Abstract
Flooding is one of the most destructive natural catastrophes that can strike anywhere in the world. With the recent, but frequent catastrophic flood events that occurred in the narrow stretch of land in southern India, sandwiched between the Western Ghats and the Arabian Sea, this study was initiated. The goal of this research is to identify flood-vulnerable zones in this area by making the local self governing bodies as the mapping unit. This study also assessed the predictive accuracy of analytical hierarchy process (AHP) and fuzzy-analytical hierarchy process (F-AHP) models. A total of 20 indicators (nine physical-environmental variables and 11 socio-economic variables) have been considered for the vulnerability modelling. Flood-vulnerability maps, created using remotely sensed satellite data and geographic information systems, was divided into five zones. AHP and F-AHP flood vulnerability models identified 12.29% and 11.81% of the area as very high-vulnerable zones, respectively. The receiver operating characteristic (ROC) curve is used to validate these flood vulnerability maps. The flood vulnerable maps, created using the AHP and F-AHP methods, were found to be outstanding based on the area under the ROC curve (AUC) values. This demonstrates the effectiveness of these two models. The results of AUC for the AHP and F-AHP models were 0.946 and 0.943, respectively, articulating that the AHP model is more efficient than its chosen counterpart in demarcating the flood vulnerable zones. Decision-makers and land-use planners will find the generated vulnerable zone maps useful, particularly in implementing flood mitigation plans.
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(Source: Elevation data of ASTER; Lithology data of Geological Survey of India)
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The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
References
Abebe Y, Kabir G, Tesfamariam S (2018) Assessing urban areas vulnerability to pluvial flooding using GIS applications and Bayesian belief network model. J Clean Prod 174:1629–1641. https://doi.org/10.1016/j.jclepro.2017.11.066
Abu Reza M, Islam T, Talukdar S, Mahato S, Kundu S, Eibek KU, Pham QB, Kuriqi A, Linh NTT (2021) Flood susceptibility modelling using advanced ensemble machine learning models. Geosci Front 12(3):101075. https://doi.org/10.1016/j.gsf.2020.09.006
Afolayan AH, Ojokoh BA, Adetunmbi AO (2020) Performance analysis of fuzzy analytic hierarchy process multi-criteria decision support models for contractor selection. Sci Afr. https://doi.org/10.1016/j.sciaf.2020.e00471
Ahmed B (2015) Landslide susceptibility mapping using multi-criteria evaluation techniques in Chittagong metropolitan area, Bangladesh. Landslides 12:1077–1095. https://doi.org/10.1007/s10346-014-0521-x
Ajin RS, Krishnamurthy RR, Jayaprakash M, Vinod PG (2013) Flood hazard assessment of Vamanapuram river basin, Kerala, India: an approach using remote Sensing & GIS techniques. Adv Appl Sci Res 4(3):263–274
Ajin RS, Loghin AM, Vinod PG, Jacob MK (2019) Flood hazard zone mapping in the tropical Achankovil river basin in Kerala: a study using remote sensing data and geographic information system. J Wetlands Biodiv 9:45–58
Akshaya M, Danumah JH, Saha S, Ajin RS, Kuriakose SL (2021) Landslide susceptibility zonation of the Western Ghats region in Thiruvananthapuram district (Kerala) using geospatial tools: a comparison of the AHP and Fuzzy-AHP methods. Saf Extreme Environ 3:181–202. https://doi.org/10.1007/s42797-021-00042-0
Ali SA, Khatun R, Ahmad A, Ahmad SN (2019) Application of GIS-based analytic hierarchy process and frequency ratio model to flood vulnerable mapping and risk area estimation at Sundarban region, India. Model Earth Syst Environ 5:1083–1102. https://doi.org/10.1007/s40808-019-00593-z
