Flood Early Warning Monitoring System Using KNN Methods In Bangkalan District
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Abstract
Bangkalan Regency faces serious challenges due to flood disasters that periodically threaten the safety and welfare of the community. The flood phenomenon in this area is caused by a complex combination of geographic and climate factors, including increased extreme rainfall and the dynamics of sea level rise. Annual floods not only cause infrastructure damage but also threaten the livelihoods and lives of residents living around the river flow. This study aims to develop an innovative flood early warning system using the K-Nearest Neighbors (KNN) method to predict potential disasters before floods occur. By using water flow data analysis and machine learning algorithms, this system is designed to provide accurate and timely early estimates. The main advantage of this study is its ability to proactively mitigate disaster risks using modern computer technology. The study produced a prototype of a flood detection and simulation system that can help local governments, related agencies, and the Bangkalan community in taking preventive and mitigation actions at an earlier stage. Therefore, this system is expected to make a significant contribution to reducing the impact of disasters and protecting the lives of Bangkalan Regency residents.
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T. J. Su, T. S. Pan, Y. L. Chang, S. S. Lin, and M. J. Hao, “A Hybrid Fuzzy and K-Nearest Neighbor Approach for Debris Flow Disaster Prevention,” IEEE Access, vol. 10, pp. 21787–21797, 2022, doi: 10.1109/ACCESS.2022.3152906.
M. Khalaf et al., “IoT-Enabled flood severity prediction via ensemble machine learning models,” IEEE Access, vol. 8, pp. 70375–70386, 2020, doi: 10.1109/ACCESS.2020.2986090.
J. Ibarreche et al., “Flash flood early warning system in colima, mexico,” Sensors (Switzerland), vol. 20, no. 18, pp. 1–26, 2020, doi: 10.3390/s20185231.
P. Muñoz, J. Orellana-Alvear, J. Bendix, J. Feyen, and R. Célleri, “Flood early warning systems using machine learning techniques: The case of the tomebamba catchment at the southern Andes of Ecuador,” Hydrology, vol. 8, no. 4, 2021, doi: 10.3390/hydrology8040183.
T. A. Khan, Z. Shahid, M. Alam, M. M. Su’ud, and K. Kadir, “Early Flood Risk Assessment using Machine Learning: A Comparative study of SVM, Q-SVM, K-NN and LDA,” MACS 2019 - 13th Int. Conf. Math. Actuar. Sci. Comput. Sci. Stat. Proc., 2019, doi: 10.1109/MACS48846.2019.9024796.
S. Liu, R. Liu, and N. Tan, “A spatial improved-knn-based flood inundation risk framework for urban tourism under two rainfall scenarios,” Sustain., vol. 13, no. 5, pp. 1–19, 2021, doi: 10.3390/su13052859.
J. Ren, B. Ren, Q. Zhang, and X. Zheng, “A novel hybrid extreme learning machine approach improved by K nearest neighbor method and fireworks algorithm for flood forecasting in medium and small watershed of Loess region,” Water (Switzerland), vol. 11, no. 9, 2019, doi: 10.3390/w11091848.
H. Lumbantobing, I. Ratna Avianti, K. Harisapto, and S. Suharjito, “Flood Prediction based on Weather Parameters in Jakarta using K-Nearest Neighbours Algorithm,” Eduvest - J. Univers. Stud., vol. 4, no. 6, pp. 5055–5065, 2024, doi: 10.59188/eduvest.v4i6.1339.
S. Van Ackere, J. Verbeurgt, L. De Sloover, S. Gautama, A. De Wulf, and P. De Maeyer, “A review of the internet of floods: Near real-time detection of a flood event and its impact,” Water (Switzerland), vol. 11, no. 11, pp. 1–26, 2019, doi: 10.3390/w11112275.
M. I. K. Alfahadiwy and A. Suliman, “Flood Detection using Sensor Network and Notification via SMS and Public Network,” Student Conf. Res. Dev. (SCOReD 2011), no. June, pp. 1–7, 2011.
M. Esposito, L. Palma, A. Belli, L. Sabbatini, and P. Pierleoni, “Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review,” Sensors, vol. 22, no. 6, 2022, doi: 10.3390/s22062124.
D. S. Rani, G. N. Jayalakshmi, and V. P. Baligar, “Low Cost IoT based Flood Monitoring System Using Machine Learning and Neural Networks: Flood Alerting and Rainfall Prediction,” 2nd Int. Conf. Innov. Mech. Ind. Appl. ICIMIA 2020 - Conf. Proc., no. Icimia, pp. 261–267, 2020, doi: 10.1109/ICIMIA48430.2020.9074928.
V. V. Krzhizhanovskaya et al., “Flood early warning system: Design, implementation and computational modules,” Procedia Comput. Sci., vol. 4, pp. 106–115, 2011, doi: 10.1016/j.procs.2011.04.012.
S. Sankaranarayanan, M. Prabhakar, S. Satish, P. Jain, A. Ramprasad, and A. Krishnan, “Flood prediction based on weather parameters using deep learning,” J. Water Clim. Chang., vol. 11, no. 4, pp. 1766–1783, 2020, doi: 10.2166/wcc.2019.321.
W. Joko, “Flood Early Warning System Develop at Garang River Semarang using Information Technology base on SMS and Web,” Int. J. Geomatics Geosci., vol. 1, no. 1, pp. 14–28, 2010.
Universitas Prima Indonesia. Fakultas Teknologi & Ilmu Komputer, Institute of Electrical and Electronics Engineers. Indonesia Section. CSS/RAS Joint Chapter, and Institute of Electrical and Electronics Engineers, “MECnIT 2020 : International Conference on Mechanical, Electronics, Computer, and Industrial Technology : June 25-26, 2020, Universitas Prima Indonesia, Medan, Indonesia,” pp. 30–35, 2020.
C. Chen, Q. Hui, W. Xie, S. Wan, Y. Zhou, and Q. Pei, “Convolutional Neural Networks for forecasting flood process in Internet-of-Things enabled smart city,” Comput. Networks, vol. 186, p. 107744, 2021, doi: 10.1016/j.comnet.2020.107744.
C. Moreno et al., “Rivercore: IoT device for river water level monitoring over cellular communications,” Sensors (Switzerland), vol. 19, no. 1, 2019, doi: 10.3390/s19010127.
I. Suwarno, A. Ma’arif, N. M. Raharja, A. Nurjanah, J. Ikhsan, and D. Mutiarin, “IoT-based Lava Flood Early Warning System with Rainfall Intensity Monitoring and Disaster Communication Technology,” Emerg. Sci. J., vol. 4, no. Special issue, pp. 154–166, 2020, doi: 10.28991/ESJ-2021-SP1-011.
M. Anbarasan et al., “Detection of flood disaster system based on IoT, big data and convolutional deep neural network,” Comput. Commun., vol. 150, no. November 2019, pp. 150–157, 2020, doi: 10.1016/j.comcom.2019.11.022.
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