Analisis Kerentanan Banjir Menggunakan Data Citra Satelit dan Machine Learning di Kota Surabaya

Ahmad Saifudin, Mahendra Andiek Maulana, Anak Agung Ngurah Satria Damanegara

Abstract


Banjir merupakan bencana alam yang biasanya terjadi saat hujan. Banjir berdampak pada kerusakan sehingga diperlukannya penilaian kerentanan banjir yang efisien. Citra satelit dapat digunakan untuk membantu mendeteksi banjir dalam skala yang luas. Salah satu tantangan dalam mengolah data citra adalah interpretasi citra. Dengan memanfaatkan kemampuan Machine Learning yang diintegrasikan dengan Sistem Informasi Geografis, interpretasi citra dapat dilakukan dengan cepat. Namun, tantangan dari penggunaan citra satelit adalah kurangnya dataset kejadian banjir dalam skala besar. Pada paper ini, kami menyajikan tiga pendekatan Machine Learning, yaitu Bayes, Rain Forest (RF), dan Support Vector Machine (SVM) yang kemudian dianalisis menggunakan metode Frequency Ratio sehingga didapatkan indeks kerentanan banjir.  Dengan memanfaatkan citra Sentinel-1 yang tersedia, analisis dalam penelitian ini menunjukkan bahwa Kota Surabaya termasuk kerentanan banjir rendah sebesar 61,23 persen dari total luas wilayah.

Keywords


Peta Rawan Banjir; Machine Learning; SIG; Sentinel-1

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DOI: http://dx.doi.org/10.12962/j2579-891X.v21i3.15910

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