Flood Risk Zoning Using Geographical Information System Case Study: Khorramabad Flood in April 2019
Določanje območij poplavne ogroženosti z uporabo geografskega informacijskega sistema, študija primer: poplava v Kharramabadu, Iran, aprila 2019
- Avtorji: Parastoo Karimi, Payam Alemi Safaval, Saeed Behzadi, Zahra Azizi, Mir Masoud Kheirkhah Zarkash, Hamide Kavusi Kalashami
- Citat: Acta hydrotechnica, vol. 35, no. 63, pp. 89-100, 2022. https://doi.org/10.15292/acta.hydro.2022.07
- Povzetek: Danes obstajajo različne metode za določanje poplavne ogroženosti na različnih območjih povodja. Uporaba orodij GIS je med raziskovalci deležna vse več pozornosti. V začetku leta 2019 so se v nekaterih provincah Irana pojavile hude in neprekinjene poplave. Khorramabad je bilo eno izmed mest, ki so jih poplave najbolj prizadele. Zaradi gradbenih posegov v mestu je prišlo do poslabšanja stanja cest. V tej študiji so bila za to območje določena poplavno ogrožena območja z GIS-algoritmom utežnega prekrivanja. Določitev poplavnih območij je temeljila na podlagi različnih kart, kot so karte padavin, oddaljenosti od plovnih poti, pedoloških kart, gostote rečne mreže, naklona, prepustnosti tal, rabe tal in vegetacije. Poplave so bile razvrščene v šest kategorij s povratnimi dobami 10, 30 in 50 let. Na podlagi tega je bilo mesto razdeljeno v tri kritična območja glede poplavne ogroženosti. Rezultati kažejo, da sotočje rek Karganeh in Khorram-Rud nima potrebne zmogljivosti za preprečevanje poplav; v prihodnjih poplavah bo mesto znova utrpelo resno škodo.
- Ključne besede: Določanje poplavnih območij, utežno prekrivanje, GIS, reka Khorramrud, mesto Khorramabad.
- Polno besedilo: a35pk.pdf
- Viri:
- Abdollahi, A., Behzadi, S. (2022). Socio-Economic and Demographic Factors Associated with the Spatial Distribution of COVID-19 in Africa. Journal of Racial and Ethnic Health Disparities: 1–13.
- Alaghmand, S., Abdullah, R., Abustan, I., Vosoogh, B. (2010). GIS-based river flood hazard mapping in urban area (a case study in Kayu Ara River Basin, Malaysia). International Journal of Engineering and Technology, 2(6): 488–500.
- Baqalani et al. (2019). Identification of effective factors on the occurrence of urban floods in Ilam watershed. Watershed Engineering and Management, 11(2): 523–536.
- Basawaraja, R., Chari, K., Mise, S., Chetti, S. (2011). Analysis of the impact of urban sprawl in altering the land-use, land-cover pattern of Raichur City, India, using geospatial technologies. Journal of Geography and Regional Planning, 4(8): 455–462.
- Behzadi, S. (2020). An intelligent location and state reorganization of traffic signal. Geodesy and Cartography, 46(3): 145–150.
- Behzadi, S., Memarimoghadan, K. (2019). A Belief-Desire-Intention Agent-based procedure for urban land growth simulation. A case study of Tehran Metropolitan Region, Iran. Forum Geografic, 18(1): 53–62.
- Burton, A., Shepard, M., Riddell, K. (2003). LAND USE AND FLOOD RISK THROUGH CATCHMENT FLOOD‐MANAGEMENT PLANS. Water and Environment Journal, 17(4): 220–225.
- Charlton, R., Fealy, R., Moore, S., Sweeney, J., Murphy, C. (2006). Assessing the impact of climate change on water supply and flood hazard in Ireland using statistical downscaling and hydrological modelling techniques. Climatic Change, 74(4): 475–491. https://doi.org/10.1007/s10584-006-0472-x.
- Choubin, B. et al. (2019). An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines. Science of the Total Environment, 651: 2087–2096. https://doi.org/10.1016/j.scitotenv.2018.10.064.
- Goodarzi, M.R., Fatehifar, A., Moradi, A. (2020). Predicting future flood frequency under climate change using Copula function. Water and Environment Journal, 34: 710–727. https://doi.org/10.1111/wej.12572.
- Guha-Sapir, D., Vos, F., Below, R., Ponserre, S. (2012). Annual Disaster Statistical Review 2011: The Numbers and Trends, published by the Centre for Research on the Epidemiology of Disasters (CRED) Brussels.
- Heidari zadeh, K., Rahimi, S., Zahrakar, N., Jodaki, R. (2019). Investigation of flood incident in Khorram Abad city, The 9th International Health Congress in Accidents and Disasters, Tehran.
- Huang, X. et al. (2008). Flood hazard in Hunan province of China: an economic loss analysis. Natural Hazards, 47(1): 65–73. https://doi.org/10.1007/s11069-007-9197-z.
- Jabbar, F.K., Grote, K., Tucker, R.E. (2019). A novel approach for assessing watershed susceptibility using weighted overlay and analytical hierarchy process (AHP) methodology: a case study in Eagle Creek Watershed, USA. Environmental Science and Pollution Research, 26(31): 31981–31997. https://doi.org/10.1007/s11356-019-06355-9.
- Jalilzadeh, A., Behzadi, S. (2020). Flood Mapping and Estimation of Flood Water-Level Using Fuzzy Method and Remote Sensing Imagery (Case Study: Golestan Province, Iran), Forum Geografic. University of Craiova, Department of Geography, pp. 165.
