PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Powiadomienia systemowe
  • Sesja wygasła!
Tytuł artykułu

Flood susceptibility mapping using qualitative and statistical methods in a semi-arid basin: case of the Manouba–Sijoumi watershed, Northeastern Tunisia

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Floods are considered among the gravest natural disasters worldwide and have resulted in enormous human and material damage. The Manouba–Sijoumi basin (Northeast of Tunisia) is often flooded due to urban expansion, population growth and unplanned land use. This study aims to identify and to define the flood-prone areas of this basin for the 2003 and 2018 extreme events based on a Geographic Information System, a qualitative method (analytic network process-ANP) and a statistical model (frequency ratio-FR). The flood risk maps obtained by both models were validated using the receiver operating characteristic, the area under the curve (AUC) and inventory map. Areas of high and very high flood sensitivity are located mainly in urban settings, with an increase in risk between 2003 and 2018. The AUC values for both models were of the same significance (98%) for the year 2003 while those for the year 2018 were 94% and 98% for the ANP and FR models, respectively. This would imply that both models yielded reasonable results. However, the FR model showed an ability to reduce the uncertainty associated with expert judgements. The results indicate that the most influential factor on flooding in this area was land use/cover. Indeed, populations were largely settled in unsuitable sites for urbanization and in potentially flood-prone areas located mainly around the Sijoumi Sebkha, especially to the west and south of it. The findings of the study are of great value for policy makers and state authorities to achieve greater awareness and adopt strategies for environmental preparedness and management.
Słowa kluczowe
Czasopismo
Rocznik
Strony
2307--2323
Opis fizyczny
Bibliogr. 90 poz., rys., tab.
Twórcy
  • GEOMODELE Laboratory, Faculty of Sciences, University of Sfax, BP 1171, 3000 Sfax, Tunisia
autor
  • GEOMODELE Laboratory, Faculty of Sciences, University of Sfax, BP 1171, 3000 Sfax, Tunisia
  • Laboratory of Management, Maintenance and Rehabilitation of Facilities and Urban Infrastructure, University Kasdi Merbah Ouargla, Ouargla, Algeria
  • Institute of Applied Technology, Thu Dau Mot University, Thu Dau Mot, Binh Duong Province, Vietnam
autor
  • GEOMODELE Laboratory, Faculty of Sciences, University of Sfax, BP 1171, 3000 Sfax, Tunisia
Bibliografia
  • 1. Abdelkarim A, Al-Alola SS, Alogayell HM, Mohamed SA, Alkadi II, Ismail YI (2020) Integration of GIS-based multicriteria decision analysis and analytic hierarchy process to assess flood hazard on the Al-Shamal train pathway in Al-Qurayyat region, Kingdom of Saudi Arabia. Water 12:1702. https://doi.org/10.3390/w12061702
  • 2. Ahmadlou M, Karimi M, Alizadeh S, Shirzadi A, Parvinnejad D, Shahabi H, Panahi M (2018) Flood susceptibility assessment using integration of adaptive network-based fuzzy inference system (ANFIS) and biogeography-based optimization (BBO) and BAT algorithms (BA). Geocarto Int. https://doi.org/10.1080/10106049.2018.1474276
  • 3. Akay H (2021) Flood hazards susceptibility mapping using statistical fuzzy logic and MCDM methods. Soft Comput 25(14):9325–9346. https://doi.org/10.1007/s00500-021-05903-1
  • 4. Akay H, Kocyigit MB (2020) Flash flood potential prioritization of sub-basins in an ungauged basin in Turkey using traditional multi-criteria decision-making methods. Soft Comput 24(18):14251–14263. https://doi.org/10.1007/s00500-020-04792-0
  • 5. Ake GE, Kouame KJ, Koffi AB, Jourda JP (2018) Cartographie des zonespotentielles de recharge de la nappe de Bonoua (sud-est de la Côte d’Ivoire). Rev Dessciences L’eau/J Water Sci 31(2):129–144. https://doi.org/10.7202/1051696ar
