An Efficient Smart Flood Detection and Alert System based on Automatic Water Level Recorder Approach using IoT
DOI:
https://doi.org/10.22399/ijcesen.717Keywords:
Smart Detection, Smart alert system, Automatic water level recorder, Internet of Things, Wireless Sensor NetworkAbstract
An innovative flood detection system may track an increase in water levels. Deployed in cities or other areas of interest, the system consists of sensors. Both mains energy and solar power are viable options for the sensors. To detect impending flooding promptly, a flood warning system uses reliable and up-to-date sensing equipment such as rain gauges, water level sensors, and flow rate sensors for the smart alert system. The challenging characteristics of smart flood detection and alert systems are that some people may not be able to access the warnings, and flash floods may happen too quickly for a warning to be adequate. Hence, in the proposed method, the Automatic Water Level Recorder enabled the Internet of Things (AWLR-IoT), which integrates a low-cost cloud to overcome the challenges of the smart flood detection and alert system and increase optimization modelling and efficiency. Among the most destructive natural catastrophes that may happen on Earth is flooding. After that, a Wireless Sensor Network (WSN) is used to accomplish the flood prediction utilizing data from sensors enabled by the Internet of Things. Heavy rainfall and the following water outflow cause flooding in nations with certain climate conditions. The system monitors humidity, temperature, water level, water rise rate, and rainfall to identify when a flood is imminent. The function of the AWLR-IoT sensor is for monitoring and recording in a database with real-time sensing. This research shows that the low-cost AWLR-IoT sensor has reduced processing time compared to conventional data processing.
References
Hashi, A. O., Abdirahman, A. A., Elmi, M. A., Hashi, S. Z. M., & Rodriguez, O. E. R. (2021). A real-time flood detection system based on machine learning algorithms with emphasis on deep learning. International Journal of Engineering Trends and Technology, 69(5), 249-256. DOI: https://doi.org/10.14445/22315381/IJETT-V69I5P232
Hassan, W. H. W., Jidin, A. Z., Aziz, S. A. C., & Rahim, N. (2019). Flood disaster indicator of water level monitoring system. International Journal of Electrical and Computer Engineering, 9(3), 1694. DOI: https://doi.org/10.11591/ijece.v9i3.pp1694-1699
Suwarno, I., Ma’arif, A., Raharja, N. M., Nurjanah, A., Ikhsan, J., &Mutiarin, D. (2021). IoT-based lava flood early warning system with rainfall intensity monitoring and disaster communication technology. Emerging Science Journal, 4(0), 154-166. DOI: https://doi.org/10.28991/esj-2021-SP1-011
Costa, D. G., Vasques, F., Portugal, P., & Aguiar, A. (2019). A distributed multi-tier emergency alerting system exploiting sensors-based event detection to support smart city applications. Sensors, 20(1), 170. DOI: https://doi.org/10.3390/s20010170
Rani, D. S., Jayalakshmi, G. N., &Baligar, V. P. (2020, March). Low cost IoT based flood monitoring system using machine learning and neural networks: flood alerting and rainfall prediction. In 2020 2nd international conference on innovative mechanisms for industry applications (ICIMIA) (pp. 261-267). IEEE. DOI: https://doi.org/10.1109/ICIMIA48430.2020.9074928
Al Qundus, J., Dabbour, K., Gupta, S., Meissonier, R., & Paschke, A. (2022). Wireless sensor network for AI-based flood disaster detection. Annals of Operations Research, 1-23. DOI: https://doi.org/10.1007/s10479-020-03754-x
Babu, V., & Rajan, V. (2019, July). Flood and earthquake detection and rescue using IoT technology. In 2019 International Conference on Communication and Electronics Systems (ICCES) (pp. 1256-1260). IEEE. DOI: https://doi.org/10.1109/ICCES45898.2019.9002406
Anbarasan, M., Muthu, B., Sivaparthipan, C. B., Sundarasekar, R., Kadry, S., Krishnamoorthy, S., & Dasel, A. A. (2020). Detection of flood disaster system based on IoT, big data and convolutional deep neural network. Computer Communications, 150, 150-157. DOI: https://doi.org/10.1016/j.comcom.2019.11.022
Van Ackere, S., Verbeurgt, J., De Sloover, L., Gautama, S., De Wulf, A., & De Maeyer, P. (2019). A review of the internet of floods: Near real-time detection of a flood event and its impact. Water, 11(11), 2275. DOI: https://doi.org/10.3390/w11112275
Aarthi, M., &Bhuvaneshwaran, A. (2021). IoT based drainage and waste management monitoring and alert system for smart city. Annals of the Romanian Society for Cell Biology, 6641-6651.
