Utilization of GIS Technology for Mapping Flood-Prone Areas in Ambon Island, Indonesia
DOI:
https://doi.org/10.18502/kss.v10i10.18679Keywords:
Ambon, flood, GIS technologyAbstract
Flooding is one of the most common natural disasters in Indonesia, including on Ambon Island, which can cause significant economic and social losses. This research aims to map flood-prone areas on Ambon Island using Geographic Information System (GIS) technology to map flood hazards and affected residential areas. This research uses variables of elevation, slope, rainfall, land cover, distance from rivers, and soil type. The weighted overlay method was used to produce maps of flood hazards and affected areas. The results showed that the low class flood had an area of 58,114.44 ha, the medium class had an area of 14,066.44 ha, and the high class had an area of 4,733.31 ha, while the built-up land area affected by flooding in the low class had an area of 907.92 ha, the medium class had an area of 3,445.92 ha, and the high class had an area of 1,681.40. The results of this study are expected to make a meaningful contribution to disaster risk management policies on Ambon Island and other areas with similar characteristics.
References
BNPB. Indeks Resiko Bencana Indonesia. Jakarta: Badan Nasional Penanggulangan Bencana; 2023. 327 pp.
Mind’je R, Li L, Amanambu AC, Nahayo L, Nsengiyumva JB, Gasirabo A, et al. Flood susceptibility modeling and hazard perception in Rwanda [Internet]. Int J Disaster Risk Reduct. 2019 Aug;38:101211. Available from: https://linkinghub.elsevier.com/retrieve/pii/S2212420918305491
Rakuasa H, Joshua B, Somae G. Modeling Flood Hazards in Ambon City Watersheds: Case Studies of Wai Batu Gantung. J Inf Syst Technol Res [Internet]. 2024 May;3(2):86–91. Available from: https://journal.aira.or.id/index.php/jistr/article/view/836
BNPB. DOKUMEN KAJIAN RISIKO BENCANA KOTA AMBON TAHUN 2017-2021. 2021. 1–58 p.
Rakuasa H, Latue PC. Analisis Spasial Daerah Rawan Banjir di DAS Wae Heru, Kota Ambon. J Tanah dan Sumberd Lahan [Internet]. 2023 Jan 1;10(1):75–82. Available from: https://jtsl.ub.ac.id/index.php/jtsl/article/view/845
Allafta H, Opp C. GIS-based multi-criteria analysis for flood prone areas mapping in the trans-boundary Shatt Al-Arab basin, Iraq-Iran [Internet]. Geomatics Nat Hazards Risk. 2021 Jan;12(1):2087–116.
Chen Y. Flood hazard zone mapping incorporating geographic information system (GIS) and multi-criteria analysis (MCA) techniques [Internet]. J Hydrol (Amst). 2022;612:128268. Available from: https://www.sciencedirect.com/science/article/pii/S002216942200840X
Kota Ambon Dalam Angka BP. 2023 [Internet]. BPS Kota Ambon, editor. Ambon: BPS Kota Ambon; 2023. 412 p. Available from: https://ambonkota.bps.go.id/publication/ 2022/02/25/d4a1a955435993babeaa1777/kota-ambon-dalam-angka-2022.html
Oktari RS, Munadi K, Idroes R, Sofyan H. Knowledge management practices in disaster management: systematic review [Internet]. Int J Disaster Risk Reduct. 2020;51:101881. Available from: https://www.sciencedirect.com/science/article/pii/ S2212420920313832
Muin A, Rakuasa H. Evaluasi Rencana Tata Ruang Wilayah Kota Ambon Berdasarkan Aspek Kerawanan Banjir. ULIL ALBAB J Ilm Multidisiplin. 2023;2(5):1727–38.
Rakuasa, H., Helwend, J. K., & Sihasale DA. Pemetaan Daerah Rawan Banjir di Kota Ambon Menggunakan Sistim Informasi Geografis. J Geogr Media Inf Pengemb dan Profesi Kegeografian. 2022;19(2):73–82.