  • 6. Ali SA, Parvin F, Pham QB, Vojtek M, Vojteková J, Costache R, LinhNguyenAhmadGhorbani NTTHQAMA (2020) GIS-based comparative assessment of flood susceptibility mapping using hybrid multi-criteria decision-making approach, naïve Bayes tree, bivariate statistics and logistic regression: a case of Topľa basin, Slovakia. Ecol Indic 117:106620. https://doi.org/10.1016/j.ecolind.2020.106620
  • 7. Alizadeh M, Ngah I, Hashim M, Pradhan B, BeiranvandPour A (2018) A hybrid analytic network process and artificial neural network (ANP–ANN) model for urban earthquake vulnerability assessment. Remote Sens 10:975. https://doi.org/10.3390/rs10060975
  • 8. Argyriou AV, Teeuw RM, Rust D, Sarris A (2016) GIS multi-criteria decision analysis for assessment and mapping of neotectonic landscape deformation: a case study from Crete. Geomorphology. https://doi.org/10.1016/j.geomorph.2015.10.018
  • 9. Arora A, Pandey M, Siddiqui MA, Hong H, Mishra VN (2021) Spatial flood susceptibility prediction in middle Ganga Plain: comparison of frequency ratio and Shannon’s entropy models. Geocarto Int 36(18):2085–2116. https://doi.org/10.1080/10106049.2019.1687594
  • 10. Avand M, Moradi HR, RamazanzadehLasboyee M (2021) Spatial prediction of future flood risk: an approach to the effects of climate change. Geosciences 11:25. https://doi.org/10.3390/geosciences11010025
  • 11. Batista CM (2018) Coastal flood hazard mapping. In: Finkl CW, Makowski C (eds) Encyclopedia of coastal science. Encycloppedia of earth sciences series, vol 1. Springer, Cham, pp 471–479. https://doi.org/10.1007/978-3-319-48657-4_356-1
  • 12. Bedient PB, Huber WC (1992) Hydrology and floodplain analysis. Addison-Wesley, Reading
  • 13. Bentivoglio R, Isufi E, Jonkman SN, Taormina R (2022) Deep learning methods for flood mapping: a review of existing applications and future research directions. Hydrol Earth Syst Sci 26(16):4345–4378. https://doi.org/10.5194/hess-26-4345-2022
  • 14. Bishaw, K (2012) Application of GIS and remote sensing techniques for flood hazard and risk assessment: the case of Dugeda bora Woreda of Oromiya regional state, Ethiopia. In: Paper presented at the Berlin conference on the human dimensions of global environmental change
  • 15. Bouamrane A, Bouziane MT, Boutebba K (2014) Decision support system for the management and maintenance of sewer networks. Larhyss J P-ISSN 1112-3680/e-issn 2521-9782 20
  • 16. Bouamrane A, Derdous O, Dahri N, Tachi SE, Boutebba K, Bouziane MT (2020) A comparison of the analytical hierarchy process and the fuzzy logic approach for flood susceptibility mapping in a semi-arid ungauged basin (Biskra basin: Algeria). Int J River Basin Manag. https://doi.org/10.1080/15715124.2020.1830786
  • 17. Bouamrane A, Bouamrane A, Abida H (2021) Water erosion hazard distribution under a semi-arid climate condition: case of Mellah watershed, North-Eastern Algeria. Geoderma 403:115381
  • 18. Boughariou E, Allouche N, Ben Brahim F, Nasri G, Bouri S (2021) Delineation of groundwater potentials of Sfax region, Tunisia, using fuzzy analytical hierarchy process, frequency ratio, and weights of evidence models. Environ Dev Sustain. https://doi.org/10.1007/s10668-021-01270-x
  • 19. Bouguerra H, Tachi SE, Derdous O, Bouanani A, Khanchoul K (2019) Suspended sediment discharge modelling during flood events using two different artificial neural network algorithms. Acta Geophys. https://doi.org/10.1007/s11600-019-00373-4
  • 20. Chaouach M, Gammar AM (2003) Eau et environnement. Dynamique de la végétation et de l’espace sur les rives de la sebkha d’Essijoumi. ENS Éditions, Paris, pp 177–188. https://doi.org/10.4000/books.enseditions.885
  • 21. Chen FW, Liu CW (2012) Estimation of the spatial rainfall distribution using inverse distance weighting (IDW) in the middle of Taiwan. Paddy Water Environ 10(3):209–222. https://doi.org/10.1007/s10333-012-0319-1
