Pemetaan Zonasi Banjir Bandang Menggunakan Metode Frekuensi Rasio dan Weight of Evidence di Sumberjambe Jember

Afif Amiluddin, Icha Tatrisya Derka, Maulida Faradella, Devi Hanurani Sugianti, Katminto Katminto, Elida Novita, Entin Hidayah

Abstract


Bencana banjir bandang di kecamatan Sumberjambe mengakibatkan kerusakan saluran air bersih bagi masyarakat. Kondisi tersebut menunjukan perlunya informasi area yang aman untuk pemasangan pipa air bersih. Hal ini menunujukan  diperlukannya informasi mengenai zonasi area banjir bandang, sebagai bentuk antisipasi dan mitigasi supaya tidak berdampak serupa dan mengurangi potensi korban jiwa. Tujuan umum Penelitian ini adalah membuat peta zonasi bencana banjir bandang berbasis Sistem Informasi geografis (SIG) dan Penginderaan Jauh. Tujuan Khusus penelitian mencakup, Identifikasi dan inventarisasi data berbasis survei lapangan dan penginderaan jauh. Menentukan faktor alam yang berpengaruh yaitu, Elevasi, Kemiringan Tanah, Indeks kesehatan tanaman (NDVI), Tataguna Lahan, Jarak Terhadap Sungai, Kepadatan Sungai, dan Indeks Kelembapan Topografi (TWI). Menentukan faktor yang memiliki kontribusi besar terhadap kejadian  menggunakan statistik bivariat Frekuensi Rasio (FR) dan Weight of Evidence (WoE). Penelitian ini terdiri dari survei lapangan kejadian banjir bandang, pembagian sampel, inventarisasi data faktor, perhitungan bobot faktor, perhitungan faktor dan Analisis Area Under the Curve (AUC). Hasil evaluasi AUC menunjukan tingkat akurasi FR 0.96 dan WoE0.82, FR menghasilkan model dengan akurasi yang lebih baik di wilayah studi.


Keywords


Banjir bandang; pemetaan kerentanan; sistem informasi geografis; penginderaan jauh; rasio frekuensi; weight of evidence; mitigasi bencana

References


A. Bannari, G. Kadhem, A. El-Battay, & N. Hameid. (2018). Comparison of SRTM-V4.1 and ASTER-V2.1 for Accurate Topographic Attributes and Hydrologic Indices Extraction in Flooded Areas. Journal of Earth Science and Engineering, 8(1), 8–30. https://doi.org/10.17265/2159-581x/2018.01.002

Abbas, A. S. (2023). Watershed Delineation from DEM by Model Builder in ArcGIS. 9(11), 2796–2805.

Abdollahi, A., Pradhan, B., Alamri, A., & Lee, C. W. (2023). Google Earth Engine for Advanced Land Cover Analysis from Landsat-8 Data with Spectral and Topographic Insights. Journal of Sensors, 2023(Ml). https://doi.org/10.1155/2023/6657171

Afif, A., & Tatrisya derka, I. (2025). Identifikasi Karakteristik Sub-DAS Sungai Biting Menggunakan DEMNAS dan STRM. 17(1), 1–12. https://doi.org/10.30736/jt.v17i1.1263

Aloscious, A. A., Artuso, M., & Torabi Moghadam, S. (2025). Nature-Based Solutions for Flood Mitigation: The Case Study of Kochi. Sustainability (Switzerland), 17(5). https://doi.org/10.3390/su17051983

Aprilia, R., Hidayah, E., & Junita K, D. (2021). Frequency ratio application for mapping flood susceptibility in Welang Watershed, East Java. IOP Conference Series: Earth and Environmental Science, 930(1). https://doi.org/10.1088/1755-1315/930/1/012095

Band, S. S., Janizadeh, S., Pal, S. C., Saha, A., Chakrabortty, R., Melesse, A. M., & Mosavi, A. (2020). Flash Flood Susceptibility Modeling Using New Approaches of Hybrid and Ensemble Tree-Based Machine Learning Algorithms. Remote Sensing, 12(21), 1–23. https://doi.org/10.3390/rs12213568

Benedict, & Jaelani, L. M. (2021). A Long-term Spatial and Temporal Analysis of NDVI Changes in Java Island Using Google Earth Engine. IOP Conference Series: Earth and Environmental Science, 936(1). https://doi.org/10.1088/1755-1315/936/1/012038

