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All issues Volume 400 (2023) E3S Web Conf., 400 (2023) 01004 References
Open Access
Issue
E3S Web Conf.
Volume 400, 2023
International Conference on Sciences, Mathematics, and Education (ICoSMEd 2022)
Article Number 01004
Number of page(s) 9
Section Theory and Application in Physics
DOI https://doi.org/10.1051/e3sconf/202340001004
Published online 03 July 2023
  1. M. A. Widiawaty and M. Dede, “Pemodelan Spasial Bahaya Dan Kerentanan Bencana Banjir Di Wilayah Timur Kabupaten Cirebon, ” J. Dialog Penanggulangan Bencana, vol. 9, no. 2, pp. 142–153, 2018. [Google Scholar]
  2. BNPB, “Data Informasi Bencana Indonesia (DIBI), ” Jakarta Timur, 2019. [Online]. Available: https://dibi.bnpb.go.id/xdibi/read/31156/73/16//2019/04/2//2 [Google Scholar]
  3. G. D. Bathrellos, H. D. Skilodimou, K. Chousianitis, A. M. Youssef, and B. Pradhan, “Suitability estimation for urban development using multi-hazard assessment map, ” Sci. Total Environ., vol. 575, pp. 119–134, 2017, doi: 10.1016/j.scitotenv.2016.10.025. [CrossRef] [Google Scholar]
  4. A. Ezzine, S. Saidi, T. Hermassi, I. Kammessi, F. Darragi, and H. Rajhi, “Flood mapping using hydraulic modeling and Sentinel-1 image: A case study of Medjerda Basin, northern Tunisia, ” Egypt. J. Remote Sens. Sp. Sci., vol. 23, no. 3, pp. 303–310, 2020, doi: 10.1016/j.ejrs.2020.03.001. [Google Scholar]
  5. B. K. Tjasyono, I. Juaeni, and W. B. Harijono, “Proses Meteorologis Bencana Banjir, ” J. Meteorol. dan Geofis., vol. 8, no. 2, pp. 64–78, 2007. [Google Scholar]
  6. D. Suryanto, Tanah Airku Salah Kelola Hujan. Yogyakarta: Deepbulish, 2016. [Google Scholar]
  7. S. Wulandarie, “Integrasi Sig-Infoworks River Simulation Untuk Pemodelan Hidrodinamik Sungai Saddang Dan Sungai Mata Allo, ” J. Environ. Sci., vol. 2, no. 2, p. 178, 2020, doi: 10.35580/jes.v2i2.13331. [Google Scholar]
  8. H. Rachmayanti, R. Musa, and A. Mallombasi, “Studi Pengaruh Perubahan Tata Guna Lahan Terhadap Debit Banjir Dengan Menggunakan Software HEC HMS (Studi Kasus DAS Saddang), ” vol. 01, no. 01, pp. 1–9, 2022. [Google Scholar]
  9. T. Cahyono, M. P. Hadi, and D. Mardianto, “Pemodelan Spasial untuk Pembuatan Peta Rawan Banjir dan Peta Tingkat Risiko Banjir Bengawan Solo di Kota Surakarta, ” Maj. Geogr. Indones., vol. 29, no. 1, p. 32, 2015, doi: https://doi.org/10.22146/mgi.13102. [Google Scholar]
  10. N. A. Haris, S. S. Kusuma, S. Arjasakusuma, and P. Wicaksono, “Comparison of Sentinel-2 and Multitemporal Sentinel-1 SAR Imagery for Mapping Aquaculture Pond Distribution in the Coastal Region of Brebes Regency, Central Java, Indonesia, ” Geogr. Tech., vol. 16, no. Special Issue, pp. 128–137, 2021, doi: 10.21163/GT_2021.163.10. [Google Scholar]
  11. B. Haack and M. Bechdol, “Integrating multisensor data and RADAR texture measures for land cover mapping, ” Comput. Geosci., vol. 26, no. 4, pp. 411–421, 2000, doi: 10.1016/S00983004(99)00121-1. [CrossRef] [Google Scholar]
  12. N. D. Herold, B. N. Haack, and E. Solomon, “An evaluation of radar texture for land use/cover extraction in varied landscapes, ” Int. J. Appl. Earth Obs. Geoinf., vol. 5, no. 2, pp. 113–128, 2004, doi: 10.1016/j.jag.2004.01.005. [Google Scholar]
  13. S. Chen, J. Useya, and H. Mugiyo, “Decision-level fusion of Sentinel-1 SAR and Landsat 8 OLI texture features for crop discrimination and classification: case of Masvingo, Zimbabwe, ” Heliyon, vol. 6, no. 11, p. e05358, 2020, doi: 10.1016/j.heliyon.2020.e05358. [CrossRef] [PubMed] [Google Scholar]