Rakuasa H, Sugandhi N. Kusratmoko, Supriatna E. Spatial Modeling of Flood Affected Areas of Krukut River in Pela-Mampang Segment, South Jakarta, Indonesia. Int J Multidiscip Appl Bus Educ Res [Internet]. 2023 Nov;4(11):4031–44. Available from: https://ijmaberjournal.org/index.php/ijmaber/article/view/1259
Oyedepo JA, Adegboyega J, Oluyege DE, Babajide EI. Weighted Linear Combination Procedures with GIS and Remote Sensing in Flood Vulnerability Analysis of Abeokuta Metropolis in Nigeria. Niger J Environ Sci Technol [Internet]. 2021 Mar;5(1):240–57. Available from: https://nijest.com/240-257_0260_vol-5-no-1_nijest-2/
Waqas H, Lu L, Tariq A, Li Q, Baqa MF, Xing J, et al. Flash Flood Susceptibility Assessment and Zonation Using an Integrating Analytic Hierarchy Process and Frequency Ratio Model for the Chitral District, Khyber Pakhtunkhwa, Pakistan [Internet]. Water. 2021 Jun;13(12):1650. Available from: https://www.mdpi.com/2073- 4441/13/12/1650
Msabi MM, Makonyo M. Flood susceptibility mapping using GIS and multi- criteria decision analysis: A case of Dodoma region, central Tanzania [Internet]. Remote Sens Appl Soc Environ. 2021;21:100445. Available from: https://www.sciencedirect.com/science/article/pii/S2352938520300161
Rakuasa H, Daniel A. Sihasale, Marhelin C Mehdila APW. Analisis Spasial Tingkat Kerawanan Banjir di Kecamatan Teluk Ambon Baguala, Kota Ambon. J Geosains dan. Remote Sens. 2022;3(2):60–9.
Zhang D, Shi X, Xu H, Jing Q, Pan X, Liu T, et al. A GIS-based spatial multi-index model for flood risk assessment in the Yangtze River Basin, China [Internet]. Environ Impact Assess Rev. 2020 Jul;83:106397. Available from: https://linkinghub.elsevier. com/retrieve/pii/S0195925520300287
Láng-Ritter J, Berenguer M, Dottori F, Kalas M, Sempere-Torres D. Compound flood impact forecasting: integrating fluvial and flash flood impact assessments into a unified system [Internet]. Hydrol Earth Syst Sci. 2022 Feb;26(3):689–709. Available from: https://hess.copernicus.org/articles/26/689/2022/
S v SS, Roy PS, v C, G SR; S V SS. Roy PS, V C, G SR. Flood risk assessment using multi-criteria analysis: a case study from Kopili River Basin, Assam, India [Internet]. Geomatics Nat Hazards Risk. 2018 Jan;9(1):79–93. Available from: https: //www.tandfonline.com/doi/full/10.1080/19475705.2017.1408705
Pham BT, Luu C, Van Phong T, Nguyen HD, Van Le H, Tran TQ, et al. Flood risk assessment using hybrid artificial intelligence models integrated with multi- criteria decision analysis in Quang Nam Province, Vietnam [Internet]. J Hydrol (Amst). 2021 Jan;592:125815. Available from: https://linkinghub.elsevier.com/retrieve/pii/ S0022169420312762
Samela C, Albano R, Sole A, Manfreda S. A GIS tool for cost-effective delineation of flood-prone areas [Internet]. Comput Environ Urban Syst. 2018;70:43–52. Available from: https://www.sciencedirect.com/science/article/pii/S0198971517303794
Aditian A, Kubota T, Shinohara Y. Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a ter- tiary region of Ambon, Indonesia [Internet]. Geomorphology. 2018;318:101–11. Available from: https://www.sciencedirect.com/science/article/pii/S0169555X16304470
Rahmati O, Pourghasemi HR. Identification of Critical Flood Prone Areas in Data-Scarce and Ungauged Regions: A Comparison of Three Data Mining Models [Internet]. Water Resour Manage. 2017 Mar;31(5):1473–87. Available from: http://link.springer.com/10.1007/s11269-017-1589-6
Ndabula C, Oyatayo KT. Spatial Multi-Criteria Evaluation of Proportional Account- ability of Flood Causal Factors and Vulnerable Areas in Makurdi, Benue State, Nigeria. J Resour Dev Manag [Internet]. 2021 Jul; Available from: https://iiste.org/Journals/index.php/JRDM/article/view/56903
Rana IA, Asim M, Aslam AB, Jamshed A. Disaster management cycle and its application for flood risk reduction in urban areas of Pakistan [Internet]. Urban Clim. 2021;38:100893. Available from: https://www.sciencedirect.com/science/article/pii/S2212095521001231
Papilloud T, Röthlisberger V, Loreti S, Keiler M. Flood exposure analy- sis of road infrastructure – Comparison of different methods at national level [Internet]. Int J Disaster Risk Reduct. 2020;47:101548. Available from: https://www.sciencedirect.com/science/article/pii/S221242091931413X
Räsänen A, Lein H, Bird D, Setten G. Conceptualizing community in disaster risk management [Internet]. Int J Disaster Risk Reduct. 2020;45:101485. Available from: https://www.sciencedirect.com/science/article/pii/S2212420919314037
Mudashiru RB, Sabtu N, Abustan I, Balogun W. Flood hazard mapping methods: A review [Internet]. J Hydrol (Amst). 2021;603:126846. Available from: https://www.sciencedirect.com/science/article/pii/S0022169421008969
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