- Khaledi, S., Behzadi, S. (2020). Monitoring and Assessing the Changes in the Coverage and Decline of Oak Forests in Lorestan Province using Satellite Images and BFAST Model. Journal of Applied researches in Geographical Sciences, 20(57): 265–280.
- Khosravi, K., Pourghasemi, H.R., Chapi, K., Bahri, M. (2016). Flash flood susceptibility analysis and its mapping using different bivariate models in Iran: a comparison between Shannon’s entropy, statistical index, and weighting factor models. Environmental monitoring and assessment, 188(12): 1–21. https://doi.org/10.1007/s10661-016-5665-9.
- Minea, G. (2013). Assessment of the flash flood potential of Basca river catchment (Romania) based on physiographic factors. Open Geosciences, 5(3): 344–353.
- Mousavi, Z., Behzadi, S. (2019). Geo-Portal Implementation with a Combined Approach of AHP and SWOT. International Journal of Natural Sciences Research, 7(1): 23–31.
- Norouzi, E., Behzadi, S. (2019). Evaluating machine learning methods and satellite images to estimate combined climatic indices. International Journal of Numerical Methods in Civil Engineering, 4(1): 30–38.
- Parhi, P.K. (2018). Flood management in Mahanadi Basin using HEC-RAS and Gumbel’s extreme value distribution. Journal of The Institution of Engineers (India): Series A, 99(4): 751–755.
- Pasha, A., Sorbi, A., Behzadi, S. (2018). Landslide risk assessment in Qazvin-Rasht quadrangle zone (North of Iran). Scientific Quarterly Journal of Geosciences, 27(106): 89–98.
- Qanavati et al. (2011). Efficiency of Hierarchical Analysis Method in Flood Studies. Geography, 31(9): 255–276.
- Rana, V.K., Suryanarayana, T.M.V. (2021). Estimation of flood influencing characteristics of watershed and their impact on flooding in data-scarce region. Annals of GIS, 27(4): 397–418.
- Saaty, T.L. (1980). Analytic hierarchy process. Wiley Online Library.
- Samela, C., Albano, R., Sole, A., Manfreda, S. (2018). A GIS tool for cost-effective delineation of flood-prone areas. Computers, Environment and Urban Systems, 70: 43–52.
- Scheuer, S., Haase, D., Volk, M. (2017). Integrative assessment of climate change for fast-growing urban areas: Measurement and recommendations for future research. PloS one, 12(12). https://doi.org/10.1371/journal.pone.0189451.
- Shiravand, H., Khaledi, S., Behzadi, S. (2019). Evaluation and Prediction of Decline of Oak Forests in Middle Zagros (Lorestan Section) with a Climate Change Approach. Iranian Journal of Forest and Range Protection Research, 17(1): 64–81.
- Siddayao, G.P., Valdez, S.E., Fernandez, P.L. (2014). Analytic hierarchy process (AHP) in spatial modeling for floodplain risk assessment. International Journal of Machine Learning and Computing, 4(5): 450.
- Slater, L.J., Singer, M.B., Kirchner, J.W. (2015). Hydrologic versus geomorphic drivers of trends in flood hazard. Geophysical Research Letters, 42(2): 370–376. https://doi.org/10.1002/2014GL062482.
- Steinberg, F., Lindfield, M. (2012). Spatial development and technologies for Green cities. Green Cities: 23.
- Tehrany, M.S., Pradhan, B., Jebur, M.N. (2013). Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS. Journal of Hydrology, 504: 69–79. https://doi.org/10.1016/j.jhydrol.2013.09.034.
- Tehrany, M.S., Pradhan, B., Mansor, S., Ahmad, N. (2015). Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. Catena, 125: 91–101. https://doi.org/10.1016/j.catena.2014.10.017.
- Udin, W.S., Binti Ismail, N.A., Bahar, A.M.A., Khan, M.M.A. (2018). GIS-based River flood hazard mapping in rural area: a case study in Dabong, Kelantan, Peninsular Malaysia. Asian Journal of Water, Environment and Pollution, 15(1): 47–55.
- Vij, N. (2007). Management of earthquakes national disaster management authority government of India. Fire Engineer, 32(2): 27–36.
- Wapwera, S.D., Egbu, C.O. (2013). Master planning system: Constraints for planning authorities in Jos Metropolis, Nigeria. The Built & Human Environment Review, 6: 61–81.
- Woldesenbet, T.A., Elagib, N.A., Ribbe, L., Heinrich, J. (2018). Catchment response to climate and land use changes in the Upper Blue Nile sub-basins, Ethiopia. Science of the total environment, 644: 193–206. https://doi.org/10.1016/j.scitotenv.2018.06.198.
- Wondrade, N., Dick, Q.B., Tveite, H. (2014). GIS based mapping of land cover changes utilizing multi-temporal remotely sensed image data in Lake Hawassa Watershed, Ethiopia. Environmental monitoring and assessment, 186(3): 1765–1780. https://doi.org/10.1007/s10661-013-3491-x.
- Xiao, Y., Xie, Y., Kulturel-Konak, S., Konak, A. (2017). A problem evolution algorithm with linear programming for the dynamic facility layout problem—A general layout formulation. Computers & Operations Research, 88: 187–207.
- Zayyari, K., Ebrahimipoor, M., Pourjafar, M.R., Salehi, E. (2020). Explaining Strategies for Increasing Physical Resilience against Flood Case Study: Cheshmeh Kile River, Tonekabon River. Sustainable city, 3(1): 89–105.
- Zhao, G., Pang, B., Xu, Z., Yue, J., Tu, T. (2018). Mapping flood susceptibility in mountainous areas on a national scale in China. Science of the Total Environment, 615: 1133–1142. https://doi.org/10.1016/j.scitotenv.2017.10.037.