Cea, L., & Costabile, P. (2022). Flood Risk in Urban Areas: Modelling, Management and Adaptation to Climate Change: A Review. Hydrology, 9(3). https://doi.org/10.3390/hydrology9030050

Chowdhury, M. S. (2024). Flash flood susceptibility mapping of north-east depression of Bangladesh using different GIS based bivariate statistical models. Watershed Ecology and the Environment, 6(September 2023), 26–40. https://doi.org/10.1016/j.wsee.2023.12.002

Costache, R. (2019). Flash-flood Potential Index mapping using weights of evidence, decision Trees models and their novel hybrid integration. Stochastic Environmental Research and Risk Assessment, 33(7), 1375–1402. https://doi.org/10.1007/s00477-019-01689-9

Costache, R., Arabameri, A., Blaschke, T., Pham, Q. B., Pham, B. T., Pandey, M., Arora, A., Linh, N. T. T., & Costache, I. (2021). Flash-flood potential mapping using deep learning, alternating decision trees and data provided by remote sensing sensors. Sensors (Switzerland), 21(1), 1–21. https://doi.org/10.3390/s21010280

De Rosa, P., Fredduzzi, A., Minelli, A., & Cencetti, C. (2018). Automaticweb procedure for calculating flood flow frequency. Water (Switzerland), 11(1), 1–22. https://doi.org/10.3390/w11010014

Dutta, M., Saha, S., Saikh, N. I., Sarkar, D., & Mondal, P. (2023). Application of bivariate approaches for flood susceptibility mapping: a district level study in Eastern India. HydroResearch, 6, 108–121. https://doi.org/10.1016/j.hydres.2023.02.004

Febriyanti, I. H., Rahardjo, A. P., & ... (2024). Identifikasi dan Analisa Kerentanan Banjir Bandang di DAS Cimanuk Hulu Garut dengan Model Shannon’s Entropy. Jurnal ALAMI: Jurnal …, 8(1). https://ejurnal.bppt.go.id/index.php/Alami/article/view/5718

Khosravi, K., Nohani, E., Maroufinia, E., & Pourghasemi, H. R. (2016). A GIS-based flood susceptibility assessment and its mapping in Iran: a comparison between frequency ratio and weights-of-evidence bivariate statistical models with multi-criteria decision-making technique. Natural Hazards, 83(2), 947–987. https://doi.org/10.1007/s11069-016-2357-2

Krvavica, N., Šiljeg, A., Horvat, B., & Panđa, L. (2023). Pluvial Flash Flood Hazard and Risk Mapping in Croatia: Case Study in the Gospić Catchment. Sustainability (Switzerland), 15(2). https://doi.org/10.3390/su15021197

Liu, J., Xiong, J., Chen, Y., Sun, H., Zhao, X., & Tu, F. (2023). A new avenue to improve the performance of integrated modeling for flash flood susceptibility assessment : Applying cluster algorithms. 146(November 2022). https://doi.org/10.1016/j.ecolind.2022.109785

Maillard, O., Vides-Almonacid, R., Salazar, Á., & Larrea-Alcazar, D. M. (2023). Effect of Deforestation on Land Surface Temperature in the Chiquitania Region, Bolivia. Land, 12(1). https://doi.org/10.3390/land12010002

Majeed, M., Lu, L., Anwar, M. M., Tariq, A., Qin, S., El-Hefnawy, M. E., El-Sharnouby, M., Li, Q., & Alasmari, A. (2023). Prediction of flash flood susceptibility using integrating analytic hierarchy process (AHP) and frequency ratio (FR) algorithms. Frontiers in Environmental Science, 10(January), 1–14. https://doi.org/10.3389/fenvs.2022.1037547

Maulana, B. I., Hidayah, E., & Halik, G. (2023). Flood Susceptibility Mapping in Gending District by Comparison Frequency Ratio and Weight of Evidence for Mitigation Strategy. UKaRsT, 7(1), 17–32. https://doi.org/10.30737/ukarst.v7i2.3999

Megahed, H. A., Abdo, A. M., AbdelRahman, M. A. E., Scopa, A., & Hegazy, M. N. (2023). Frequency Ratio Model as Tools for Flood Susceptibility Mapping in Urbanized Areas: A Case Study from Egypt. Applied Sciences (Switzerland), 13(16). https://doi.org/10.3390/app13169445

