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Application of entropy weighting method for urban flood hazard mapping

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Flooding is one of the most frequently occurring natural hazards worldwide. Mapping and assessment of possible flood hazards are critical components of the evaluation and mitigation of flood risk. In this study, six flood-related indices, i.e., slope, elevation, distance to discharge channel, runof volume, street-drainage network intersection, index of the development and persistence of the drainage network (IDPR), were used to assess the flood hazard. The entropy weighting method was used for assigning the weights to flood-related indices and combining them to prepare urban food hazard mapping in Hamadan city. The produced map showed that nearly 20% of the study area (14.7 km2 ) corresponded to very high susceptibility to flooding, 19.4% (143 km2 ) to high susceptibility and 20.3%, 20.7% and 19.6% regard the moderate, low and very low susceptibility to flooding, respectively. Finally, two methods were used to evaluate the accuracy of the produced food susceptibility map. The frst method is related to assessing the behavior of the map by making and propagating error in foodrelated indices and used model (entropy weighting method), and the second method is superimposing method. The results showed that by making and propagation of error, the behavior of producing food susceptibility mapping, the produced map has a robust behavior either in ranking importance of flood-related indices and percentage of food susceptibility areas. On the other hand, regarding the result of the superimposing method, the accuracy of the flood susceptibility map was 72%, which also suggests an acceptable result.
Czasopismo
Rocznik
Strony
841--854
Opis fizyczny
Bibliogr. 77 poz.
Twórcy
  • Department of Watershed Management, Faculty of Natural Resources, Yazd University, Yazd, Iran
  • Department of Watershed Management, Faculty of Natural Resources, Yazd University, Yazd, Iran
  • Environmental Quality, Atmospheric Science and Climate Change Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam
  • Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam
  • Department of Watershed Management, Faculty of Natural Resources, Yazd University, Yazd, Iran
  • Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam
  • Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
autor
  • Department of Geography and Regional Development, Faculty of Natural Sciences, Constantine the Philosopher University in Nitra, Trieda A. Hlinku 1, 94901 Nitra, Slovakia
  • Department of Geography and Regional Development, Faculty of Natural Sciences, Constantine the Philosopher University in Nitra, Trieda A. Hlinku 1, 94901 Nitra, Slovakia
Bibliografia
  • 1. Bell CD, Tague CL, McMillan SK (2019) Modeling runoff and nitrogen loads from a watershed at different levels of impervious surface coverage and connectivity to storm water control measures. Water Resour Res 55:2690–2707
  • 2. Bertsimas D, Pachamanova D, Sim M (2004) Robust linear optimization under general norms. Oper Res Lett 32:510–516
  • 3. Bilal N, 2014. Implementation of Sobol’s method of global sensitivity analysis to a compressor simulation model.
  • 4. Büchele B, Kreibich H, Kron A, Thieken A, Ihringer J, Oberle P, Merz B, Nestmann F (2006) Flood-risk mapping: contributions towards an enhanced assessment of extreme events and associated risks. Nat Hazards Earth Syst Sci 6:485–503
  • 5. Chen Z-M, Yeh Y-L, Chen T-C (2018) Assessment of a regional flood disaster indicator via an entropy weighting method. Nat Hazard Rev 19:05018002
  • 6. Clark PU, Mix AC, Eby M, Levermann A, Rogelj J, Nauels A, Wrathall DJ (2018) Sea-level commitment as a gauge for climate policy. Nat Clim Chang 8:653
  • 7. Costache R (2019a) Flash-flood potential assessment in the upper and middle sector of Prahova river catchment (Romania). A comparative approach between four hybrid models. Sci Total Environ 659:1115–1134
  • 8. Costache R (2019b) Flash-flood potential index mapping using weights of evidence, decision Trees models and their novel hybrid integration. Stoch Env Res Risk Assess 33(7):1375–1402
  • 9. Costache R (2019c) Flood susceptibility assessment by using bivariate statistics and machine learning models - a useful tool for flood risk management. Water Resour Manage 33(9):3239–3256
  • 10. Costache R, Hong H, Pham QB (2020a) Comparative assessment of the flash-flood potential within small mountain catchments using bivariate statistics and their novel hybrid integration with machine learning models. Sci Total Environ 711:134514
  • 11. Costache R, Pham QB, Sharifi E, Linh NTT, Abba S, Vojtek M, Vojteková J, Nhi PTT, Khoi DN (2020b) Flash-flood susceptibility assessment using multi-criteria decision making and machine learning supported by remote sensing and gis techniques. Remote Sensing 12:106
  • 12. Covino T (2017) Hydrologic connectivity as a framework for understanding biogeochemical flux through watersheds and along fluvial networks. Geomorphology 277:133–144
  • 13. Cox LA (2009) Limitations of risk assessment using risk matrices. Risk Analysis of Complex and Uncertain Systems. Springer, pp 101–124
  • 14. Cronshey R, Roberts R, Miller N, 1986. Urban hydrology for small watersheds. Technical report, US Dept. of Agriculture, Soil Conservation Service, Engineering Division.
