Hermawan, Raihan Daffa (2024) Analisis Indeks Risiko Bencana Banjir Kota Surabaya Menggunakan Geomorphic Flood Index Dan Machine Learning. Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Menurut buku IRBI Tahun 2022, Kota Surabaya merupakan daerah dengan indeks risiko bencana pada tahun 2023 sebesar 142.96 dan juga menjadi urutan ke lima dari urutan indeks risiko banjir tertinggi di Jawa Timur pada tahun 2022 serta menjadi daerah yang memiliki risiko bencana tertinggi kelima di Jawa Timur. Oleh karena itu evaluasi kajian risiko bencana perlu dilakukan untuk mengevaluasi bencana selanjutnya. Penelitian ini bertujuan untuk melakukan analisa perhitungan risiko bencana banjir berdasarkan indeks bahaya bencana banjir, indeks kerentanan banjir, dan indeks kapasitas bencana di setiap kelurahan di Kota Surabaya. Indeks bahaya bencana banjir Kota Surabaya dianalisa menggunakan Geomorphic Flood Index (GFI), indeks kerentanan bencana banjir dianalisa menggunakan metode Machine Learning dengan algoritma Random Forest (RF) dan indeks kapasitas bencana dianalisa menggunakan kuisioner dan skoring. Kemudian dilakukan perhitungan untuk mencari indeks risiko banjir Kota Surabaya. Pada parameter indeks bahaya banjir menggunakan GFI didapatkan rentang nilai -1,993 sampai 16,59. Kemudian untuk parameter indeks kerentanan banjir dilakukan dengan permodelan random forest 80:20 yang mendapatkan Acc (Accuracy) nilai sebesar 0.917. Sen (Sensitivity) nilai sebesar 0.833, Spe (Specificity) nilai sebesar 1.000, BA (Balanced Accuracy) nilai sebesar 0.917 serta didapatkan juga kurva ROC-AUC permodelan RF pada penilitian ini sebesar 0.923. Didapatkan indeks risiko banjir dengan rentang 0 – 1 dengan sebaran spasialnya kelas rendah memiliki total luasan 28212,308 ha atau sama dengan 91,155% dari total luas risiko banjir. Sedangkan kelas sedang 1585,033 ha sama dengan 5,121% dan kelas rendah hanya 1152,572 ha atau 3,724%. Luasan area yang terhitung indeks risiko bencananya sebesar 91,893% dari total keseluruhan luas Kota Surabaya.
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According to the 2022 IRBI book, the city of Surabaya is an area with a disaster risk index in 2023 of 142.96 and is also fifth in the ranking of the highest flood risk index in East Java in 2022 and is the area with the fifth highest disaster risk in East Java. Therefore, disaster risk assessment evaluation needs to be carried out to evaluate future disasters. This research aims to carry out an analysis of flood disaster risk calculations based on the flood disaster hazard index, flood vulnerability index, and disaster capacity index in each sub-district in the city of Surabaya. The Surabaya City flood disaster hazard index was analyzed using the Geomorphic Flood Index (GFI), the flood disaster vulnerability index was analyzed using the Machine Learning method with the Random Forest (RF) algorithm and the disaster capacity index was analyzed using questionnaires and scoring. Then calculations were carried out to find the flood risk index for the city of Surabaya. For the flood hazard index parameter using GFI, a value range of -1.993 to 16.59 was obtained. Then the flood vulnerability index parameters were carried out using random forest 80:20 modeling which obtained an Acc (Accuracy) value of 0.917. Sen (Sensitivity) value is 0.833, Spe (Specificity) value is 1.000, BA (Balanced Accuracy) value is 0.917 and the ROC-AUC curve for RF modeling in this research is also 0.923. A flood risk index was obtained with a range of 0 – 1 with a low class spatial distribution having a total area of 28212,308 ha or equal to 91.155% of the total flood risk area. Meanwhile, the medium class is 1585,033 ha equal to 5.121% and the low class is only 1152,572 ha or 3.724%. The area whose disaster risk index is calculated is 91.893% of the total area of the city of Surabaya
| Item Type: | Thesis (Other) |
|---|---|
| Uncontrolled Keywords: | Risiko Bencana, Geomorphic Flood Index, Machine Learning Disaster Risk, Geomorphic Flood Index, Machine Learning. |
| Subjects: | G Geography. Anthropology. Recreation > G Geography (General) > G70.212 ArcGIS. Geographic information systems. |
| Divisions: | Faculty of Civil, Planning, and Geo Engineering (CIVPLAN) > Geomatics Engineering > 29202-(S1) Undergraduate Thesis |
| Depositing User: | Raihan Daffa Hermawan |
| Date Deposited: | 29 Jul 2024 13:31 |
| Last Modified: | 29 Jul 2024 13:31 |
| URI: | http://repository.its.ac.id/id/eprint/110072 |
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