Sistem Prediksi dan Penanggulangan Banjir Terintegrasi Polder Berbasis Machine Learning dan Internet of Things

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Prima Prasetyo Simanjuntak Rifaldi Kallolangi Natanael Bagus Jatinendra Hadha Afrisal

Abstract

Floods are the natural disaster that occur regularly in Indonesia. According to National Disaster Management Authority (BNPB), Indonesia saw roughly 1,279 flood occurrences in 2021. The most common flood control measures is the Early Warning System (EWS) installation, which detects the flooding possibility. Another major move is the polder system construction. Polder is a drainage management method of securing flood-prone areas from the water and infrastructure runoff effects. Although, there are significant barriers in its application, such as EWS's limited ability to identify floods and the frequent delays in pumping water at the polder. To address these issues and achieve the SDGs' goal of combating climate change, this article proposes an EWS-integrated polder system automation. It is divided into two sections: EWS system and polder system. EWS system uses machine learning to categorize rivers into four categories: action, minor, moderate, and major. Machine learning functions to obtain the accurate flood detection results. Whereas the polder system handles the pump based on the detection results. The action stage activates one pump, minor stage two pumps, moderate stage three pumps, and major stage three pumps and sirens. This is the first step to prevent flooding by reducing the volume of water. In the system, Internet of Things performs to connect EWS, polder systems, and databases, resulting a real-time monitoring system. Sensor readings, flood predictions, and pump status will be displayed on public-accessible apps and websites. Solar panels serve power to the system. The designed system is expected to resolve Indonesia's flooding issues.

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References

Hermawan, R., & Abdurrohman, A. (2020). Pemanfaatan Teknologi Internet Of Things pada Alarm Sepeda Motor Menggunakan NodeMCU LOLIN V3 dan Media Telegram. Infotronik: Jurnal Teknologi Informasi Dan Elektronika, 5(2), 58–67.
Lindsey, R., & Dahlman, L. (2021). Climate Change: Global Temperature. Climate.Gov.https://www.climate.gov/news-features/understanding-climate/climate-change-global-temperature
Mahmudi, M. N., & Setiawan, B. I. (2020). Desain Kolam Retensi Berbantu Komputer di Cibuluh Kota Bogor. J-Sil (Jurnal Teknik Sipil Dan Lingkungan), 5(2), 115–124.
Nuryadin, N., & Rizal, S. (2009). Penanganan Sistem Drainase Sungai Kendal Kota Kendal--Jawa Tengah (Controlling Drainage System Of Kendal River Kendal City--Middle Java). F. TEKNIK UNDIP.
Pengel, B. E., Krzhizhanovskaya, V. V, Melnikova, N. B., Shirshov, G. S., Koelewijn, A. R., Pyayt, A. L., & Mokhov, I. I. (2013). Flood Early Warning System: Sensors and Internet. IAHS Red Book, 357, 445–453.
Rahardjo, P. N. (2018). 7 Penyebab Banjir di Wilayah Perkotaan yang Padat Penduduknya. Jurnal Air Indonesia, 7(2).
Retnoningsih, E., & Pramudita, R. (2020). Mengenal Machine Learning dengan Teknik Supervised dan Unsupervised Learning Menggunakan Python. Bina Insani Ict Journal, 7(2), 156–165.
Saputra, B. J., Saputra, W. A., Arifin, B., Abriaman, L. O., & Lubna, L. (2015). Monitoring dan Pengendali Banjir pada Rumah Pompa PRPP Jawa Tengah. SENS.
Suripin, S., & Kurniani, D. (2016). Pengaruh Perubahan Iklim terhadap Hidrograf Banjir di Kanal Banjir Timur Kota Semarang. Media Komunikasi Teknik Sipil, 22(2), 119–128.