  • 15. de Mello Silva C, da Silva GBL (2020) Cumulative effect of the disconnection of impervious areas within residential lots on runoff generation and temporal patterns in a small urban area. J Environ Manage 253:109719
  • 16. Devi NN, Sridharan B, Kuiry SN (2019) Impact of urban sprawl on future flooding in Chennai city, India. J Hydrol 574:486–496
  • 17. Eini M, Kaboli HS, Rashidian M, Hedayat H (2020) Hazard and vulnerability in urban flood risk mapping: Machine learning techniques and considering the role of urban districts. Int J Disaster Risk Reduc 50:101687
  • 18. Elkhrachy I (2015) Flash flood hazard mapping using satellite images and GIS tools: a case study of Najran City, Kingdom of Saudi Arabia (KSA). The Egypt J Remote Sens Space Sci 18:261–278
  • 19. El Ghaoui L, Lebret H (1997) Robust solutions to least-squares problems with uncertain data. SIAM J Matrix Anal Appl 18:1035–1064
  • 20. Fernández D, Lutz M (2010) Urban flood hazard zoning in Tucumán Province, Argentina, using GIS and multicriteria decision analysis. Eng Geol 111:90–98
  • 21. Forsee WJ, Ahmad S (2011) Evaluating urban storm-water infrastructure design in response to projected climate change. J Hydrol Eng 16:865–873
  • 22. Garg H, Agarwal N, Tripathi A (2017) Generalized intuitionistic fuzzy entropy measure of order α and degree β and its applications to multi-criteria decision making problem. Int J Fuzzy Syst Appl (IJFSA) 6:86–107
  • 23. Gay A, Cerdan O, Mardhel V, Desmet M (2016) Application of an index of sediment connectivity in a lowland area. J Soils Sediments 16:280–293
  • 24. Haghizadeh A, Siahkamari S, Haghiabi AH, Rahmati O (2017) Forecasting flood-prone areas using Shannon’s entropy model. J Earth Syst Sci 126:39
  • 25. Heckmann T, Cavalli M, Cerdan O, Förster S, Javaux M, Lode E, Smetanova A, Vericat D, Brardinoni F 2015. Indices of hydrological and sediment connectivity-state of the art and way forward, EGU General Assembly Conference Abstracts.
  • 26. Hu S, Cheng X, Zhou D, Zhang H (2017) GIS-based flood risk assessment in suburban areas: a case study of the Fangshan District, Beijing. Nat Hazards 87:1525–1543
  • 27. Huang C-L, Hsu N-S, Wei C-C, Luo W-J (2015) Optimal spatial design of capacity and quantity of rainwater harvesting systems for urban flood mitigation. Water 7:5173–5202
  • 28. Huang S-L, Yeh C-T, Budd WW, Chen L-L (2009) A Sensitivity Model (SM) approach to analyze urban development in Taiwan based on sustainability indicators. Environ Impact Assess Rev 29:116–125
  • 29. Ildoromi AR, Sepehri M, Malekinezhad H, Kiani-Harchegani M, Ghahramani A, Hosseini SZ, Artimani MM (2019) Application of multi-criteria decision making and GIS for check dam Layout in the Ilanlu Basin, Northwest of Hamadan Province. Iran Phys Chem Earth, Parts A/B/C 114:102803
  • 30. Jothibasu A, Anbazhagan S (2016) Flood susceptibility appraisal in Ponnaiyar River Basin, India using frequency ratio (FR) and Shannon’s Entropy (SE) models. Int J Adv Rem Sens GIS 5:1946–1962
  • 31. Jurlina T, Baugh C, Pappenberger F, Prudhomme C (2020) Flood hazard risk forecasting index (FHRFI) for urban areas: the hurricane harvey case study. Meteorol Appl 27:e1845
  • 32. Kalantari Z, Nickman A, Lyon SW, Olofsson B, Folkeson L (2014) A method for mapping flood hazard along roads. J Environ Manage 133:69–77
  • 33. Kawachi T, Maruyama T, Singh VP (2001) Rainfall entropy for delineation of water resources zones in Japan. J Hydrol 246:36–44
  • 34. Kazakis N, Kougias I, Patsialis T (2015) Assessment of flood hazard areas at a regional scale using an index-based approach and Analytical Hierarchy Process: application in Rhodope-Evros region, Greece. Sci Total Environ 538:555–563
