PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Assessment of food hazard mapping in urban areas using entropy weighting method: a case study in Hamadan city, Iran

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Flood is one of the major natural disasters which cause enormous casualties and damages particularly in urban areas. In urban areas, studies on food hazards have been accompanied by tensions for various reasons, including complexity of urban levels, diferent spatial modeling indices, lack of accurate hydrological data, and precise modeling of land surface simulations. This paper used a Constrained Delaunay Triangular Irregular Network to model fne urban surfaces (based on the detailed ground sampling data), and subsequently discusses fve indicators regarding the dangers of food, namely (1) elevation, (2) slope, (3) distance to discharge channels, (4) index of development and persistence of the drainage network (IDPR), and (5) infiltration rate. In the next step for food hazard mapping, the combination of geographical information systems and the entropy weight method as the multi-criteria decision analysis was used to combine the indicators. The proposed methodology was used for Hamadan city that is located in the central part of Hamadan Province in Iran where several foods occur annually. The food hazard mapping indicates that approximately 15.83% of the total study area is classifed as very highly hazardous, 31.72% as hazardous, 20.11% as moderate, 16.02% as minor, and 16.32% as the least hazardous. Finally, superimposition and receiver operating characteristic (ROC) curve methods were used to verify the accuracy of the obtained food hazard map. In terms of superimposition and ROC curve, the accuracy of the model was approximately 70% and 73%, respectively.
Słowa kluczowe
Czasopismo
Rocznik
Strony
1435--1449
Opis fizyczny
Bibliogr. 56 poz.
Twórcy
  • Department of Rangeland and Watershed Management, Faculty of Natural Resources, Yazd University, Yazd, Iran
  • Department of Rangeland and Watershed Management, Faculty of Natural Resources, Yazd University, Yazd, Iran
  • Department of Rangeland and Watershed Management, Faculty of Natural Resources, Yazd University, Yazd, Iran
  • Department of Watershed and Range Land Management, Faculty of Natural Resources, Malayer University, Hamadan, Iran
Bibliografia
  • 1. Bathrellos G, Karymbalis E, Skilodimou H, Gaki-Papanastassiou K, Baltas E (2016) Urban flood hazard assessment in the basin of Athens Metropolitan city. Greece Environ Earth Sci 75:319
  • 2. Bathrellos GD, Skilodimou HD, Chousianitis K, Youssef AM, Pradhan B (2017) Suitability estimation for urban development using multi-hazard assessment map. Sci Total Environ 575:119–134
  • 3. Covino T (2017) Hydrologic connectivity as a framework for understanding biogeochemical flux through watersheds and along fluvial networks. Geomorphology 277:133–144
  • 4. Cox LA (2009) Limitations of risk assessment using risk matrices. In: Cox LA (ed) Risk analysis of complex and uncertain systems. Springer, Berlin, pp 101–124
  • 5. Degiorgis M, Gnecco G, Gorni S, Roth G, Sanguineti M, Taramasso AC (2012) Classifiers for the detection of flood-prone areas using remote sensed elevation data. J Hydrol 470:302–315
  • 6. Dysarz T, Wicher-Dysarz J, Sojka M, Jaskuła J (2019) Analysis of extreme flow uncertainty impact on size of flood hazard zones for the Wronki gauge station in the Warta river. Acta Geophysica 67:661–676
  • 7. Elkhrachy I (2015) Flash flood hazard mapping using satellite images and GIS tools: a case study of Najran City, Kingdom of Saudi Arabia (KSA). Egypt J Remote Sens Space Sci 18:261–278
  • 8. Fernández D, Lutz M (2010) Urban flood hazard zoning in Tucumán Province, Argentina, using GIS and multicriteria decision analysis. Eng Geol 111:90–98
