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ESTD Year: 2017 | Impact Factor: 6.9
DOI Prefix: 10.47001/IRJIET
ESTD Year: 2017 | Impact Factor: 6.9
DOI Prefix: 10.47001/IRJIET
Vol 10 No 7 (2026): Volume 10, Issue 7, July 2026 | Pages: 48-53
International Research Journal of Innovations in Engineering and Technology
OPEN ACCESS | Research Article | Published Date: 21-07-2026
Flood prediction is appealing in the global scheme of things among the technologies that enhance research growth and the reality of human existence today. This work presents the development of an IoT based prototypical real time flood monitoring system for a Flood Prone Region “Anambra West,” Nigeria. The study region lies approximately between the latitude 6°20'N to 6°40'N and longitude 6°30'E to 6°50'E. The research area is among the regions most vulnerable and highly susceptible to devastating flooding in the South Eastern part of Nigeria and, poses significant threats to lives and livelihoods. Accurate real time variation measurement of water levels, flood current and territorial events is crucial for effective flood risk management in this dynamic environment. We aimed at developing an efficient and reliable real time flood monitoring system combining IoT with ultrasonic sensors. To identify the safe mode and the vulnerability state of the three research communities, an adapted technology of threshold methodology is established using Esp32, boxes, pipes and joints, wooden platform, LCD i2c, USB power cable, water container, Sim modules, and Ultrasonic Sensors to develop a hardware system for investigation of water levels at Mmiata, Ifite Ogwari, and Umueje in Anambra West. The developed prototypical real time system monitors water levels at the three mentioned communities with the help of the S1, S2, S3 and is programmed to send What’s App messages and SMS alert to the Admin and to the registered community members. This real time system continues to upload and update on the state of the art of flood surge, and food receding in the three communities. The study helps to fine tone the flood extent and to equip with better planning for risk reduction and management during flooding period.
Susceptibility, devastating floods, vulnerable, Food prediction, water Level, Change detection, real time update, Flood surge, Flood Receding.
Chukwuezugo Anthony Ibegbunam, G.N. Ezeh, K.C. Agubor, Onyebuchi C. Nosiri, & Tobechukwu A. Obi. (2026). Development of an IoT Based Prototypical Real Time Flood Monitoring System for the Flood Prone Region “Anambra West,” Nigeria. International Research Journal of Innovations in Engineering and Technology - IRJIET, 10(7), 48-53. Article DOI https://doi.org/10.47001/IRJIET/2026.107006
This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
Choubin, B., Moradi, & Mosavi, A., Golsham, M., Sajedi, H., (2019). An ensemble prediction of flood susceptibility using multivariate discriminate analysis.
Ulloa, P.S., Rohit, K., Thapa, Jessenia H.E, Andrew. S, & Rodrigo, R.C. (2025). Effects of squat training jump training, and their combination on jump & performance of team sports Athletes: A systematic server with meta-analysis.
Echendu A.J, (2022). Urban Planning, Sustainable Development and Flooding: A Case Study of Port Harcourt City, Nigeria. Master of Research Western Sydney University.
Guesmi. H (2017). Wireless Smart Sensor Networks for Real time warning system of flash flood and torrents in KSA int, j. comp. App. 165 (6). https://doi:105120.5120/ijca 2017913240
Holdeman, E. E. (2023). Emergency Management’s Journey with Technology. In Disaster Management and Information Technology: Professional Response and Recovery Management in the Age of Disasters (pp. 3-23). Cham: Springer International Publishing.www.govtech.com/em/emergency-blogs/disaster-zone/the-five-most common-types of flooding
Merlo R.T., Fard F. Sulimaman H., (2025) Cloud Computing’s Impact on the Digital Transformation of the Enterprise: A Mixed-Methods Approach. Department of information Sciences, University of North Texas, Detion, TX 7620-501, USA, Sustainability 20205, 17(13), 5155; https:doi.org/10.3390/su1735755
Njoya, H. M. Matavel, C. Msangi, H.A. Wouapi, H. A, N. Löhr, K. Sieber, S. (2022) Climate change vulnerability and smallholder farmers' adaptive responses in the semi-arid Far North Region of Cameroon. Discover Sustain., (3)41.View in Scopus Google Scholar.
Pappenberger, F. Buizza, R., hagedorn, R., ECMWF, 2009. Combined medium-Range Weather forecasting. (florian.ppenberger@ecmwf.int)
Pappenberger, F. Romos, M.H .Cake, H.L. Wettenhall, Alfieri. L. (2015). How do I know IF my Forecasts are better? Using bench Marks in hydrological ensemble prediction. PP. 697-713. https:11do.org/10.1016/j.hydrol.2015.10,1015.01.024
Ryberg K.R., Akyuez, F.A, Wiche G.J., Lin W., Changes in seasonality and timing of peak stream flow in snow and semi-arid climates of the North-central united States, 1910-202Hydrological processes 30(8), 1218, 2012.
Renkeer S., (2025) Flood warning system Definition and Types. https//www.Renkeer.com
Schumann, G.J.P. Frye. S., Wells., G., Adler B.R., Bolten J. Murray J., Slaynback D., Policelli, F. (2021) UNLOCKING THE FULL POTENTIAL OF Earth Observation during the 2015. Texas flood disaster. Https://doi.org/10.1002/2015WR018428
Thekkil T.M., Prabkaran N. (2019) Minimizing Remote, Monitoring Service Cost of Wireless Sensor Networks Using Krill Swarm Optimization, Journal of control Engineering Practice, 109, 1429-1448. Htts://doi.org/10.1007/s11277-019-06620-7
Yang, H., Wang, H., Lu, J., Zhou, Z., Feng, Q., & Wu, Y. (2021). Full lifecycle monitoring on drought-converted catastrophic flood using sentinel-1 sar: A case study of poyang lake region during summer. https://colmex primo.hosted.exlibrisgroup.com
Yumang N.A. (2025) .Development of road Defect Detection and Classification System via Object Recognition Using YOLOV8, Conference paper, www.researchgate.net 57, Issue1/pp.1-2. 0https://doi.org/10.1002/j.1681-4835.2013.tboo407.x.
Zhang ,H., Yee, S., Wenzhong, S., Dizhou, G. & Nanshan Z., (2021). An object –based spastio temporal fusion model for remote sensing images. European Journal of Remote Sensing images. Sensing, 2(22). https://doi.org/10/080/2279254.2021.1819683