Flood risk zoning in Jahrom urban basin using machine algorithm (Maxent

Document Type : پژوهشی

Authors

1 . Associate Professor, Department of Geography. Shiraz University. Shiraz. Iran. Department of Geography, shiraz university

2 Ph.D Student in Geomorphology, Mohaghegh Ardabili University, Ardabi. IranDepartment of geography, mohaghegh ardabili university

Abstract

Flood is one of the most destructive natural disasters with social, economic and environmental consequences. Therefore, comprehensive flood management is necessary to reduce the effects of floods on human life and livelihood. The main goal of this study is to investigate the application of the maximum entropy model (Entropy Maxent) in R software for flood susceptibility mapping in Fars province (Jahrom urban basin). First, by using the information of natural resources of Fars province and field visits, flood-prone points (50 points) were determined. In the next step, environmental variables such as altitude, slope, and distance from the river, drainage density, average annual rainfall, land use, soil type, and geology were selected by performing the multiple collinearity test, and vegetation cover and topographic humidity index were removed. Among the selected environmental variables, the three factors of height distance from the waterway and land use have had the greatest impact in the modeling process. After that, the receiver operating characteristic curve (ROC) was drawn for the flood sensitivity map, and the value of training data (0.943) and test data (0.932) was obtained. In the following, the model was optimized and upgraded using the genetic algorithm. As a result, this flood susceptibility map can be useful for researchers and planners in flood mitigation strategies.