Amrutha K, Danumah JH, Nikhil S, Saha S, Rajaneesh A, Mammen PC, Ajin RS, Kuriakose SL (2022) Demarcation of forest fire risk zones in silent valley national park and the effectiveness of forest management regime. J Geovisual Spat Anal. https://doi.org/10.1007/s41651-022-00103-3
Ayhan MB (2013) A fuzzy AHP approach for supplier selection problem: a case study in a gear motor company. Int J Manag Value Supply Chains 4(3):11–23. https://doi.org/10.5121/ijmvsc.2013.4302
Buckley JJ (1985) Fuzzy hierarchical analysis. Fuzzy Sets Syst 17(1):233–247
Carnero MC (2020) Fuzzy multicriteria models for decision making in gamification. Mathematics. https://doi.org/10.3390/math8050682
Chadsuthi S, Chalvet-Monfray K, Wiratsudakul A, Modchang C (2021) The effects of flooding and weather conditions on leptospirosis transmission in Thailand. Sci Rep. https://doi.org/10.1038/s41598-020-79546-x
Chanal D, Steiner NY, Petrone R, Chamagne D, Péra MC (2021) Online diagnosis of PEM fuel cell by fuzzy C-means clustering. Ref Module Earth Syst Environ Sci. https://doi.org/10.1016/B978-0-12-819723-3.00099-8
Ching YC, Lee YH, Toriman ME, Abdullah M, Yatim BB (2015) Effect of the big flood events on the water quality of the Muar River, Malaysia. Sustain Water Resour Manag 1:97–110. https://doi.org/10.1007/s40899-015-0009-4
Chou SW, Chang YC (2008) The implementation factors that influence the ERP (enterprise resource planning) benefits. Decis Support Syst 46(1):149–157
Chukwuma EC, Okonkwo CC, Ojediran JO, Anizoba DC, Ubah JI, Nwachukwu CP (2021) A GIS based flood vulnerability modelling of Anambra State using an integrated IVFRN-DEMATEL-ANP model. Heliyon 7(9):e08048. https://doi.org/10.1016/j.heliyon.2021.e08048
Crabtree A (2013) Questioning psychosocial resilience after flooding and the consequences for disaster risk reduction. Soc Indic Res 113:711–728. https://doi.org/10.1007/s11205-013-0297-8
Dandapat K, Panda GK (2017) Flood vulnerability analysis and risk assessment using analytical hierarchy process. Model Earth Syst Environ 3:1627–1646. https://doi.org/10.1007/s40808-017-0388-7
Danumah JH, Odai SN, Saley BM, Szarzynski J, Thiel M, Kwaku A, Kouame FK, Akpa LY (2016) Flood risk assessment and mapping in Abidjan district using multi-criteria analysis (AHP) model and geoinformation techniques, (cote d’ivoire). Geoenviron Dis. https://doi.org/10.1186/s40677-016-0044-y
Deepak S, Rajan G, Jairaj PG (2020) Geospatial approach for assessment of vulnerability to flood in local self-governments. Geoenviron Dis. https://doi.org/10.1186/s40677-020-00172-w
Department of Mining and Geology (2016) District survey report of minor minerals (except river sand) – Kottayam district. Government of Kerala
Desalegn H, Mulu A (2020) Flood vulnerability assessment using GIS at Fetam watershed, upper Abbay basin. Ethiopia Heliyon. https://doi.org/10.1016/j.heliyon.2020.e05865
Donnini M, Napolitano E, Salvati P, Ardizzone F, Bucci F, Fiorucci F, Santangelo M, Cardinali M, Guzzetti F (2017) Impact of event landslides on road networks: a statistical analysis of two Italian case studies. Landslides 14:1521–1535. https://doi.org/10.1007/s10346-017-0829-4
Duan Y, Xiong J, Cheng W, Wang N, Li Y, He Y, Liu J, He W, Yang G (2021) Flood vulnerability assessment using the triangular fuzzy number-based analytic hierarchy process and support vector machine model for the belt and road region. Nat Hazards. https://doi.org/10.1007/s11069-021-04946-9
Eskandari S, Miesel JR (2017) Comparison of the fuzzy AHP method, the spatial correlation method, and the Dong model to predict the fire high-risk areas in Hyrcanian forests of Iran. Geomat Nat Haz Risk 8(2):933–949. https://doi.org/10.1080/19475705.2017.1289249
Feloni E, Mousadis I, Baltas E (2020) Flood vulnerability assessment using a GIS‐based multi‐criteria approach: the case of Attica region. J Flood Risk Manag. https://doi.org/10.1111/jfr3.12563
Feng B, Zhang Y, Bourke R (2021) Urbanization impacts on flood risks based on urban growth data and coupled flood models. Nat Hazards 106:613–627. https://doi.org/10.1007/s11069-020-04480-0