  14. I. A. El-Magd, E. Hermas, and M. El Bastawesy, “GIS-modelling of the spatial variability of flash flood hazard in Abu Dabbab catchment, Red Sea Region, Egypt, ” Egypt. J. Remote Sens. Sp. Sci., vol. 13, no. 1, pp. 81–88, 2010, doi: 10.1016/j.ejrs.2010.07.010. [Google Scholar]
  15. R. Cheah, L. Billa, A. Chan, F. Y. Teo, B. Pradhan, and A. M. Alamri, “Geospatial modeling of watershed peak flood discharge in Selangor, Malaysia, ” Water (Switzerland), vol. 11, no. 12, pp. 1–12, 2019, doi: 10.3390/w11122490. [Google Scholar]
  16. A. Tegos, A. Ziogas, V. Bellos, and A. Tzimas, “Forensic Hydrology: A Complete Reconstruction of an Extreme Flood Event in Data-Scarce Area, ” Hydrology, vol. 9, no. 5, p. 93, 2022, doi: 10.3390/hydrology9050093. [CrossRef] [Google Scholar]
  17. J. T. Samarasinghe, V. Basnayaka, M. B. Gunathilake, H. M. Azamathulla, and U. Rathnayake, “Comparing Combined 1D/2D and 2D Hydraulic Simulations Using High-Resolution Topographic Data: Examples from Sri Lanka— Lower Kelani River Basin, ” Hydrology, vol. 9, no. 2, pp. 1–17, 2022, doi: 10.3390/hydrology9020039. [Google Scholar]
  18. V. K. Rana and T. M. V. Suryanarayana, “Evaluation of SAR speckle filter technique for inundation mapping, ” Remote Sens. Appl. Soc. Environ., vol. 16, no. October, p. 100271, 2019, doi: 10.1016/j.rsase.2019.100271. [Google Scholar]
  19. P. O. Gislason, J. A. Benediktsson, and J. R. Sveinsson, “Random forests for land cover classification, ” Pattern Recognit. Lett., vol. 27, no. 4, pp. 294–300, 2006, doi: 10.1016/j.patrec.2005.08.011. [CrossRef] [Google Scholar]
  20. A. D. Kulkarni and B. Lowe, “Random Forest Algorithm for Land Cover Classification, ” Int. J. Recent Innov. Trends Comput. Commun., vol. 4, no. 3, pp. 58–63, 2016, [Online]. Available: https://scholarworks.uttyler.edu/cgi/viewcontent.cgi?article=1002&context=compsci_fac [Google Scholar]
  21. S. Rapinel, E. Fabre, S. Dufour, D. Arvor, C. Mony, and L. Hubert-Moy, “Mapping potential, existing and efficient wetlands using free remote sensing data, ” J. Environ. Manage., vol. 247, no. November 2018, pp. 829–839, 2019, doi: 10.1016/j.jenvman.2019.06.098. [Google Scholar]
  22. C. B. Obida, G. A. Blackburn, J. D. Whyatt, and K. T. Semple, “River network delineation from Sentinel-1 SAR data, ” Int. J. Appl. Earth Obs. Geoinf., vol. 83, no. June, p. 101910, 2019, doi: 10.1016/j.jag.2019.101910. [Google Scholar]
  23. A. Hardy et al., “Automatic detection of open and vegetated water bodies using Sentinel 1 to map African malaria vector mosquito breeding habitats, ” Remote Sens., vol. 11, no. 5, 2019, doi: 10.3390/rs11050593. [CrossRef] [Google Scholar]
  24. M. Ottinger, K. Clauss, J. Huth, C. Eisfelder, P. Leinenkugel, and C. Kuenzer, “Time series sentinel-1 SAR data for the mapping of aquaculture ponds in coastal Asia, ” Int. Geosci. Remote Sens. Symp., vol. 2018-July, pp. 9371–9374, 2018, doi: 10.1109/IGARSS.2018.8651419. [Google Scholar]
  25. S. Manfreda, M. Di Leo, and A. Sole, “Detection of Flood-Prone Areas Using Digital Elevation Models, ” J. Hydrol. Eng., vol. 16, no. 10, pp. 781–790, 2011, doi: 10.1061/(asce)he.19435584.0000367. [CrossRef] [Google Scholar]
  26. F. Bioresita, A. Puissant, A. Stumpf, and J. P. Malet, “Fusion of Sentinel-1 and Sentinel-2 image time series for permanent and temporary surface water mapping, ” Int. J. Remote Sens., vol. 40, no. 23, pp. 9026–9049, 2019, doi: 10.1080/01431161.2019.1624869. [Google Scholar]
  27. M. Haq, M. Akhtar, S. Muhammad, S. Paras, and J. Rahmatullah, “Techniques of Remote Sensing and GIS for flood monitoring and damage assessment: A case study of Sindh province, Pakistan, ” Egypt. J. Remote Sens. Sp. Sci., vol. 15, no. 2, pp. 135–141, 2012, doi: 10.1016/j.ejrs.2012.07.002. [Google Scholar]