Keywords

Main Subjects

References

  • Afifi, Mohammad Ibrahim. (2018). Evaluation of factors affecting flood risks and preparation of a map of sensitivity and probability of its occurrence using Shanun entropy model (case study: Firozabad River watershed). Natural Hazards Management, 6(2): pp.167-149.(in persian).
  • Amiri, Poua and Afifi, Mohammad Ebrahim and Moghli, Marzieh. (2022). Prospective research and flood risk assessment in Tehran using Support Vector Machine (SVM) algorithm. Natural Geography Quarterly, 14(55): pp. 77-93. (in persian).
  • Arabameri, A., Pradhan, B., Rezaei, K., & Conoscenti, C. (2019). Gully erosion susceptibility mapping using GIS-based multi-criteria decision analysis techniques. Catena, 180, 282-297.
  • Asghari Saraskanroud, Sayad., Pirouzi, Elnaz., and Zainali, Betul. (2014). Flood risk zoning in Agh Lakhanchai watershed using Vicor model. Quantitative Geomorphology Research, 4(3), pp. 231-245. (in persian).
  • Askari, Shams-oleh; Safari, Amir; Fathi, Hojjatullah. (2017). Investigating flood potential in Jafarabad watershed. Applied Research of Geographical Sciences, 50(17), pp. 77-90. (in persian).
  • Bakhtiari, Morteza, Kashfipour, Seyed Mahmoud, and Asghari Pari, Seyedamin. (2013). Investigating the impact of transverse structures on the flood zone using the HEC-RAS hydraulic model in the GIS geographic information system environment. Irrigation Science and Engineering, 35(3): 46-37.
  • Bui, Q.T., Q. H. Nguyen, X. L. Nguyen, V. D. Pham, H. D. Nguyen and V.M. Pham.(2020). Verification of novel integrations of swarm intelligence algorithms into deep learning neural network for flood susceptibility mapping. Journal of Hydrology 581: 124371-9.
  • Cabrera, J. S., & Lee, H. S. (2020). Flood risk assessment for Davao Oriental in the Philippines using geographic information system‐based multi‐criteria analysis and the maximum entropy model. Journal of Flood Risk Management, 13(2), e12607.
  • Dai FC, Lee CF, Li JX, Xu ZW.(2001) .Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environmental Geology, 40(3):381-91.
  • Davand, Khadijah, Shahabi, Hayman and Salari, Mamand. (2021) Flood risk zoning in the city of Ilam using the model based on definitive evidence, geography and environmental hazards. 38: pp. 1-20(in persian)..
  • Davoudi Moghaddam D, Pourghasemi HR, and Rahmati O .(2019). Assessment of the contribution of geo-environmental factors to flood inundation in a semi-arid region of SW Iran: comparison of different advanced modeling approaches. In: Advances in Natural and Technological Hazards Research (Vol 48, pp 59–78). Springer Netherlands. https:// doi. org/ 10. 1007/ 978-3- 319- 73383–8.
  • Esmaili, Reza, Qureshundi, Maryam, and Jokarsarhangi, Isa. (2017). Identification and ranking of areas under flood risk in the alluvial cones of North Izeh, Khuzestan province. Hydrogeomorphology, year 5, number 17, pp. 163-183. (in persian).
  • Faramarzi, Hassan, Hosseini, Seyed Mohsen, Pourqasmi, Hamidreza, and Farnaghi, Mehdi. (2018). Evaluation and zoning of flood risk in Golestan National Park. Ecohydrology, 6(4): pp. 1068-1055.
  • Fernández, D.S.; and M.A. Lutz. (2010). Urban flood hazard zoning in Tucumán Province Argentina, using GIS and multicriteria decision analysis. Engineering Geology, 111: 90–98.
  • Foss, J. E., Moormann, F. R., & Rieger, S. (1983). Inceptisols. In Developments in Soil Science(Vol. 11, pp. 355-381). Elsevier.
  • Franklin, J. (2013). Species distribution models in conservation biogeography: developments and challenges. Diversity and distributions, 19(10), 1217-1223.
  • Ghahrodi Tali, Manijeh. (2013). Vulnerability of the railway lines in the north of the Lut Plain against floods, Environmental Hazards, 1(2): pp. 1-18. (in persian).
  • Goldberg D .(1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Boston.
  • Gupta, K. (2020). Challenges in developing urban flood resilience in India. Philosophical Transactions of the Royal Society A, 378(2168), 20190211.
  • Hampf, A. C., T. Stella, M. Berg-Mohnicke, T. Kawohl, M. Kilian and C. Nendel. (2020). Future yields of double-cropping systems in the Southern Amazon, Brazil, under climate change and technological development. Agricultural Systems 177: 102707-16.
  • Holland JH .(1975). Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor.
  • Hosseinzadeh, Mohammad Mehdi, Salehipour, Alireza, Rezaian Zarandini, Fatemeh. (2023). Zoning of the sensitivity of sub-basins of the Nekarood basin to flooding, Neka-Mazandaran. Hydrogeomorphology. 10(34). pp. 75-100. (in persian).
