Ambon City Portrait of Flood Vulnerabilities: Spatial Analysis and Identification of Causing Factors
Keywords:
Spatial Analysis, Identification of Causative Factors., Flood Vulnerability, Spatial Analysis
Abstract
Ambon City is one of the cities with a relatively high flood disaster intensity. This study aims to analyze the factors that cause flooding and determine the vulnerability of flooding. The method is based on a geographic information system (GIS) by integrating secondary and primary data. Parameters analyzed include elevation, rainfall, slope, soil type, and land use. Study results show that the factors causing flooding in Ambon City include relatively high-intensity rains, land use patterns dominated by mixed gardens, slopes in lowland areas, low elevations, and soil types easily inundated with water. The flood hazard zone is divided into three classes, namely high, medium, and low hazard zones. Areas with high vulnerability are 2,251.3 ha (6.99%) of the total area of the study area. For this reason, the community and the Ambon City government need to pay attention to this area in dealing with flood disasters.
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References
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[2] F. Cian, M. Marconcini, and P. Ceccato, “Normalized Difference Flood Index for rapid flood mapping: Taking advantage of EO big data,” Remote Sens. Environ., vol. 209, pp. 712–730, 2018, doi: https://doi.org/10.1016/j.rse.2018.03.006.
[3] Z. Kalantari, C. S. S. Ferreira, A. J. Koutsouris, A.-K. Ahlmer, A. Cerdà, and G. Destouni, “Assessing flood probability for transportation infrastructure based on catchment characteristics, sediment connectivity and remotely sensed soil moisture,” Sci. Total Environ., vol. 661, pp. 393–406, 2019, doi: https://doi.org/10.1016/j.scitotenv.2019.01.009.
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[7] A. Arabameri, K. Rezaei, A. Cerdà, C. Conoscenti, and Z. Kalantari, “A comparison of statistical methods and multi-criteria decision making to map flood hazard susceptibility in Northern Iran,” Sci. Total Environ., vol. 660, pp. 443–458, 2019, doi: https://doi.org/10.1016/j.scitotenv.2019.01.021.
[8] G. Sofia, G. Roder, G. Dalla Fontana, and P. Tarolli, “Flood dynamics in urbanised landscapes: 100 years of climate and humans’ interaction,” Sci. Rep., vol. 7, no. December 2016, pp. 1–12, 2017, doi: 10.1038/srep40527.
[9] F. Hernoza, B. Susilo, and A. Erlansari, “Pemetaan Daerah Rawan Banjir Menggunakan Penginderaan Jauh dengan Metode Normalized Difference Vegetation Index , Normalized Difference Water Index dan Simple Additive Weighting ( Studi Kasus : Kota Bengkulu ),” J. Rekursif, vol. 8, no. 2, pp. 144–152, 2020, [Online]. Available: https://ejournal.unib.ac.id/index.php/rekursif/
[10] R. W. Lestari, I. Kanedi, and Y. Arliando, “Sistem Informasi Geografis (Sig) Daerah Rawan Banjir Di Kota Bengkulu Menggunakan Arcview,” J. Media Infotama, vol. 12, no. 1, pp. 41–48, 2016, doi: 10.37676/jmi.v12i1.271.
[11] Z. W. Kundzewicz et al., “Le risque d’inondation et les perspectives de changement climatique mondial et régional,” Hydrol. Sci. J., vol. 59, no. 1, pp. 1–28, 2014, doi: 10.1080/02626667.2013.857411.
[12] J. J. Yu, X. S. Qin, and O. Larsen, “Joint Monte Carlo and possibilistic simulation for flood damage assessment,” Stoch. Environ. Res. Risk Assess., vol. 27, no. 3, pp. 725–735, 2013, doi: 10.1007/s00477-012-0635-4.
[13] G. Nachappa et al., “Flood susceptibility mapping with machine learning, multi-criteria decision analysis and ensemble using Dempster Shafer Theory,” J. Hydrol., vol. 590, p. 125275, 2020, doi: https://doi.org/10.1016/j.jhydrol.2020.125275.
[14] Y. Wang et al., “Quantifying the response of potential flooding risk to urban growth in Beijing,” Sci. Total Environ., vol. 705, p. 135868, 2020, doi: https://doi.org/10.1016/j.scitotenv.2019.135868.
[15] H. R. Pourghasemi, A. Gayen, M. Panahi, F. Rezaie, and T. Blaschke, “Multi-hazard probability assessment and mapping in Iran,” Sci. Total Environ., vol. 692, pp. 556–571, 2019, doi: https://doi.org/10.1016/j.scitotenv.2019.07.203.
[16] L. Lin, C. Tang, Q. Liang, Z. Wu, X. Wang, and S. Zhao, “Rapid urban flood risk mapping for data-scarce environments using social sensing and region-stable deep neural network,” J. Hydrol., vol. 617, p. 128758, 2023, doi: https://doi.org/10.1016/j.jhydrol.2022.128758.
[17] S. Yousefi, H. R. Pourghasemi, O. Rahmati, S. Keesstra, S. N. Emami, and J. Hooke, “Geomorphological change detection of an urban meander loop caused by an extreme flood using remote sensing and bathymetry measurements (a case study of Karoon River, Iran),” J. Hydrol., vol. 597, p. 125712, 2021, doi: https://doi.org/10.1016/j.jhydrol.2020.125712.
[18] B. Jongman, “Effective adaptation to rising flood risk,” Nat. Commun., vol. 9, no. 1, p. 1986, 2018, doi: 10.1038/s41467-018-04396-1.
