Integrated Dam Automation: Real-Time Monitoring and Controlling Using IoT

Year : 2025 | Volume : 12 | Issue : 01 | Page : 31 38
    By

    Umar Farooq Babulal Mokashi,

  • Manikanta G,

  • Pavan Kumar B,

  • Rahul K,

  • Adarsh Gowda S.P,

  1. Student, Department of Electrical and Electronics Engineering, Dayananda Sagar College of Engineering, Bangalore, Karnataka, India
  2. Assistant Professor, Department of Electrical and Electronics Engineering, Dayananda Sagar College of Engineering, Bangalore, Karnataka, India
  3. Student, Department of Electrical and Electronics Engineering, Dayananda Sagar College of Engineering, Bangalore, Karnataka, India
  4. Student, Department of Electrical and Electronics Engineering, Dayananda Sagar College of Engineering, Bangalore, Karnataka, India
  5. Student, Department of Electrical and Electronics Engineering, Dayananda Sagar College of Engineering, Bangalore, Karnataka, India

Abstract

Dam automation is a critical area in water resource management, especially given the rising demand for sustainable and safe water control systems. An integrated approach to dam automation involves implementing advanced sensors and monitoring systems to improve structural safety, water quality, and resource management. This paper presents a comprehensive automation model that combines crack detection, convolutional neural networks (CNNs), water level monitoring, turbidity sensing, and rainfall data to ensure real-time safety, structural integrity, and environmental responsiveness in dam operations. The role of CNNs in predictive crack detection involves the automated analysis of images to identify structural cracks in dams. Initially, high-resolution images of the dam surface are captured using cameras or drones. CNNs then process these images by extracting features such as crack shapes, textures, and patterns, allowing them to automatically identify cracks without the need for manual feature identification. The network is trained on a large dataset of labeled images, learning to differentiate between cracks and other surface anomalies internet of things (IoT)-enabled real-time monitoring and flood control use interconnected sensors to continuously gather data from a dam’s environment, such as water levels, rainfall, and structural health. This data is transmitted in real time to a centralized system for constant monitoring. By analyzing water levels and rainfall data, the system can predict flood risks and automatically adjust operations, like controlling water release through gates, to prevent overflow. Additionally, IoT systems can trigger alerts when critical conditions are met, enabling operators to respond quickly.

Keywords: Convolutional neural networks (CNNs), turbidity sensors, rainfall sensors, LiteC3 module, actuators, communication technologies

[This article belongs to Journal of Microcontroller Engineering and Applications ]

How to cite this article:
Umar Farooq Babulal Mokashi, Manikanta G, Pavan Kumar B, Rahul K, Adarsh Gowda S.P. Integrated Dam Automation: Real-Time Monitoring and Controlling Using IoT. Journal of Microcontroller Engineering and Applications. 2025; 12(01):31-38.
How to cite this URL:
Umar Farooq Babulal Mokashi, Manikanta G, Pavan Kumar B, Rahul K, Adarsh Gowda S.P. Integrated Dam Automation: Real-Time Monitoring and Controlling Using IoT. Journal of Microcontroller Engineering and Applications. 2025; 12(01):31-38. Available from: https://journals.stmjournals.com/jomea/article=2025/view=208879