  • Ismaili Yusuf, Yusofund Fariborz, Shabanlou Saeed, Izadbakhsh Mohammad Ali. (2023). Determining the most prone areas of flooding using the irregular background model (Maxent) in Marzdaran watershed, Tehran province. Journal of Water and Soil Sciences. 27(2): pp. 51-33. (in persian).
  • Khosravi K, Shahabi H, Pham BT, Adamowski J, Shirzadi A, Pradhan B, Prakash I .(2019). A comparative assessment of flood susceptibility modeling using multi-criteria decision-making analysis and machine learning methods. J Hydrol 573:311–323. https:// doi. org/ 10. 1016/j. jhydr ol. 2019. 03. 073.
  • Mahmood S, and Rahman A ur .(2019). Flash flood susceptibility modeling using geo-morphometric and hydrological approaches in Panjkora Basin, Eastern Hindu Kush, Pakistan. Environ Earth Sci. https:// doi. org/ 10. 1007/ s12665- 018- 8041-y.
  • McCall, J. (2005). Genetic algorithms for modelling and optimisation. Journal of computational and Applied Mathematics, 184(1), 205-222.
  • Miller JR, Ritter DF, Kochel RC. (1990). Morphometric assessment of lithologic controls on drainage basin evolution in the Crawford Upland, south-central Indiana. Am J Sci. 290:569– 599.
  • Mircholi, Fahima and Gholami, Isa and Broghni, Mehdi. (2023). Flood susceptibility zoning in Famnat watershed, Gilan province. Water and soil magazine. 37(6): 853-841.
  • Mobley, W., Sebastian, A., Highfield, W., & Brody, S. D. (2019). Estimating flood extent during Hurricane Harvey using maximum entropy to build a hazard distribution model. Journal of Flood Risk Management, 12, e12549.
  • Naderkhanlou, Vahid and Samadi Rahim, Ali and Mazaheri, Mehdi and Sepehari, Jalal. (2013). Numerical and laboratory simulation of flood caused by dam failure, 13th Iran Hydraulic Conference, Tabriz. (in persian).
  • Nguyen-Tien, V., Elliott, R. J., & Strobl, E. A. (2018). Hydropower generation, flood control and dam cascades: A national assessment for Vietnam. Journal of hydrology, 560, 109-126.
  • Panahi M, Jaafari A, Shirzadi A, Shahabi H, Rahmati O, Omidvar E, Bui DT (2021) Deep learning neural networks for spatially explicit prediction of flash flood probability. Geosci Front 12(3):101076. https:// doi. org/ 10. 1016/j. gsf. 2020. 09. 007.
  • Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecological modelling, 190(3-4), 231-259.
  • Qasimi, A. B., Isazade, V., & Berndtsson, R. (2024). Flood susceptibility prediction using MaxEnt and frequency ratio modeling for Kokcha River in Afghanistan. Natural Hazards, 120(2), 1367-1394.
  • Rahmati, O., Pourghasemi, H. R., & Zeinivand, H. (2016). Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province, Iran. Geocarto International, 31(1), 42-70.
  • Rostami, Nuruddin, and Kazemi, Yunus. (2018). Flood risk zoning in Ilam city using AHP and GIS method. Spatial Analysis of Environmental Hazards, 6(1): pp. 179-193. (in persian).
  • Sharifi Pichon, Mohammad, Omidret, Kamal, and Motzakar, Kausar. (2018). The use of cluster analysis and multivariate regression in the assessment of flood potential with emphasis on hydrogeomorphological parameters (case study: Maron watershed). Natural Environment Hazards, 8(21): pp. 75-92. (in persian).
  • Stockwell, D., & Peterson, A. T. (2003). Comparison of resolution of methods used in mapping biodiversity patterns from point-occurrence data. Ecological indicators, 3(3), 213-221.
  • Sullivan -Wiley, K. A., & Gianotti, A. G. S.(2017). Risk Perception in a Multi-Hazard Environment. World Development, article in press, 115.
  • 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.
  • Tsangaratos, P., & Ilia, I. (2017). Applying machine learning algorithms in landslide susceptibility assessments. In Handbook of neural computation(pp. 433-457). Academic Press.
  • Xie, S., Wu, W., Mooser, S., Wang, Q. J., Nathan, R., & Huang, Y. (2021). Artificial neural network based hybrid modeling approach for flood inundation modeling. Journal of Hydrology, 592, 125605.
  • Youssef, A. M., Pradhan, B., & Sefry, S. A. (2016). Flash flood susceptibility assessment in Jeddah city (Kingdom of Saudi Arabia) using bivariate and multivariate statistical models. Environmental Earth Sciences, 75(1), 12.
  • Zarei, Mehdi, Zandi, Rahman, Naimi Tabar, Mahnaz. (2022). Evaluation of flood potential using data mining models of support vector machine, chaid and random forest (case study: Farizi area Watershed). Watershed Management Journal, 13(25): pp. 133-144. (in persian).

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History

  • Receive Date: 12 June 2024
  • Revise Date: 24 October 2024
  • Accept Date: 29 October 2024

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