[19] H. A. El-Naggar et al., “An integrated field data and remote sensing approach for impact assessment of human activities on epifauna macrobenthos biodiversity along the western coast of Aqaba Gulf,” Ecohydrology, vol. 15, no. 3, p. e2400, Apr. 2022, doi: https://doi.org/10.1002/eco.2400.
[20] J. Rusk et al., “Multi-hazard susceptibility and exposure assessment of the Hindu Kush Himalaya,” Sci. Total Environ., vol. 804, p. 150039, 2022, doi: https://doi.org/10.1016/j.scitotenv.2021.150039.
[21] M. A. Saddam, E. K. Dewantara, and A. Solichin, “Sentiment Analysis of Flood Disaster Management in Jakarta on Twitter Using Support Vector Machines,” Sinkron, vol. 8, no. 1, pp. 470–479, 2023, doi: 10.33395/sinkron.v8i1.12063.
[22] J. Hall et al., “Understanding flood regime changes in Europe: a state-of-the-art assessment,” Hydrol. Earth Syst. Sci., vol. 18, no. 7, pp. 2735–2772, 2014, doi: 10.5194/hess-18-2735-2014.
[23] J. Matondang, S. Kahar, and B. Sasmito, “Analisis Zonasi Daerah Rentan Banjir Dengan Pemanfaatan Sistem Informasi Geografis (Studi Kasus : Kota Kendal Dan Sekitarnya),” J. Geod. Undip, vol. 2, no. 2, p. 84658, 2013, [Online]. Available: https://ejournal3.undip.ac.id/index.php/geodesi/article/view/2442
[24] E. J. Plate, “Flood risk and flood management,” J. Hydrol., vol. 267, no. 1–2, pp. 2–11, 2002, doi: 10.1016/S0022-1694(02)00135-X.
[25] Z. Faisal, A. Azis, A. M. Subhan, S. Badaruddin, and D. A. Puspita, “Spatial Analysis Study on the Flood Impact of WalanaeCenranae River Area in Soppeng Regency South Sulawesi Province,” INTEK J. Penelit., vol. 7, no. 1, p. 39, 2020, doi: 10.31963/intek.v7i1.2112.
[26] K. Darmawan, H. Hani’ah, and A. Suprayogi, “Analisis Tingkat Kerawanan Banjir Di Kabupaten Sampang Menggunakan Metode Overlay Dengan Scoring Berbasis Sistem Informasi Geografis,” J. Geod. Undip, vol. 6, no. 1, pp. 31–40, 2017.
[27] G. Antzoulatos et al., “Flood Hazard and Risk Mapping by Applying an Explainable Machine Learning Framework Using Satellite Imagery and GIS Data,” Sustain., vol. 14, no. 6, 2022, doi: 10.3390/su14063251.
[28] R. K. Samanta, G. S. Bhunia, P. K. Shit, and H. R. Pourghasemi, “Flood susceptibility mapping using geospatial frequency ratio technique: a case study of Subarnarekha River Basin, India,” Model. Earth Syst. Environ., vol. 4, no. 1, pp. 395–408, 2018, doi: 10.1007/s40808-018-0427-z.
[29] N. K. R. R. Dewi, I. W. Nuarsa, and I. W. S. Adnyana, “Aplikasi Sistem Informasi Geografis (SIG) untuk Kajian Banjir di Kota Denpasar,” E-Jurnal Agroekoteknologi Trop., vol. 6, no. 2, pp. 134–142, 2017.
[30] B. T. Pham et al., “A comparative study of kernel logistic regression, radial basis function classifier, multinomial naive bayes, and logistic model tree for flash flood susceptibility mapping,” Water (Switzerland), vol. 12, no. 1, 2020, doi: 10.3390/w12010239.
[31] M. Rahman et al., “Flood Susceptibility Assessment in Bangladesh Using Machine Learning and Multi-criteria Decision Analysis,” Earth Syst. Environ., vol. 3, no. 3, pp. 585–601, 2019, doi: 10.1007/s41748-019-00123-y.
[32] M. A. Lasaiba, “Evaluasi lahan untuk permukiman dalam pengembangan wilayah Kota Ambon,” Tesis, 2006, [Online]. Available: http://etd.repository.ugm.ac.id/penelitian/detail/31752
[33] M. Vojtek, “Flood Susceptibility Mapping on a National Scale in Slovakia Using the Analytical Hierarchy Process,” 2019, doi: 10.3390/w11020364.
[34] Y. Kwak, “Nationwide Flood Monitoring for Disaster Risk Reduction Using Multiple Satellite Data,” 2017, doi: 10.3390/ijgi6070203.
[35] H. S. Yunus, “’The Impacts of Urban Sprawling Process on the Farmers’ Commitment in Agricultural Lands and Activities in the Urban Fringe Areas of the City of Yogyakarta, DIY’,” Indones. J. Geogr., vol. 35, no. 1, pp. 15–28, 2003.
[36] M. A. Lasaiba and A. W. Saud, “Pemanfaatan Citra Landsat 8 Oli/Tirs Untuk Identifikasi Kerapatan Vegetasi Menggunakan Metode Normalized Difference Vegetation Index (Ndvi) Di Kota Ambon,” J. Geogr. Geogr. dan Pengajarannya, vol. 20, no. 1, pp. 53–65, 2022, doi: https://doi.org/10.26740/jggp.v20n1.p53-65.
[37] M. A. Lasaiba, “Fenomena geosfer dalam perspektif geografi telaah substansi dan kompleksitas 1,” vol. 15, no. 1, pp. 1–14, 2022, [Online]. Available: https://ojs3.unpatti.ac.id/index.php/jp/article/view/6402/4501
Published
2023-08-07