  • 22. Chen YR, Yeh CH, Yu B (2011) Integrated application of the analytic hierarchy process and the geographic information system for flood risk assessment and flood plain management in Taiwan. Nat Hazards 59(3):1261–1276
  • 23. Chouari W (2013) Problèmes d’environnement liés à l’urbanisation contemporaine dans le système endoréique d’Essijoumiss (Tunisie nord-orientale). Physio-Géo 7:111–138
  • 24. Chouari W (2019) La perception du risque d’inondations dans les zones inondables du bassin versant de manouba-essijoumi (Tunisie nord-orientale): sensibilité au risque et aux actions de prévention. BSGLg 73(2019):117–129
  • 25. Chowdhuri I, Chandra Pal S, Chakrabortty R (2019) Flood susceptibility mapping by ensemble evidential belief function and binomial logistic regression model on river basin of eastern India. Adv Space Res. https://doi.org/10.1016/j.asr.2019.12.003
  • 26. Costache R, Țîncu R, Elkhrachy I, Pham QB, Popa MC, DiaconuAvandCostacheArabameriBui DCMLADT (2020) New neural fuzzy-based machine learning ensemble for enhancing the prediction accuracy of flood susceptibility mapping. Hydrol Sci J 65(16):2816–2837. https://doi.org/10.1080/02626667.2020.1842412
  • 27. Dahri N (2018) Caractérisation quantitative et qualitative des flux d’eaux pluviales dans la ville de Gabès: apports des outils hydrologiques cartographiques et géochimiques. Thèse de doctorat. Faculté des Sciences de Sfax
  • 28. Dahri N, Abida H (2017) Monte Carlo simulation-aided analytical hierarchy process (AHP) for flood susceptibility mapping in Gabes basin (southeastern Tunisia). Environ Earth Sci 76:302. https://doi.org/10.1007/s12665-017-6619-4
  • 29. Dahri N, Yousfi R, Bouamrane A, Abida H, Pham QB, Derdous O (2022) Comparison of analytic network process and artificial neural network models for flash flood susceptibility assessment. J Afr Earth Sci 193:104576. https://doi.org/10.1016/j.jafrearsci.2022.104576
  • 30. Danumah JH, NiiOdai S, MahamanSaley B, Szarzynski J, Thiel M, Kwaku A, KoffiKouame F, You Akpa L (2016) Flood risk assessment and mapping in Abidjan district using multi-criteria analysis (AHP) model and geoinformation techniques, (coted’ivoire). Geoenviron Disasters 3:10. https://doi.org/10.1186/s40677-016-0044-y
  • 31. Das B, Pal SC (2019) Combination of GIS and fuzzy-AHP for delineating groundwater recharge potential zones in the critical Goghat-II block of West Bengal, India. HydroResearch 2:21–30. https://doi.org/10.1016/j.hydres.2019.10.001
  • 32. Demirel T, Tüzün S (2011) Multi criteria evaluation of the methods for preventing soil erosion using fuzzy ANP: the case of Turkey. In: Proceedings of the world congress on engineering, London, U.K
  • 33. Eastman JR (2003) IDRISI Kilimanjaro: guide to GIS and image processing. Clark Labs, Clark University, Worcester, USA, pp 328
  • 34. Falah F, Rahmati O, Rostami M, Ahmadisharaf E, Daliakopoulos LN, Pourghasemi HR (2019) Artificial neural networks for flood susceptibility mapping in data-scarce urban areas. In: Spatial modeling in GIS and R for earth and environmental sciences. Elsevier, Amsterdam, pp 323–336
  • 35. Fehri N, Zahar Y (2016) Étude de l’impact de l’extension et de la densification du tissu urbain sur les coefficients de ruissellement dans le bassin versant des oueds El-Ghrich et El-Greb (Tunis) par l’application de la méthode SCS aux évènements de septembre 2003. Physio-Géo 10:61–79. https://doi.org/10.4000/physio-geo.4769
  • 36. Ferchichi H, Farhat B, Ben-Hamouda MF, Ben-Mammou A (2017) Understanding groundwater chemistry in Mediterranean semi-arid system using multivariate statistics techniques and GIS methods: case of Manouba aquifer (Northeastern Tunisia). Arab J Geosci 10:530. https://doi.org/10.1007/s12517-017-3314-4
