Contributing Factor Influencing Flood Disaster Using MICMAC (Ciliwung Watershed Case Study)
Abstract
Flood disaster is the most common disaster in Indonesia, flood events continue to increase and occur almost every year during the rainy season, on average caused by extreme rainfall above 100 mm, with the number of losses increasing every years, there are many factors that affect One of the causes of flooding is human activities resulting in changes in land use in the form of settlements and agricultural land. This paper aims to present a conceptual reference from the implementation results using software assistance, to determine the level of influence and dependent variables. The method used to assess the most influential factors on flood events, especially in the Ciliwung watershed, is by using Cross-Impact Matric Multiplication Applied to Classification (MICMAC). From the 11 contributing factors analyzed based on the Matrix of Direct Influence (MDI) and Matrix of Indirect Influence (MII) it is known that rainfall and distance from the river are the variables that have the most influence. While the variables with the greatest dependence based on MDI and MII are land cover, slope, population density and river waste, with institutions experiencing a larger variable displacement compared to other variables. The results of the identification of these contributing factors can be used by stakeholders to be careful of each factor as input in anticipating influencing factors.
References
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Ahmed MT, Saleh M. 2009. El Maghara Scenario A Search for Sustainability and Equity An Egyptian Case Study. J. Futur. Study.(September 2015).
Ajim S, Parvin F, Bao Q, Vojtek M, Vojteková J, Costache R, Thi N, Linh T, Quan H, Ahmad A. 2020. GIS-based comparative assessment of fl ood susceptibility mapping using hybrid multi-criteria decision-making approach , naïve Bayes tree , bivariate statistics and logistic regression : A case of Top ľ a basin , Slovakia. Ecol. Indic. 117(June):106620.doi:10.1016/j.ecolind.2020.106620.
Ambrosio-albala M. 2008. Understanding rural areas dynamics from a complex perspective An application of Prospective Structural Analysis. 12th EAAE Congr. ‘People, Food Environ. Glob. Trends Eur. Strateg.(September 2014).
Arifasihati Y, Kaswanto. 2016. Analysis of land use and cover changes in Ciliwung and Cisadane Watershed in three decades. Procedia Environ. Sci. 33:465–469.doi:10.1016/j.proenv.2016.03.098.
Ariyani D, Aprilia V, Tri Juniati A, Dewi A, Kurnia F. 2020. Curve Number Method to Determine the Runoff Height in the Upper Cimanuk Watershed. IOP Conf. Ser. Mater. Sci. Eng. 852:12020.doi:10.1088/1757-899X/852/1/012020.
Bashir H, Ojiako U. 2020. An integrated ISM-MICMAC approach for modelling and analysing dependencies among engineering parameters in the early design phase. J. Eng. Des. 31(8–9):461–483.doi:10.1080/09544828.2020.1817347.
Benjumea-arias M, Castañeda L, Alejandro Valencia-Arias. 2020. Structural Analysis of Strategic Variables through MICMAC Use Case Study Leonel Castañeda Alejandro Valencia-Arias. Mediterr. J. Soc. Sci. 7(4):11–19.doi:10.5901/mjss.2016.v7n4p.
Bian G, Du J, Song M, Zhang Xueliang, Zhang Xingqi, Li R, Wu S, Duan Z, Xu C. 2020. Detection and attribution of flood responses to precipitation change and urbanization: a case study in Qinhuai River Basin, Southeast China. Hydrol. Res.:1–15.doi:10.2166/nh.2020.063.
BNPB. 2022. Kejadian Bencana Tahun 2021. https://bnpb.go.id/infografis/kejadian-bencana-tahun-2021.
Christian R, Hutauruk H, Alfiandy S, Nainggolan HA. 2020. GIS-based Flood Susceptibility Mapping Using Overlay Method in Central GIS-based Flood Susceptibility Mapping in Central Sulawesi. Indones. J. Spat. Reg. Anal. 34(December):136–145.doi:10.23917/forgeo.v34i2.10667.
Delgado-serrano MM, Vanwildemeersch P, London S, Ortiz-guerrero CE, Semerena RE, Rojas M. 2016. Adapting prospective structural analysis to strengthen sustainable management and capacity building in community-based natural resource management contexts. Ecol. Soc.(June).doi:10.5751/ES-08505-210236.
Dewan A, Yamaguchi Y. 2008. Effect of land cover changes on flooding: Example from Greater Dhaka of Bangladesh. Int. J. Geoinformatics. 4:11–20.
Edwin Dwi Putranto. 2021. Aksi Pecinta Alam Bersihkan Sungai Ciliwung. Republika.co.id.