  • 35. Kumar A, Saha SK, Matsagar VA (2019) Stochastic response analysis of elastic and inelastic systems with uncertain parameters under random impulse loading. J Sound Vib 461:114899
  • 36. Lai C, Shao Q, Chen X, Wang Z, Zhou X, Yang B, Zhang L (2016) Flood risk zoning using a rule mining based on ant colony algorithm. J Hydrol 542:268–280
  • 37. Lee G, Jun KS, Chung E-S (2014) Robust spatial flood vulnerability assessment for Han River using fuzzy TOPSIS with α-cut level set. Expert Syst Appl 41:644–654
  • 38. Lei X, Chen W, Avand M, Janizadeh S, Kariminejad N, Shahabi H, Costache R, Shahabi H, Shirzadi A, Mosavi A (2020) GIS-based machine learning algorithms for gully erosion susceptibility mapping in a semi-arid region of Iran. Remote Sens 12(15):2478
  • 39. Li L, Liu D-J (2014) Study on an air quality evaluation model for Beijing City under haze-fog pollution based on new ambient air quality standards. Int J Environ Res Public Health 11:8909–8923
  • 40. Li W, Lin K, Zhao T, Lan T, Chen X, Du H, Chen H (2019) Risk assessment and sensitivity analysis of flash floods in ungauged basins using coupled hydrologic and hydrodynamic models. J Hydrol 572:108–120
  • 41. Lotfi FH, Fallahnejad R (2010) Imprecise Shannon’s entropy and multi attribute decision making. Entropy 12:53–62
  • 42. Luan B, Yin R, Xu P, Wang X, Yang X, Zhang L, Tang X (2019) Evaluating green stormwater infrastructure strategies efficiencies in a rapidly urbanizing catchment using SWMM-based TOPSIS. J Clean Prod 223:680–691
  • 43. Lyu H-M, Sun W-J, Shen S-L, Arulrajah A (2018) Flood risk assessment in metro systems of mega-cities using a GIS-based modeling approach. Sci Total Environ 626:1012–1025
  • 44. Mahmoud SH, Gan TY (2018) Multi-criteria approach to develop flood susceptibility maps in arid regions of Middle East. J Clean Prod 196:216–229
  • 45. Malczewski J (1999) GIS and multicriteria decision analysis. John Wiley & Sons
  • 46. Meshram SG, Alvandi E, Meshram C, Kahya E, Al-Quraishi AMF (2020) Application of SAW and TOPSIS in prioritizing watersheds. Water Resour Manage. https://doi.org/10.1007/s11269-019-02470-x
  • 47. Mishra K, Sinha R (2020) Flood risk assessment in the Kosi megafan using multi-criteria decision analysis: a hydro-geomorphic approach. Geomorphology 350:106861
  • 48. Moradi AM, Fard AS, Nassabi F (2008) A historical study of Ekbatana Hill and a Glance at its rehabilitation. J Asian Archit Building Eng 7:147–154
  • 49. Plate E, 2009. HESS Opinions Classification of hydrological models for flood management. Hydrol Earth Syst Sci 13.
  • 50. Rodríguez R, Gauthier-Maradei P, Escalante H (2017) Fuzzy spatial decision tool to rank suitable sites for allocation of bioenergy plants based on crop residue. Biomass Bioenerg 100:17–30
  • 51. Roumyani A, Salehi Mishani H, Vosoughi Rod L, Ghaderi B, Amraie S (2017) Application of RS-GIS models in urban expansion optimization with emphasis on environmental protection (Case study: Hamedan City). J Geogr Reg Dev 14:51–66. https://doi.org/10.22067/geography.v14i2.42141
  • 52. Roy S, Sahu AS (2017) Potential interaction between transport and stream networks over the lowland rivers in Eastern India. J Environ Manage 197:316–330
  • 53. Sepehri M, Ildoromi AR, Malekinezhad H, Ghahramani A, Ekhtesasi MR, Cao C, Kiani-Harchegani M (2019a) Assessment of check dams’ role in flood hazard mapping in a semi-arid environment. Geomat Nat Haz Risk 10:2239–2256
  • 54. Sepehri M, Malekinezhad H, Hosseini SZ, Ildoromi AR (2019b) Assessment of flood hazard mapping in urban areas using entropy weighting method: a case study in Hamadan city. Iran Acta Geophysica 67:1435–1449
  • 55. Sepehri M, Malekinezhad H, Hosseini SZ, Ildoromi AR, 2019c. Suburban flood hazard mapping in Hamadan city, Iran, Proceedings of the Institution of Civil Engineers-Municipal Engineer. Thomas Telford Ltd, pp. 1–13.