  • 37. Ghorbanzadeh O, Feizizadeh B, BlaschkeT (2017) Multi-criteria risk evaluation by integrating an analytical network process approach into GIS based sensitivity and uncertainty analyses. Geomat Nat Hazards Risk. https://doi.org/10.1080/19475705.2017.1413012
  • 38. Guellouh S, Dridi H, Kalla M, Filali A (2020) A Multi-criteria analytical hierarchy process (AHP) to flood vulnerability assessment in Batna watershed (Algeria). Analele Universităţii Din Oradea SeriaGeografie 30(1):41–47. https://doi.org/10.30892/auog.301105-810
  • 39. Hammami S, Zouhri L, Souissi D, Souei A, Zghibi A, Marzougui A, Dlala M (2019) Application of the GIS based multi-criteria decision analysis and analytical hierarchy process (AHP) in the flood susceptibility mapping (Tunisia). Arab J Geosci 12(21):1–16. https://doi.org/10.1007/s12517-019-4754-9
  • 40. Hong H, Tsangaratos P, Ilia I, Liu J, Zhu A-X, Chen W (2018) Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang county, China. Sci Total Environ 625:575–588. https://doi.org/10.1016/j.scitotenv.2017.12.256
  • 41. Horton R (1932) Drainage basin characteristics. Trans Am Geophys Union 13:350–361. https://doi.org/10.1029/TR013i001p00350
  • 42. Hossain MK, Meng Q (2020) A fine-scale spatial analytics of the assessment and mapping of buildings andpopulation at different risk levels of urban flood. Land Use Policy 99:104829. https://doi.org/10.1016/j.landusepol.2020.104829
  • 43. Hosseini FS, Sigaroodi SK, Salajegheh A, Moghaddamnia A, Choubin B (2021) Towards a flood vulnerability assessment of watershed using integration of decision-making trial and evaluation laboratory, analytical network process, and fuzzy theories. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-021-14534-w
  • 44. Kadoić N (2018) Characteristics of the analytic network process, a multi-criteria decision-making method. Croat Oper Res Rev 9:235–244
  • 45. Kanani-Sadat Y, Arabsheibani R, Karimipour F, Nasseri M (2019) A new approach to flood susceptibility assessment in data-scarce and ungauged regions based on GIS-based hybrid multi criteria decision-making method. J Hydrol 572:17–31. https://doi.org/10.1016/j.jhydrol.2019.02.034
  • 46. Khosravi K, Nohani E, Maroufinia E, Pourghasemi HR (2016a) A GIS-based flood susceptibility assessment and its mapping in Iran: a comparison between frequency ratio and weights-of-evidence bivariate statistical models with multi-criteria decision-making technique. Nat Hazards. https://doi.org/10.1007/s11069-016-2357-2
  • 47. Khosravi K, Pourghasemi HR, Chapi K, Bahri M (2016b) 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. Environ Monit Assess 188(12):1–21. https://doi.org/10.1007/s10661-016-5665-9
  • 48. Lee MJ, Kang JE, Jeon S (2012) Application of frequency ratio model and validation for predictive floodedn area susceptibility mapping using GIS. In: Proceedings of the geoscience and remote sensing symposium (IGARSS), Munich, Germany, pp 895–898
  • 49. Lee S, Lee S, Lee MJ, SupJung H (2018) Spatial assessment of urban flood susceptibility using data mining and geographic information system (GIS) tools. Sustainability 10:648. https://doi.org/10.3390/su10030648
  • 50. Li J, Heap AD (2008) A review of spatial interpolation methods for environmental scientists
  • 51. Malekinezhad H, Sepehri M, Pham QB, Hosseini SZ, Meshram SG, Vojtek M, Vojteková J (2021) Application of entropy weighting method for urban flood hazard mapping. Acta Geophys 69:841–854. https://doi.org/10.1007/s11600-021-00586-6
  • 52. Malik S, Pal SC (2021) Potential flood frequency analysis and susceptibility mapping using CMIP5 of MIROC5 and HEC-RAS model: a case study of lower Dwarkeswar river, East India. SN Appl Sci 3(1):1–22
  • 53. Mansour R, El Ghali A (2019) Cartographie numérique du risque d’inondation dans le Nord-Est de la Tunisie par la méthode du rapport de fréquence et l’indice statistique. Rev Int De Géomat 29(3–4):339–360
