution of equipment for data collection and transmission. To address this, a hexagonal algorithm was employed for equipment placement in this study. By identifying the most flood-prone areas within the basin through the use of the K-means clustering algorithm and flood index(f), the study reveals the efficacy of combining these tools in determining flood potential and optimal locations for implementing flood warning systems. Following the identification of sensitive points within the basin, the study selects the Maidan sub-basin as the most susceptible to floods. Through the application of the hexagonal algorithm and a WSN-03 module with a 3.5-kilometer data transmission range, 192 suitable locations for equipment installation are pinpointed for data reception and transmission. This study will be useful for the development of early flood warning systems and reservoir management.
Keywords
Hexagonal algorithm
K-means clustering algorithm
Curve number
Time of concentration
Subjects
  1. Vaghefi SA, Keykhai M, Jahanbakhshi F, Sheikholeslami J, Ahmadi A, Yang, H, Abbaspour KC, (2019). The Future of Extreme Climate in Iran. Scientific Reports 9, Article 1464.
  2. World Bank. (2019). Climate Knowledge Portal. [online] Available at:https://climateknowledge portal.worldbank.org/country/iran [Accessed 9 Apr. 2019].
  3. Yuliandoko H, Subono S, WardhaniVA, Pramono Sh.H, Suwindarto P, (2018). Design of flood warning system based iot and water characteristics. TELKOMNIKA (Telecommunication Computing Electronics and Control), 16(5), pp: 2101-2110.
  4. Basha E, Rus D, (2007), Design of Early Warning Flood Detection Systems for Developing Countries. International Conference on Information and Communication Technologies and Development. Technologies and Development 2007 Dec 15, IEEE: 1-10.
  5. Rani Dola Sh, Jayalakshmi GN, Baligar VP, (2020). Low cost IoT based flood monitoring system using machine learning and neural networks: flood alerting and rainfall prediction. 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), pp: 261-267.
  6. Sood SK, Sandhu R, Singla K, Chang V, (2018). IoT, big data and HPC based smart flood management framework. Sustainable Computing: Informatics and Systems 20, pp: 102-117.
  7. Ancona M, Dellacasa A, Delzanno G, Camera AL, Rellini I, (2015). An "Internet of Things" vision of the flood monitoring problem. InProc. 5th Int. Conf. Ambient Comput., Appl., Services Technol, pp:26-29.
  8. Udo EN, Isong EB, (2013). Flood monitoring and detection system using wireless sensor network. Asian journal of computer and information systems, 1(4).
  9. Mousa, M., Zhang, X. and Claudel, C., 2016. Flash flood detection in urban cities using ultrasonic and infrared sensors. IEEE Sensors Journal, 16(19):7204-7216.
  10. Madhumathi M, Grace R, (2017). Flood alert management system using iot and microcontroller. International Journal of Innovative Research in Computer and Communication Engineering, 5(4).
  11. Hartono A, Satira S, Djamal M, Ramli M, (2017). Characterization Analysis of PVDF Thin Films Fabricated Using Deep Coating Machines. In International Conference on Science and Technology-Promoting Sustainable Agriculture, Food Security, Energy, and Environment Through Science and Technology for Development: 129-132.
  12. Azid ShI, Sharma BN, Raghuwaiya K, Chand A, Prasad S, Jacquier A, (2015). SMS based flood monitoring and early warning system. ARPN Journal of Engineering and Applied Sciences, 10(15), pp: 6387-6391.
  13. Shah WM, Arif FA, Shahrin A, Hassan A, (2018). The implementation of an IoT-based flood alert system. International journal of advanced computer science and applications 9(11).
  14. Andrade MVA, Oishi S, (2014). A Wireless Mesh Sensor Network framework for river flood detection which can be used as a emergency communications network in case of disaster. 104, pp: 284.
  15. Mitra P, Ray R, Chatterjee R, Basu R, Saha P, Raha S, Barman R, Patra S, Biswas SS, Saha S, (2016). Flood forecasting using Internet of things and artificial neural networks. 7th Annual Information Technology, Electronics and Mobile Communication Conference: 1-5.
  16. Bande S, Shete VV, (2017). Smart flood disaster prediction system using IoT and neural networks. International Conference On Smart Technologies For Smart Nation (SmartTechCon), pp:189-194.
  17. Shalini E, Surya P., Thirumurugan R, Subbulakshmi S, (2016). Cooperative flood detection using SMS through IoT. Int. J. Adv. Res. Elect., Electron. Instrum. Eng, 5(3), pp: 3410-3414.
  18. Zahir Sh, Ehkan B, Sabapathy Ph, Jusoh Th, Osman M, Yasin M.N, Wahab M N, et al. (2019). Smart IoT flood monitoring system. Journal of physics: conference series, 1339(1), 012043.
  19. Solaimani Sarood F, Soltani Kopaii S, Salajeghe A, (2013). Selection of Appropriate Flooding Potential Index by Using RainfallRunoff (HEC-HMS) Model and RS & GIS Techniques in Jiroft Dam Basin, Journal of Watershed Management Research, 4(8), pp:90-105.
  20. Sarai MH, Mahmoudi I, Bidsheki A, Kamandari M, (2017). A Study of the Geographical Distribution of Crimes in the Areas of Jiroft City Between 2008-2011. Journal of Geography and Urban Planning Zagros Landscape, 9(33), pp: 21-43.
  21. Van Ackere S, Verbeurgt J, Sloover LD, Gautama S, Wulf AD, Maeyer Ph, (2019). A review of the internet of floods: Near real-time detection of a flood event and its impact. Water 11(11), pp: 2275.
  22. Saghafian B, Farazjou H, (2007). Determination of place causing flood and respecting the floating of hydrologic units of Golestan Dam catchment. Journal of Iran Watershed Management, 1, pp:1-11, (In Persian).
  23. Wulandari S, (2020). Analyze k-value selected method of k-means clustering algorithm to clustering province based on disease case. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 9(3), pp:121-124.
  24. Sari BN, (2016). Identification of Tuberculosis Patient Characteristics Using K-Means Clustering. Scientific Journal of Informatics, 3(2), pp:129-138.
  25. Tabatabaei M, Solaimani K, Habibnejad Roshan M, Kavian A, (2015). Estimation of Daily Suspended Sediment Concentration Using Artificial Neural Networks and Data Clustering by Self-Organizing Map (Case Study: Sierra Hydrometry Station- Karaj Dam Watershed) . J Watershed Manage Res. 5(10), pp: 98-116, (In Persian).