  • 9. Gay A, Cerdan O, Mardhel V, Desmet M (2016) Application of an index of sediment connectivity in a lowland area. J Soils Sedim 16:280–293
  • 10. Ghiglieri G, Carletti A, Pittalis D (2014) Runoff coefficient and average yearly natural aquifer recharge assessment by physiography-based indirect methods for the island of Sardinia (Italy) and its NW area (Nurra). J Hydrol 519:1779–1791
  • 11. Gigović L, Pamučar D, Bajić Z, Drobnjak S (2017) Application of GIS-interval rough AHP methodology for flood hazard mapping in urban areas. Water 9:360
  • 12. Guo E, Zhang J, Ren X, Zhang Q, Sun Z (2014) Integrated risk assessment of flood disaster based on improved set pair analysis and the variable fuzzy set theory in central Liaoning Province, China. Nat Hazards 74:947–965
  • 13. Hall M, Ellis J (1985) Water quality problems of urban areas. GeoJournal 11:265–275
  • 14. Heckmann T, et al (2015) Indices of hydrological and sediment connectivity-state of the art and way forward. In: EGU general assembly conference abstracts
  • 15. Hernandes TAD, Scarpare FV, Seabra JEA (2018) Assessment of the recent land use change dynamics related to sugarcane expansion and the associated effects on water resources availability. J Clean Prod 197:1328–1341
  • 16. 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
  • 17. Kawachi T, Maruyama T, Singh VP (2001) Rainfall entropy for delineation of water resources zones in Japan. J Hydrol 246:36–44
  • 18. 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
  • 19. Kowalzig J (2008) Climate, Poverty, and Justice: What the Poznan UN climate conference needs to deliver for a fair and effective global deal. Oxfam Policy Pract Clim Change Resil 4:117–148
  • 20. Lee S, Lee S, Lee M-J, Jung H-S (2018) Spatial assessment of urban flood susceptibility using data mining and geographic information system (GIS) tools. Sustainability 10:648
  • 21. Li Q (2013) Fuzzy approach to analysis of flood risk based on variable fuzzy sets and improved information diffusion methods. Nat Hazards Earth Syst Sci 13(2):239–249
  • 22. Li Z, Wu L, Zhu W, Hou M, Yang Y, Zheng J (2014) A new method for urban storm flood inundation simulation with fine CD-TIN surface. Water 6:1151
  • 23. Liu L, Zhou J, An X, Zhang Y, Yang L (2010) Using fuzzy theory and information entropy for water quality assessment in Three Gorges region, China. Expert Syst Appl 37:2517–2521
  • 24. Lohani AK, Goel N, Bhatia K (2014) Improving real time flood forecasting using fuzzy inference system. J Hydrol 509:25–41
  • 25. Lotfi FH, Fallahnejad R (2010) Imprecise Shannon’s entropy and multi attribute decision making. Entropy 12:53–62
  • 26. Lyu H-M, Shen S-L, Zhou A, Yang J (2019) Perspectives for flood risk assessment and management for mega-city metro system. Tunn Undergr Space Technol 84:31–44
  • 27. Machiwal D, Jha MK, Mal BC (2011) Assessment of groundwater potential in a semi-arid region of India using remote sensing, GIS and MCDM techniques. Water Resour Manag 25:1359–1386
  • 28. 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
  • 29. Malczewski J (1999) GIS and multicriteria decision analysis. Wiley, Hoboken
  • 30. Mayor ÁG, Bautista S, Small EE, Dixon M, Bellot J (2008) Measurement of the connectivity of runoff source areas as determined by vegetation pattern and topography: a tool for assessing potential water and soil losses in drylands. Water Resour Res. https://doi.org/10.1029/2007WR006367
  • 31. Mishra AK, Özger M, Singh VP (2009) An entropy-based investigation into the variability of precipitation. J Hydrol 370:139–154