  • 54. Mersha T, Meten M (2020) GIS-based landslide susceptibility mapping and assessment using bivariate statistical methods in Simada area, northwestern Ethiopia. Geoenviron Disasters 7(1):1–22. https://doi.org/10.1186/s40677-020-00155-x
  • 55. Msabi MM, Makonyo M (2021) Flood susceptibility mapping using GIS and multi-criteria decision analysis: a case of Dodoma region, central Tanzania. Remote Sens Appl Soc Environ 21:100445
  • 56. Nachappa TG, Piralilou ST, Gholamnia K, Ghorbanzadeh O, Rahmati O, Blaschke T (2020) Flood susceptibility mapping with machine learning, multi-criteria decision analysis and ensemble using Dempster Shafer theory. J Hydrol. https://doi.org/10.1016/j.jhydrol.2020.125275
  • 57. Nazmfar H (2019) An integrated approach of the analytic network process and fuzzy model mapping of evaluation of urban vulnerability against earthquake. Geomat Nat Hazards Risk. https://doi.org/10.1080/19475705.2019.1588791
  • 58. Neaupane KM, Piantanakulchai M (2006) Analytic network process model for landslide hazard zonation. Eng Geol 85(3–4):281–294
  • 59. Nekhay O, Arriaza M, Boerboom L (2009) Evaluation of soil erosion risk using analytic network process and GIS: a case study from Spanish mountain olive plantations. J Environ Manag 90(10):3091–3104
  • 60. Nguyen HD, Fox D, Dang DK, Pham LT, Du VietNguyenDangTranVuNguyenBuiPetrisor QVTHTTNVTPLQ-HQ-TA-I (2021) Predicting future urban flood risk using land change and hydraulic modeling in a river watershed in the central province of Vietnam. Remote Sens 13(2):262. https://doi.org/10.3390/rs13020262
  • 61. Ouma YO, Tateishi R (2014) Urban flood vulnerability and risk mapping using integrated multi-parametric AHP and GIS: methodological overview and case study assessment. Water 6(6):1515–1545. https://doi.org/10.3390/w6061515
  • 62. Ou Yang YP, Shieh HM, Leu JD, Tzeng GH (2008) A novel hybrid mcdm model combined with dematel and anp with applications. Int J Oper Res 5:160–168
  • 63. Pal SC, Chowdhuri I (2019) GIS-based spatial prediction of landslide susceptibility using frequency ratio model of Lachung river basin, North Sikkim, India. SN Appl Sci 1(5):1–25. https://doi.org/10.1007/s42452-019-0422-7
  • 64. Pourghasemi HR, Pradhan B, Gokceoglu C (2012) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Nat Hazards 63(2):965–996. https://doi.org/10.1007/s11069-012-0217-2
  • 65. Pradhan B, Mansor S, Pirasteh S, Buchroithner MF (2011) Landslide hazard and risk analyses at a landslide prone catchment area using statistical based geospatial model. Int J Remote Sens 32:4075–4087
  • 66. Rad TG, Sadeghi-Niaraki A, Abbasi A, Choi SM (2018) A methodological framework for assessment of ubiquitous cities using ANP and DEMATEL methods. Sustain Cities Soc 37:608–618. https://doi.org/10.1016/j.scs.2017.11.024
  • 67. Rahmati O, Pourghasemi HR, Zeinivand H (2016) Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan province. Iran Geocarto Int 31(1):42–70. https://doi.org/10.1080/10106049.2015.1041559
  • 68. Rahmati O, Zeinivand H, Besharat M (2015) Flood hazard zoning in Yasooj region, Iran, using GIS and multi-criteria decision analysis. Geomat Nat Hazards Risk 7(3):1000–1017. https://doi.org/10.1080/19475705.2015.1045043
  • 69. Rogger M, Agnoletti M, Alaoui A, Bathurst JC, Bodner G, BorgaChaplotGallartGlatzelHallHaldenBlöschl MVFGJJG (2017) Land use change impacts on floods at the catchment scale: challenges and opportunities for future research. Water Resour Res 53(7):5209–5219. https://doi.org/10.1002/2017WR020723
  • 70. Saaty TL (1980) The analytic hierarchy processes. McGraw-Hill International, New York
  • 71. Saaty TL (1984) Inconsistency and rank preservation. J Math Psychol 28(2):2055214
  • 72. Saaty TL (1996) Decision making with dependence and feedback: the analytic network process, vol 4922, no 2. RWS publications, Pittsburgh