Yuan, F., Fan, C., Farahmand, H., Coleman, N., Esmalian, A., Lee, C. C., ... & Mostafavi, A. (2022). Smart flood resilience: harnessing community-scale big data for predictive flood risk monitoring, rapid impact assessment, and situational awareness. Environmental Research: Infrastructure and Sustainability, 2(2), 025006. DOI: https://doi.org/10.1088/2634-4505/ac7251
Abdullahi, S. I., Habaebi, M. H., & Abd Malik, N. (2019). Intelligent flood disaster warning on the fly: developing IoT-based management platform and using 2-class neural network to predict flood status. Bulletin of Electrical Engineering and Informatics, 8(2), 706-717. DOI: https://doi.org/10.11591/eei.v8i2.1504
Basnyat, B., Roy, N., & Gangopadhyay, A. (2021, March). Flood detection using semantic segmentation and multimodal data fusion. In 2021 IEEE international conference on pervasive computing and communications workshops and other affiliated events (PerCom Workshops) (pp. 135-140). IEEE. DOI: https://doi.org/10.1109/PerComWorkshops51409.2021.9430985
Munawar, H. S., Ullah, F., Qayyum, S., & Heravi, A. (2021). Application of deep learning on uav-based aerial images for flood detection. Smart Cities, 4(3), 1220-1242. DOI: https://doi.org/10.3390/smartcities4030065
Boulouard, Z., Ouaissa, M., Ouaissa, M., Siddiqui, F., Almutiq, M., &Krichen, M. (2022). An integrated artificial intelligence of things environment for river flood prevention. Sensors, 22(23), 9485. DOI: https://doi.org/10.3390/s22239485
Srinithi, A., Nachiappan, D. M., & Vegesna, V. V. (2019). An Embedded Based Integrated Flood Forecasting through HAM Communication. Muthukumaran and Vegesna, Vinod Varma, An Embedded Based Integrated Flood Forecasting through HAM Communication (January 21, 2019).
Perera, D., Seidou, O., Agnihotri, J., Mehmood, H., & Rasmy, M. (2020). Challenges and technical advances in flood early warning systems (FEWSs). Flood impact mitigation and resilience enhancement. DOI: https://doi.org/10.5772/intechopen.93069
Munawar, H. S., Ullah, F., Qayyum, S., Khan, S. I., &Mojtahedi, M. (2021). UAVs in disaster management: Application of integrated aerial imagery and convolutional neural network for flood detection. Sustainability, 13(14), 7547. DOI: https://doi.org/10.3390/su13147547
Zahir, S. B., Ehkan, P., Sabapathy, T., Jusoh, M., Osman, M. N., Yasin, M. N., ... & Jamaludin, R. (2019, December). Smart IoT flood monitoring system. In journal of physics: conference series (Vol. 1339, No. 1, p. 012043). IOP Publishing. DOI: https://doi.org/10.1088/1742-6596/1339/1/012043
Chaduvula, K., Markapudi, B. R., & Jyothi, C. R. (2023). Design and Implementation of IoT based flood alert monitoring system using microcontroller 8051. Materials Today: Proceedings, 80, 2840-2844. DOI: https://doi.org/10.1016/j.matpr.2021.07.048
Vinothini, K., & Jayanthy, S. (2019, May). IoT based flood detection and notification system using decision tree algorithm. In 2019 international conference on intelligent computing and control systems (ICCS) (pp. 1481-1486). IEEE. DOI: https://doi.org/10.1109/ICCS45141.2019.9065799
Sabre, M. S. M., Abdullah, S. S., & Faruq, A. (2019). Flood warning and monitoring system utilizing internet of things technology. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 287-296. DOI: https://doi.org/10.22219/kinetik.v4i4.898
Hayder, I. M., Al-Amiedy, T. A., Ghaban, W., Saeed, F., Nasser, M., Al-Ali, G. A., & Younis, H. A. (2023). An intelligent early flood forecasting and prediction leveraging machine and deep learning algorithms with advanced alert system. Processes, 11(2), 481. DOI: https://doi.org/10.3390/pr11020481
Khan, R., Shabaz, M., Hussain, S., Ahmad, F., & Mishra, P. (2022). Early flood detection and rescue using bioinformatic devices, internet of things (IOT) and Android application. World Journal of Engineering, 19(2), 204-215. DOI: https://doi.org/10.1108/WJE-05-2021-0269
https://www.kaggle.com/datasets/virajkadam/sen12flood
M. Devika, & S. Maflin Shaby. (2024). Optimizing Wireless Sensor Networks: A Deep Reinforcement Learning-Assisted Butterfly Optimization Algorithm in MOD-LEACH Routing for Enhanced Energy Efficiency. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.708 DOI: https://doi.org/10.22399/ijcesen.708
K. Yasotha, K. Meenakshi Sundaram, & J. Vandarkuzhali. (2025). Optimizing Energy Efficiency and Network Performance in Wireless Sensor Networks: An Evaluation of Routing Protocols and Swarm Intelligence Algorithm. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.830 DOI: https://doi.org/10.22399/ijcesen.830
M, P., B, J., B, B., G, S., & S, P. (2024). Energy-efficient and location-aware IoT and WSN-based precision agricultural frameworks. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.480 DOI: https://doi.org/10.22399/ijcesen.480
Radhi, M., & Tahseen, I. (2024). An Enhancement for Wireless Body Area Network Using Adaptive Algorithms. International Journal of Computational and Experimental Science and Engineering, 10(3). https://doi.org/10.22399/ijcesen.409 DOI: https://doi.org/10.22399/ijcesen.409
Kosaraju Chaitanya, & Gnanasekaran Dhanabalan. (2024). Precise Node Authentication using Dynamic Session Key Set and Node Pattern Analysis for Malicious Node Detection in Wireless Sensor Networks. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.613 DOI: https://doi.org/10.22399/ijcesen.613
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