AI-driven Flood Surveillance and Dam Control: Advancing Resilience Through Data Science

Year : 2024 | Volume :01 | Issue : 02 | Page : 9-17
By

Ushaa Eswaran

C. Pushpalatha

Shaik Beebi

  1. Principal and Professor Department of ECE, Indira Institute of Technology and Sciences, Markapur Andhra Pradesh India
  2. Assistant Professor Department of ECE, Indira Institute of Technology and Sciences, Markapur Andhra Pradesh India
  3. Assistant Professor Department of ECE, Indira Institute of Technology and Sciences, Markapur Andhra Pradesh India

Abstract

This study presents the development and real-world deployment of an intelligent system for flood monitoring and automated dam gate control using artificial intelligence (AI) and internet of things (IoT) sensors. Supervised machine learning models are developed to predict floods up to 48 h in advance. An automated dam gate operation system is designed to leverage the flood forecasts and real-time stream water levels for emergency control. The complete end-to-end infrastructure is installed across a flood-prone river basin spanning 50 km2, with sensors streaming data to an integrated software platform. The recurrent neural network model performs accurate hourly flood predictions across multiple lead times with an average precision of 0.82 and recall of 0.79 on the testing dataset. The automated dam gate system initiates opening/closing actions based on the flood predictions, while maintaining water levels within the safe operation zone 95.6% of the deployment duration. The end-to-end system provides emergency responders 36 h of early warning on average and reduces risk of infrastructure failures and downstream inundation.

Keywords: Flood monitoring, dam gate control, machine learning, internet of things, sensors

[This article belongs to International Journal of Data Structure Studies(ijdss)]

How to cite this article: Ushaa Eswaran, C. Pushpalatha, Shaik Beebi. AI-driven Flood Surveillance and Dam Control: Advancing Resilience Through Data Science. International Journal of Data Structure Studies. 2024; 01(02):9-17.
How to cite this URL: Ushaa Eswaran, C. Pushpalatha, Shaik Beebi. AI-driven Flood Surveillance and Dam Control: Advancing Resilience Through Data Science. International Journal of Data Structure Studies. 2024; 01(02):9-17. Available from: https://journals.stmjournals.com/ijdss/article=2024/view=130845


Browse Figures

References

  1. World Meteorological Organization (WMO). (2022). State of climate services 2022: Water. [Online]. https://reliefweb.int/report/world/state-of-climate-services-2022-water-enarfrzh
  2. Du J, Qian L, Rui H, Zuo T, Zheng D, Xu Y, Xu CY. Assessing the effects of urbanization on annual runoff and flood events using an integrated hydrological modeling system for Qinhuai River basin, China. J Hydrol. 2012; 464–465: 127–139. https://doi.org/10.1016/j.jhydrol.2012.06.057
  3. France-Presse A. (2017 Feb 13). California ‘bullet train’ hits a big sinking feeling. [Online]. The Telegraph India. https://www.telegraphindia.com/world/california-bullet-train-hits-a-big-sinking-feeling/cid/1201009
  4. Ushaa Eswaran, et al. Disease detection using nanosensor models. Int J Adv Sci Technol. 2011; 106–120.
  5. Ushaa Eswaran, et al. Modeling and simulation of nanosensor arrays for automated disease detection and drug delivery unit. Int J Adv Eng Technol. 2012; 2(1): 564–577.
  6. Fan FM, Deng K, Zhou SL, Li X, Wang GQ, Chan PW. Forecasting flash floods in mountainous areas using machine learning methods – Case study for Longnan City, China. J Hydrol. 2019; 568: 823–840. https://doi.org/10.1016/j.jhydrol.2018.11.058
  7. Ushaa Eswaran, et al. Disease detection using pattern recognition techniques. GITAM Journal of Information Communication Technology. 2009; 1(2): 34–37.
  8. Islam S, Rico-Ramirez MA, Han D, Srivastava PK. Artificial intelligence techniques for clouds and rain precipitation nowcasting: A review. Artif Intell Rev. 2015; 45(3): 345–392. https://doi.org/10.1007/s10462-015-9452-5
  9. Maier HR, Jain A, Dandy GC, Sudheer KP. Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions. Environ Model Softw. 2010; 25(8): 891–909. https://doi.org/10.1016/j.envsoft.2010.02.003
  10. Olivera F, Raina R. Flood early warning systems. Encyclopedia of Water Science. 2003; 469–473.
  11. Kratzert F, Klotz D, Brenner C, Schulz K, Herrnegger M. Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks. Hydrol Earth Syst Sci. 2018; 22(11): 6005–6022. https://doi.org/10.5194/hess-22-6005-2018
  12. Hornsby D, Jeswiet J. Application of artificial intelligence methods to forecasting dam inflows for real-time optimization. Can Water Resour J/ Revue Canadienne Des Ressources Hydriques. 2019; 45(1): 28–44. https://doi.org/10.1080/07011784.2019.1652381
  13. Castelletti A, Galelli S, Restelli M, Soncini-Sessa R. Tree-based reinforcement learning for optimal water reservoir operation. Water Resour Res. 2010; 46(9): W09507. https://doi.org/10.1029/2009WR008898
  14. Ushaa Eswaran, et al. Embedded system based automated drug delivery unit and microfluidics for drug discovery. Int J Adv Res Comput Commun Eng. 2012;1(1): 13–20. https://doi.org/10.17148/IJARCCE
  15. Gers FA, Schmidhuber J, Cummins F. Learning to forget: Continual prediction with LSTM. Neural Comput. 1999; 12(10): 2451–2471. https://doi.org/10.1162/089976699300016112
  16. Ushaa Eswaran, et al. Design and analysis of high sensitive microcantilever based biosensor for CA 15-3 biomarker detection. Journal of Applied Science and Computations (JASC). 2018; 5(7): 682–698. https://doi.org/10.26811/1076-5153.2018.5.7.10264
Regular Issue Subscription Review Article
Volume 01
Issue 02
Received December 5, 2023
Accepted December 12, 2023
Published January 8, 2024
Views: 86