  • 73. Saaty TL (1999) Basic theory of the analytic hierarchy process: how to make a decision. Rev De La Real Acad De Cienc Exact Fis y Nat 93(4):395–423
  • 74. Saaty TL (2001) Decision making with the analytic network process (ANP) and its super decisions software: the national missile defense (NMD) example. ISAHP 2001 proceedings, pp 2–4
  • 75. Saaty TL, Vargas LG (1991) Prediction, projection and forecasting. Kluwer Academic, Boston, p 254
  • 76. Saha AK, Agrawal S (2020) Mapping and assessment of flood risk in Prayagraj district, India: a GIS and remote sensing study. Nanotechnol Environ Eng 5(2):1–18. https://doi.org/10.1007/s41204-020-00073-1
  • 77. Saidi S, Ghattassi A, Anselme B, Bouri S (2018) GIS Based multi-criteria analysis for flood risk assessment: case of ManoubaEssijoumi Basin, NE Tunisia. Conf Arab J Geosci. https://doi.org/10.1007/978-3-030-01440-7_64
  • 78. Samanta S, Pal DK, Palsamanta B (2018) Flood susceptibility analysis through remote sensing, GIS and frequency ratio model. Appl Water Sci 8:66. https://doi.org/10.1007/s13201-018-0710-1
  • 79. 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 68(2):477–493. https://doi.org/10.1007/s11600-019-00398-9
  • 80. Stieglitz M, Rind D, Famiglietti J, Rosenzweig C (1997) An efficient approach to modelling the topographic control of surface hydrology for regional and global climate modelling. J Clim 10:118–137
  • 81. Sun H, Xu G, Tian P (2007) Design alternatives evaluation of emergency bridge by applying analytic network process (ANP). Syst Eng Theory Pract 27(3):63–70. https://doi.org/10.1016/S1874-8651(08)60025-3
  • 82. 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 model in GIS. J Hydrol 504:69–79. https://doi.org/10.1016/j.jhydrol.2013.09.034
  • 83. Tehrany MS, Pradhan B, Jebur MN (2015) Flood susceptibility analysis and its verification using a novel ensemble support vector machine and frequency ratio method. Stoch Environ Res Risk Assess. https://doi.org/10.1007/s00477-015-1021-9
  • 84. Tehrany MS, Kumar L, Neamah Jebur M, Shabani F (2019) Evaluating the application of the statistical index method in flood susceptibility mapping and its comparison with frequency ratio and logistic regression methods. Geomat Nat Hazards Risk 10(1):79–101. https://doi.org/10.1080/19475705.2018.1506509
  • 85. Ullah K, Zhang J (2020) GIS-based flood hazard mapping using relative frequency ratio method: a case study of Panjkora River basin, eastern Hindu Kush, Pakistan. PLoS ONE 15(3):e0229153. https://doi.org/10.1371/journal.pone.0229153
  • 86. Vojtek M, Vojteková J, Pham QB (2021a) GIS-based spatial and multi-criteria assessment of riverine flood potential: A case study of the Nitra river basin, Slovakia. ISPRS Int J Geo-Inf 10(9):578. https://doi.org/10.3390/ijgi10090578
  • 87. Vojtek M, Vojteková J, Pham QB, Lee S, Arshad A, Costache R, Sahoo S, Linh NTT, Anh DT (2021b) Comparison of multi-criteria-analytical hierarchy process and machine learning-boosted tree models for regional flood susceptibility mapping: a case study from Slovakia. Geomat Nat Hazards Risk 12(1):1153–1180. https://doi.org/10.1080/19475705.2021.1912835
  • 88. Wubalem A, Tesfaw G, Dawit Z, Getahun B, Mekuria T, Jothimani M (2020) Comparison of statistical and analytical hierarchy process methods on flood susceptibility mapping: in a case study of Tana sub-basin in northwestern Ethiopia. Nat Hazards Earth Syst Sci Discuss 1–43
  • 89. Yariyan P, Avand M, Abbaspour RA, TorabiHaghighi A, Costache R, GhorbanzadehJanizadehBlaschke OST (2020) Flood susceptibility mapping using an improved analytic network process with statistical models. Geomat Nat Hazards Risk 11(1):2282–2314. https://doi.org/10.1080/19475705.2020.1836036
  • 90. Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-37088e1c-e0a9-4997-89b7-7caa8452f72e
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.