References

1. Guidelines for Seismic Design of Earth Dams and Embankments. Indian Institute of Technology Kanpur, Gujarat State Disaster Management Authority. August 2005, Revised May 2007. [Online]. Available at https://www.iitk.ac.in/nicee/IITK-GSDMA/EQ09.pdf
2. Sadrnejad S. A multi-lined behavior simulation approach for liquefaction of earth-dam. Comput Methods Civil Eng. 2011; 2 (2): 201–218.
3. Dadgar HR, Arjomand MA, Arefnia A. Numerical analysis of cyclic loading effect on progressive failure in earth dams upon a multiline-multilaminate model. Int J Eng Technol. 2019; 11 (3): 492–504.
4. Desai CS, Siriwardane HJ. Constitutive Laws for Engineering Materials with Emphasis on Geologic Materials. Englewood Cliffs, NJ, USA: Prentice-Hall; 1984.
5. Naylor D. Stresses in nearly incompressible materials by finite elements with application to the calculation of excess pore pressures. Int J Numer Methods Eng. 1974; 8: 443–460.
6. Sheng D, Sloan S, Yu H. Aspects of finite element implementation of critical state models. Comput Mech. 2000; 26: 185–196. doi: 10.1007/s004660000166.
7. Gu C, Wang J, Cai Y, Yang Z, Gao Y. Undrained cyclic triaxial behavior of saturated clays under variable confining pressure. Soil Dynam Earthquake Eng. 2012; 40: 118–128. doi: 10.1016/j.soildyn.2012.03.011.
8. Sadrnezhad S. Constitutive model for multi-laminate induced anisotropic double hardening elastic-plasticity of sand. Int J Eng A. 2002; 15: 115–124.
9. Dadgar HR, Arjomand MA, Arefnia A. Crack detection in earth dams upon a multiline-multilaminate model. Int J Eng Technol. 2019; 11 (3): 445–455. doi: 10.21817/ijet/2019/v11i3/191103030.
10. Bažant ZP, Xiang Y, Prat PC. Microplane model for concrete. I: stress-strain boundaries and finite strain. J Eng Mech. 1996; 122 (3): 245–254. doi: 10.1061/(ASCE)0733-9399(1996)122:3(245).
11. Benmore CJ. A review of high-energy X-ray diffraction from glasses and liquids. Int Scholar Res Netw ISRN Mater Sci. 2012; 2012: 1–19.
12. Sun E, Zhang X, Li Z. The internet of things (IOT) and cloud computing (CC) based tailings dam monitoring and pre-alarm system in mines. Safety Sci. 2012; 50 (4): 811–815.
13. Raman R, Rathi T. Efficient dam water level management using cloud-based data analytics and LSTM networks. In: 2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, India, March 14–15, 2024. pp. 1–6.
14. Negi P, Pachouri V, Pandey S, Chaudhary M, Singh R, Chhabra G. Automation intervention using the internet of things in the infrastructure of dam. In: 2023 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), Erode, March 23–25, 2023. pp. 1185–1191.
15. Dhinakaran D, Sankar SU, Latha BC, Anns AE, Sri VK. Dam management and disaster monitoring system using IoT. In: 2023 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), Erode, India, March 23–25, 2023. pp. 1197–1201.
16. Wyawahare M, Subhash A. Smart dam control: embedded systems and LSTM-based water level prediction. In: Rajagopal S, Popat K, Meva D, Bajeja S, editors. International Conference on Advancements in Smart Computing and Information Security 2023. Cham, Switzerland: Springer Nature; 2023. pp. 245–253.
17. Van de Straete HJ, Degezelle P, De Schutter J, Belmans RJ. Servo motor selection criterion for mechatronic applications. IEEE/ASME Trans Mechatron. 1998; 3 (1): 43–50.
18. Liu S, Wang X, Lu Q, Li H, Wang Y, Deng L. Dam leakage detection by borehole radar: a case-history study. Remote Sensing. 2019; 11 (8): 969.
19. Junior MM, Nunes RO, Celinski VG. Comparison of the responses of low cost electrical soil sensors, and a Arduino microcontroller platform. Iberoamer J Appl Comput. 2012; 2 (1): 29–38.
20. Rosly MA, Samad Z, Shaari MF. Feasibility studies of Arduino microcontroller usage for IPMC actuator control. In: 2014 IEEE International Conference on Control System, Computing and Engineering (ICCSCE 2014), Penang, Malaysia, November 28–30, 2014. pp. 101–106.

Regular Issue Subscription Review Article
Volume 12
Issue 01
Received 16/01/2025
Accepted 22/01/2025
Published 28/02/2025
Publication Time 43 Days
343

Login


My IP

PlumX Metrics