Negese, A., Worku, D., Shitaye, A., & Getnet, H. (2022). Potential flood-prone area identification and mapping using GIS-based multi-criteria decision-making and analytical hierarchy process in Dega Damot district, northwestern Ethiopia. Applied Water Science, 12(12), 1–21. https://doi.org/10.1007/s13201-022-01772-7

Pandey, M., Arora, A., Arabameri, A., Costache, R., Kumar, N., Mishra, V. N., Nguyen, H., Mishra, J., Siddiqui, M. A., Ray, Y., Soni, S., & Shukla, U. K. (2021). Flood Susceptibility Modeling in a Subtropical Humid Low-Relief Alluvial Plain Environment: Application of Novel Ensemble Machine Learning Approach. Frontiers in Earth Science, 9(December), 1–26. https://doi.org/10.3389/feart.2021.659296

Parajuli, G., Neupane, S., Kunwar, S., Adhikari, R., & Acharya, T. D. (2023). A GIS-Based Evacuation Route Planning in Flood-Susceptible Area of Siraha Municipality, Nepal. ISPRS International Journal of Geo-Information, 12(7). https://doi.org/10.3390/ijgi12070286

Peng, X., Chen, S., Miao, Z., Xu, Y., Ye, M., & Lu, P. (2025). Automatic Flood Monitoring Method with SAR and Optical Data Using Google Earth Engine. Water (Switzerland), 17(2). https://doi.org/10.3390/w17020177

Popa, M. C., Peptenatu, D., Draghici, C. C., & Diaconu, D. C. (2019). Flood hazard mapping using the flood and Flash-Flood Potential Index in the Buzau River catchment, Romania. Water (Switzerland), 11(10). https://doi.org/10.3390/w11102116

Rane, N. L., & Jayaraj, G. K. (2022). Comparison of multi-influence factor, weight of evidence and frequency ratio techniques to evaluate groundwater potential zones of basaltic aquifer systems. Environment, Development and Sustainability, 24(2), 2315–2344. https://doi.org/10.1007/s10668-021-01535-5

Rani Mahanta, A., & Singh Rawat, K. (2020). Comparative Analysis of CARTOSAT, ASTER and SRTM Digital Elevation Models for Kancheepuram Watershed. International Journal of Advanced Research in Engineering and Technology (IJARET), 11(5), 827–834. https://iaeme.com/Home/issue/

Saha, A., & Saha, S. (2020). Comparing the efficiency of weight of evidence, support vector machine and their ensemble approaches in landslide susceptibility modelling: A study on Kurseong region of Darjeeling Himalaya, India. Remote Sensing Applications: Society and Environment, 19, 100323. https://doi.org/10.1016/j.rsase.2020.100323

Spatial, G. I. S., Case, A., Valea, S., & Catchment, R. (2022). Flash Flood Vulnerability Mapping Based on FFPI Using. Mdpi, 22(9). https://doi.org/10.3390/s22093573

Tariq, A., Yan, J., Ghaffar, B., Qin, S., Mousa, B. G., Sharifi, A., Huq, M. E., & Aslam, M. (2022). Flash Flood Susceptibility Assessment and Zonation by Integrating Analytic Hierarchy Process and Frequency Ratio Model with Diverse Spatial Data. Water (Switzerland), 14(19). https://doi.org/10.3390/w14193069

Yariyan, P., Avand, M., Abbaspour, R. A., Torabi Haghighi, A., Costache, R., Ghorbanzadeh, O., Janizadeh, S., & Blaschke, T. (2020). Flood susceptibility mapping using an improved analytic network process with statistical models. Geomatics, Natural Hazards and Risk, 11(1), 2282–2314. https://doi.org/10.1080/19475705.2020.1836036

Zhang, Y., Liu, T., Wang, Y., Zhang, M., & Zheng, Y. (2021). A spatial and temporal analysis on NDVI in Urad grassland during 2010-2019 over remote sensing. MATEC Web of Conferences, 336, 06029. https://doi.org/10.1051/matecconf/202133606029

Zhou, G., Dong, W., & Wei, H. (2019). A fast and simple algorithm for calculating flow accumulation matrices from raster digital elevation models. Abstracts of the ICA, 1(July), 1–2. https://doi.org/10.5194/ica-abs-1-434-2019




DOI: https://doi.org/10.24198/jt.vol20n1.13

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