Fauzi A. 2019. Teknik Analisa Berkelanjutan. Ed ke-2019 Fajariyanto, editor. Jakarta: Gramedia Pustaka Utama.
Isa M, Fauzi A, Susilowati I, Muhammadiyah U, Tengah J, Economics E, Tengah J. 2019. Flood risk reduction in the northern coast of Central Java Province , Indonesia An application of stakeholder’s analysis. J. Disaster Risk study.(Juli):1–9.
Joshi A, Pal DK. 2015. Likert Scale : Explored and Explained. Br. J. Appl. Sci. Technol. 7(4):396–403.doi:10.9734/BJAST/2015/14975.
Nugroho SP. 2008. Analisis Curah Hujan Penyebab Banjir Besar di Jakarta. JAI. 4(1):50–55.
Panahi A, Alijani B, Mohammadi H. 2010. The Effect of the Land Use / Cover Changes on the Floods of the Madarsu Basin of Northeastern Iran. J. Water Resour. Prot. 2010(April):373–379.doi:10.4236/jwarp.2010.24043.
Paulus CA, Fauzi A. 2017. Factors Affecting Sustainability of Alternatives Livelihood in Coastal Community of Nembrala , East Nusa Tenggara an Application of Micmac Method. J. Ekon. Pembang. 18(2):175–182.doi:10.23917/jep.v18i2.4397.
Perera D, Seidou O, Agnihotri J, Wahid A, Rasmy M. 2019. Flood Early Warning Systems: A Review Of Benefits, Challenges And Prospects.
Presiden Republik Indonesia. 2011. Peraturan Pemerintah Republik Indonesia Nomor 38 Tahun 2011 Tentang Sungai. Indonesia: Presiden Republik Indonesia.hlm 1–61.
Qomariyatus Sholihah, Kuncoro W, Wahyuni S, Suwandi SP, Feditasari ED. 2020. The analysis of the causes of flood disasters and their impacts in the perspective of environmental The analysis of the causes of flood disasters and their impacts in the perspective of environmental law. IOP Conf. Ser. Earth Environ. Sci. 437:1–7.doi:10.1088/1755-1315/437/1/012056.
Shi L. 2020. Beyond flood risk reduction : How can green infrastructure advance both social justice and regional impact ? Socio-Ecological Pract. Res. 2(4):311–320.doi:10.1007/s42532-020-00065-0.
Sunarharum TM. 2021. Membangun Ketangguhan dan Adaptasi Transformatif : Kasus Pengurangan Risiko Bencana Banjir di Jakarta. Reka Ruang. 3(2):71–80.
Teimouri R, Hodjati H. 2017. Structural Analysis of Affecting Factors for Future Development of Green Spaces in Tabriz City. Asian Soc. Sci. 13(3):185–197.doi:10.5539/ass.v13n3p185.
Thanvisitthpon N. 2019. Impact of land use transformation and anti-flood infrastructure on flooding in world heritage site and peri-urban area: A case study of Thailand’s Ayutthaya province. J. Environ. Manage. 247:518–524.doi:https://doi.org/10.1016/j.jenvman.2019.06.094.
Titley HA, Cloke HL, Harrigan S, Pappenberger, Prudhomme C, Robbins C, Stephens EM, Zsótér. 2021. Key Factors Influencing the Severity of Fluvial Flood Hazard from Tropical Cyclones. J. Hydrometeorol. 22(12 Maret):1801–1817.doi:10.1175/JHM-D-20-0250.1.
Veltmeyer J, Sahin O. 2014. Modelling climate change adaptation using cross-impact analysis an approach for integrating qualitative and quantitative data Modelling climate change adaptation using cross-impact analysis : an approach for integrating qualitative and quantitative data. Int. Environ. Model. Softw. Soc.(June).
Walters J. 2018. A Systems Analysis of Factors Influencing Household Solar PV Adoption in Santiago , Chile. Sustainability. 10(April):1–17.doi:10.3390/su10041257.
Yilmaz AG, Hossain I, Perera BJC. 2014. Effect of climate change and variability on extreme rainfall intensity – frequency – duration relationships : a case study of Melbourne. Hydrol. Earth Syst. Sci.(November 2016).doi:10.5194/hess-18-4065-2014.
Authors
AriyaniD., Mohammad Yanuar Jarwadi Purwanto, Euis Sunarti and Perdinan (2022) “Contributing Factor Influencing Flood Disaster Using MICMAC (Ciliwung Watershed Case Study)”, Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management). Bogor, ID, 12(2), pp. 268-280. doi: 10.29244/jpsl.12.2.268-280.
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