  • 32. Patra S, Mishra P, Mahapatra SC (2018) Delineation of groundwater potential zone for sustainable development: A case study from Ganga Alluvial Plain covering Hooghly district of India using remote sensing, geographic information system and analytic hierarchy process. J Clean Prod 172:2485–2502
  • 33. Pradhan B (2009) Flood Susceptible mapping and risk area delineation using logistic regression, GIS and remote sensing. J Spatial Hydrol 9:1–18
  • 34. Radwan F, Alazba A, Mossad A (2019) Flood risk assessment and mapping using AHP in arid and semiarid regions. Acta Geophysica 67:215–229
  • 35. Rodríguez LR, Nouvel R, Duminil E, Eicker U (2017) Setting intelligent city tiling strategies for urban shading simulations. Sol Energy 157:880–894
  • 36. Samanta S, Koloa C, Kumar Pal D, Palsamanta B (2016) Flood risk analysis in lower part of Markham river based on multi-criteria decision approach (MCDA). Hydrology 3:29
  • 37. Schanze J (2006) Flood risk management–a basic framework. In: Schanze J (ed) Flood risk management: hazards, vulnerability and mitigation measures. Springer, Berlin, pp 1–20
  • 38. 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 Cities Soc 43:317–324. https://doi.org/10.1016/j.scs.2018.09.005
  • 39. Sepehri M, Malekinezhad H, Hosseini SZ, Ildoromi AR (2019) Suburban flood hazard mapping in Hamadan city, Iran. In: Proceedings of the institution of civil engineers-municipal engineer. Thomas Telford Ltd, pp 1–13
  • 40. Shadman Roodposhti M, Aryal J, Shahabi H, Safarrad T (2016) Fuzzy shannon entropy: a hybrid GIS-based landslide susceptibility mapping method. Entropy 18:343
  • 41. Singh V (1997) The use of entropy in hydrology and water resources. Hydrol Process 11:587–626
  • 42. Sivapalan M (2003) Prediction in ungauged basins: a grand challenge for theoretical hydrology. Hydrol Process 17:3163–3170
  • 43. Skilodimou HD, Bathrellos GD, Chousianitis K, Youssef AM, Pradhan B (2019) Multi-hazard assessment modeling via multi-criteria analysis and GIS: a case study. Environ Earth Sci 78:47
  • 44. Smithson M (1989) Cognitive science Ignorance and uncertainty: emerging paradigms. Springer, New York. https://doi.org/10.1007/978-1-4612-3628-3
  • 45. Stefanidis S, Stathis D (2013) Assessment of flood hazard based on natural and anthropogenic factors using analytic hierarchy process (AHP). Nat Hazards 68:569–585
  • 46. 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
  • 47. Tehrany MS, Pradhan B, Jebur MN (2013) Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS. J Hydrol 504:69–79
  • 48. 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
  • 49. Unal I (2017) Defining an optimal cut-point value in roc analysis: an alternative approach. Comput Math Methods Med 2017:3762651. https://doi.org/10.1155/2017/3762651
  • 50. USDA (1986) Urban hydrology for small watersheds. Tech Release 55:2–6
  • 51. Uwasu M, Yabar H (2011) Assessment of sustainable development based on the capital approach. Ecol Indic 11:348–352
  • 52. 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
  • 53. Wang M, Zhang DQ, Su J, Dong JW, Tan SK (2018) Assessing hydrological effects and performance of low impact development practices based on future scenarios modeling. J Clean Prod 179:12–23
  • 54. 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. https://doi.org/10.1016/j.jhydrol.2018.06.060
  • 55. Yang X-l, Ding J-h, Hou H (2013) Application of a triangular fuzzy AHP approach for flood risk evaluation and response measures analysis. Nat Hazards 68:657–674
  • 56. Zou Q, Zhou J, Zhou C, Song L, Guo J (2013) Comprehensive flood risk assessment based on set pair analysis-variable fuzzy sets model and fuzzy AHP. Stoch Environ Res Risk Assess 27:525–546
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-3733124a-e6e6-4c9b-bc58-3486d3a5c55a
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.