  • 56. Sepehri M, Malekinezhad H, Ilderomi AR, Talebi A, Hosseini SZ (2018) Studying the effect of rain water harvesting from roof surfaces on runoff and household consumption reduction. Sustain Urban Areas 43:317–324
  • 57. Sepehri M, Malekinezhad H, Jahanbakhshi F, Ildoromi AR, Chezgi J, Ghorbanzadeh O, Naghipour E (2020) Integration of interval rough AHP and fuzzy logic for assessment of flood prone areas at the regional scale. Acta Geophys. https://doi.org/10.1007/s11600-019-00398-9
  • 58. Siahkamari S, Haghizadeh A, Zeinivand H, Tahmasebipour N, Rahmati O (2018) Spatial prediction of flood-susceptible areas using frequency ratio and maximum entropy models. Geocarto Int 33:927–941
  • 59. Singh V (1997) The use of entropy in hydrology and water resources. Hydrol Process 11:587–626
  • 60. Smith MB (2006) Comment on ‘analysis and modeling of flooding in urban drainage systems.’ J Hydrol 3:355–363
  • 61. Smithson M (2012) Ignorance and uncertainty: emerging paradigms. Springer
  • 62. Souissi D, Zouhri L, Hammami S, Msaddek MH, Zghibi A, Dlala M (2019) GIS-based MCDM–AHP modeling for flood susceptibility mapping of arid areas, southeastern Tunisia. Geocarto Int. https://doi.org/10.1080/10106049.2019.1566405
  • 63. Tam VT, Nga TTV (2018) Assessment of urbanization impact on groundwater resources in Hanoi. Vietnam J environ manag 227:107–116
  • 64. Tan Y, Jiao L, Shuai C, Shen L (2018) A system dynamics model for simulating urban sustainability performance: a China case study. J Clean Prod 199:1107–1115
  • 65. Tang Z, Zhang H, Yi S, Xiao Y (2018) Assessment of flood susceptible areas using spatially explicit, probabilistic multi-criteria decision analysis. J Hydrol 558:144–158
  • 66. Thapa R, Gupta S, Reddy D (2017) Application of geospatial modelling technique in delineation of fluoride contamination zones within Dwarka Basin, Birbhum, India. Geosci Front 8:1105–1114
  • 67. Toosi AS, Calbimonte GH, Nouri H, Alaghmand S (2019) River basin-scale flood hazard assessment using a modified multi-criteria decision analysis approach: a case study. J Hydrol 574:660–671
  • 68. Uwasu M, Yabar H (2011) Assessment of sustainable development based on the capital approach. Ecol Ind 11:348–352
  • 69. Wang H, Wang H, Wu Z, Zhou Y (2021) Using multi-factor analysis to predict urban flood depth based on Naive Bayes. Water 13:432
  • 70. Wang Y, Luan Q, Wang H, Liu J, Ma J (2019) Risk Assessment of rainstorm waterlogging in new district based on MIKE Urban. Sustainable Development of Water Resources and Hydraulic Engineering in China. Springer, NewYork, pp 29–40
  • 71. Wang Z, Lai C, Chen X, Yang B, Zhao S, Bai X (2015) Flood hazard risk assessment model based on random forest. J Hydrol 527:1130–1141
  • 72. Xu H, Ma C, Lian J, Xu K, Chaima E (2018) Urban flooding risk assessment based on an integrated k-means cluster algorithm and improved entropy weight method in the region of Haikou, China. J Hydrol 563:975–986
  • 73. Yariyan P, Janizadeh S, Phong TV, Nguyen HD, Costache R, Le HV, Pham BT, Pradhan B, Tiefenbacher JP (2020) Improvement of best first decision trees using bagging and dagging ensembles for flood probability mapping. Water Resour Manage 34(9):3037–3053
  • 74. Zhao G, Xu Z, Pang B, Tu T, Xu L, Du L (2019) An enhanced inundation method for urban flood hazard mapping at the large catchment scale. J Hydrol 571:873–882
  • 75. Zhou Q, Leng G, Su J, Ren Y (2019) Comparison of urbanization and climate change impacts on urban flood volumes: Importance of urban planning and drainage adaptation. Sci Total Environ 658:24–33
  • 76. Zhou Q, Mikkelsen PS, Halsnæs K, Arnbjerg-Nielsen K (2012) Framework for economic pluvial flood risk assessment considering climate change effects and adaptation benefits. J Hydrol 414:539–549
  • 77. Zope P, Eldho T, Jothiprakash V (2015) Impacts of urbanization on flooding of a coastal urban catchment: a case study of Mumbai City, India. Nat Hazards 75:887–908
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-e15e83f6-cba7-4d09-96f8-a18ab650e2a0
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