  28. S. Tian, X. Zhang, J. Tian, and Q. Sun, “Random forest classification of wetland landcovers from multi-sensor data in the arid region of Xinjiang, China, ” Remote Sens., vol. 8, no. 11, pp. 1–14, 2016, doi: 10.3390/rs8110954. [Google Scholar]
  29. Gond V., E. Bartholomé, F. Ouattara, A. Nonguierma, and L. Bado, “Mapping and monitoring small ponds in dryland with VEGETATION instrument application to West Africa, ” Proceeding Veg., no. April, pp. 327–333, 2000. [Google Scholar]
  30. J. V. Soares, C. D. Rennó, A. R. Formaggio, C. D. C. F. Yanasse, and A. C. Frery, “An investigation of the selection of texture features for crop discrimination using SAR imagery, ” Remote Sens. Environ., vol. 59, no. 2, pp. 234–247, 1997, doi: 10.1016/S0034-4257(96)00156-3. [CrossRef] [Google Scholar]
  31. V. Demir and O. Kisi, “Flood Hazard Mapping by Using Geographic Information System and Hydraulic Model: Mert River, Samsun, Turkey, ” Adv. Meteorol., vol. 2016, 2016, doi: 10.1155/2016/4891015. [CrossRef] [Google Scholar]
  32. H. W. Chung, C. C. Liu, I. F. Cheng, Y. R. Lee, and M. C. Shieh, “Rapid response to a typhooninduced flood with an SAR-derived map of inundated areas: Case study and validation, ” Remote Sens., vol. 7, no. 9, pp. 11954–11973, 2015, doi: 10.3390/rs70911954. [CrossRef] [Google Scholar]
  33. B. Slagter, N.-E. Tsendbazar, A. Vollrath, and J. Reiche, “Mapping wetland characteristics using temporally dense Sentinel-1 and Sentinel-2 data: A case study in the St. Lucia wetlands, South Africa, ” Int. J. Appl. Earth Obs. Geoinf., vol. 86, no. October 2019, p. 102009, 2020, doi: 10.1016/j.jag.2019.102009. [Google Scholar]
  34. Z. Zeng et al., “Towards high resolution flood monitoring: An integrated methodology using passive microwave brightness temperatures and Sentinel synthetic aperture radar imagery, ” J. Hydrol., vol. 582, no. November 2019, p. 124377, 2020, doi: 10.1016/j.jhydrol.2019.124377. [CrossRef] [Google Scholar]
  35. G. Brandon and B. Bradley, “Classification Algorithms Random Forest Working of Random Forest Algorithm, ” Mach. Learn., vol. 3, pp. 1–5, 2020. [Google Scholar]
  36. S. A. Mohamed, “Application of satellite image processing and GIS-Spatial modeling for mapping urban areas prone to flash floods in Qena governorate, Egypt, ” J. African Earth Sci., vol. 158, no. March, p. 103507, 2019, doi: 10.1016/j.jafrearsci.2019.05.015. [CrossRef] [Google Scholar]
  37. J. S. Cabrera and H. S. Lee, “Flood-prone area assessment using GIS-based multi-criteria analysis: A case study in Davao Oriental, Philippines, ” Water (Switzerland), vol. 11, no. 11, 2019, doi: 10.3390/w11112203. [Google Scholar]
  38. N. Machineni, V. S. P. Sinha, P. Singh, and N. T. Reddy, “The impact of distributed landuse information in hydrodynamic model application in storm surge inundation, ” Estuar. Coast. Shelf Sci., vol. 231, no. September, p. 106466, 2019, doi: 10.1016/j.ecss.2019.106466. [CrossRef] [Google Scholar]
  39. T. Nharo, H. Makurira, and W. Gumindoga, “Mapping floods in the middle Zambezi Basin using earth observation and hydrological modeling techniques, ” Phys. Chem. Earth, vol. 114, no. June, p. 102787, 2019, doi: 10.1016/j.pce.2019.06.002. [CrossRef] [Google Scholar]
  40. A. N. Matori, D. U. Lawal, K. W. Yusof, M. A. Hashim, and A. L. Balogun, “Spatial analytic hierarchy process model for flood forecasting: An integrated approach, ” IOP Conf. Ser. Earth Environ. Sci., vol. 20, no. 1, 2014, doi: 10.1088/1755-1315/20/1/012029. [CrossRef] [Google Scholar]

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