Smart Disaster Mitigation: The Role of IoT and Machine Learning

Nur Laily; Juwita Sari; Muktar Redy Susila

doi:10.18535/ijsrm/v11i12.ec03

Abstract

This prototype output research has the aim of creating a dashboard that is used to monitor river water levels. The dashboard created will display data in real-time and prediction results. The use of the dashboard is to minimize the risk in the event of flooding caused by river overflow. The way this prototype works is to take data from sensors that have been installed at several points. The recorded data will be stored in a database using the working principles of the Internet of Things. For predictions, machine learning is used to produce future river water level figures. The machine learning used is using time series regression with rainfall input and river water level output. Long-term output data is needed, therefore to forecast future rainfall the Hybrid method is used. Data generated from sensors as well as from prediction results are stored in one database. From the database, data visualization is displayed along with important figures used for river overflow intelligence. Therefore, the dashboard is very useful for people living around the river flow.

References

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[refR-1] Andayani, M., Indrasari, W., & Iswanto, B. H. (2016). Kalibrasi Sensor Ultrasonik HC-SR04 sebagai Sensor Pendeteksi Jarak pada Prototipe Sistem Peringatan Dini Bencana Banjir. Prosiding Seminar Nasional Fisika (E-Journal), 5, 43–46.Google Scholar ↗

[refR-2] Bentivoglio, R., Isufi, E., Jonkman, S. N., & Taormina, R. (2022). Deep learning methods for flood mapping: a review of existing applications and future research directions. Hydrology and Earth System Sciences, 26(16), 4345–4378. https://doi.org/10.5194/hess-26-4345-2022DOI ↗Google Scholar ↗

[refR-3] Chang, L. C., Chang, F. J., Yang, S. N., Kao, I. F., Ku, Y. Y., Kuo, C. L., & Amin, I. M. Z. bin M. (2018). Building an intelligent hydroinformatics integration platform for regional flood inundation warning systems. Water (Switzerland), 11(1). https://doi.org/10.3390/w11010009DOI ↗Google Scholar ↗

[refR-4] Chang, L. C., Shen, H. Y., Wang, Y. F., Huang, J. Y., & Lin, Y. T. (2010). Clustering-based hybrid inundation model for forecasting flood inundation depths. Journal of Hydrology, 385(1–4), 257–268. https://doi.org/10.1016/j.jhydrol.2010.02.028DOI ↗Google Scholar ↗

[refR-5] Chidthong, Y., Tanaka, H., & Supharatid, S. (2009). Developing a hybrid multi-model for peak flood forecasting. Hydrological Processes, 2274(1 april 2009), 2267–2274. https://doi.org/10.1002/hypDOI ↗Google Scholar ↗

[refR-6] Chitwatkulsiri, D., Miyamoto, H., Irvine, K. N., Pilailar, S., & Loc, H. H. (2022). Development and Application of a Real-Time Flood Forecasting System (RTFlood System) in a Tropical Urban Area: A Case Study of Ramkhamhaeng Polder, Bangkok, Thailand. Water, 14(1641).Google Scholar ↗

[refR-7] Kaur, M., Kaur, P. D., & Sood, S. K. (2021). Energy Efficient IoT Based Cloud Framework for Early Flood Prediction. Research Square. https://doi.org/10.1007/s11069-021-04910-7DOI ↗Google Scholar ↗

[refR-8] Munawar, H. S., Hammad, A. W. A., Waller, S. T., Thaheem, M. J., & Shrestha, A. (2021). An integrated approach for post-disaster flood management via the use of cutting-edge technologies and UAVs: A review. Sustainability (Switzerland), 13(14). https://doi.org/10.3390/su13147925DOI ↗Google Scholar ↗

[refR-9] Puttinaovarat, S., & Horkaew, P. (2020). Flood Forecasting System Based on Integrated Big and Crowdsource Data by Using Machine Learning Techniques. IEEE Access, 8, 5885–5905. https://doi.org/10.1109/ACCESS.2019.2963819DOI ↗Google Scholar ↗

[refR-10] Sejati, W., Paramitha AH, D., Khansa, F., Maulana, A. S., & Julianingsih, D. (2021). Flood Disaster Mitigation Using the HEC-RAS Application to Determine River Water Levels in the Old City Area of Jakarta. Aptisi Transactions on Technopreneurship (ATT), 4(2), 121–134. https://doi.org/10.34306/att.v4i2.253DOI ↗Google Scholar ↗

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[refR-12] Susila, M. R. (2021). Model Hybrid Peredaran Uang Elektonik di Indonesia. In STIESIA Surabaya.Google Scholar ↗

[refR-13] Susila, M. R., Jamil, M., & Santoso, B. H. (2022). Analisis Dampak COVID-19 dan Faktor-Faktor yang Mempengaruhi Indeks Saham Bank Jatim Menggunakan Pendekatan Regresi Time Series. Jambura Journal of Mathematics, 4(2), 220–231.Google Scholar ↗

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[refR-15] Yang, S. N., & Chang, L. C. (2020). Regional inundation forecasting using machine learning techniques with the internet of things. Water (Switzerland), 12(6). https://doi.org/10.3390/W12061578DOI ↗Google Scholar ↗