Fernandez P, Mourato S, Moreira M, Pereira L (2016) A new approach for computing a flood vulnerability index using cluster analysis. Phys Chem Earth Parts a/b/c 94:47–55. https://doi.org/10.1016/j.pce.2016.04.003
Franzke CLE, Torelló i Sentelles H (2020) Risk of extreme high fatalities due to weather and climate hazards and its connection to large-scale climate variability. Clim Change 162:507–525. https://doi.org/10.1007/s10584-020-02825-z
Fucile-Sanchez E, Davlasheridze M (2020) Adjustments of socially vulnerable populations in Galveston County Texas USA following Hurricane Ike. Sustainability. https://doi.org/10.3390/su12177097
Gessesse AA, Melesse AM (2019) Chapter 8 - Temporal relationships between time series CHIRPS-rainfall estimation and eMODIS-NDVI satellite images in Amhara Region, Ethiopia. In: Melesse AM, Abtew W, Senay G (Eds) Extreme hydrology and climate variability, Elsevier, pp 81–92. https://doi.org/10.1016/B978-0-12-815998-9.00008-7
Global Natural Disaster Assessment Report (2020) Academy of Disaster Reduction and Emergency Management, Ministry of Emergency Management - Ministry of Education, National Disaster Reduction Center of China, Ministry of Emergency Management, International Federation of Red Cross and Red Crescent Societies
Government of Kerala (2018a) Memorandum (Revised): Monsoon calamity losses 29th May to 31st July 2018a. Available at https://sdma.kerala.gov.in/disaster-memoranda/
Government of Kerala (2018b) Kerala floods - 2018b: 1st August to 30th August 2018b. Available at https://sdma.kerala.gov.in/disaster-memoranda/
Government of Kerala (2019a) Memorandum: Kerala floods – 2019a (1st August to 31st August 2019a). Available at https://sdma.kerala.gov.in/disaster-memoranda/
Government of Kerala (2019b) Rebuild kerala development programme. Rebuild Kerala Initiative, Government of Kerala. Available at https://rebuild.kerala.gov.in/en/rebuild
Grimnes S, Martinsen ØG (2015) Data and models. Bioimpedance and bioelectricity basics. Elsevier, pp 329–404. https://doi.org/10.1016/B978-0-12-411470-8.00009-X
Hajat S, Ebi KL, Kovats RS, Menne B, Edwards S, Haines A (2005) The human health consequences of flooding in Europe: a review. In: Kirch W, Bertollini R, Menne B (eds) Extreme Weather Events and Public Health Responses. Springer-Verlag, Berlin/Heidelberg, pp 185–196. https://doi.org/10.1007/3-540-28862-7_18
Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143:29–36
Hao L, Rajaneesh A, van Westen C, Sajinkumar KS, Martha TR, Jaiswal P, McAdoo BG (2020) Constructing a complete landslide inventory dataset for the 2018 Monsoon disaster in Kerala, India, for land use change analysis. Earth Syst Sci Data 12(4):2899–2918. https://doi.org/10.5194/essd-12-2899-2020
Hao L, Cees van Westen A, Rajaneesh KSS, Martha TR, Jaiswal P (2022) Evaluating the relation between land use changes and the 2018 landslide disaster in Kerala, India. CATENA 216:106363. https://doi.org/10.1016/j.catena.2022.106363
Hoffmann R, Blecha D (2020) Education and disaster vulnerability in Southeast Asia: evidence and policy implications. Sustainability 12(4):1401. https://doi.org/10.3390/su12041401
Hoque MAA, Tasfia S, Ahmed N, Pradhan B (2019) Assessing spatial flood vulnerability at Kalapara Upazila in Bangladesh using an analytic hierarchy process. Sensors 19(6):1302. https://doi.org/10.3390/s19061302
Hosmer DW, Lemeshow S (2000) Applied logistic regression, 2nd Ed. Chapter 5, John Wiley and Sons, New York, NY, pp 160–164
Hunt KMR, Menon A (2020) The 2018 Kerala floods: a climate change perspective. Clim Dyn 54:2433–2446. https://doi.org/10.1007/s00382-020-05123-7
Hussain M, Tayyab M, Zhang J, Shah AA, Ullah K, Mehmood U, Al-Shaibah B (2021) GIS-based multi-criteria approach for flood vulnerability assessment and mapping in district Shangla: Khyber Pakhtunkhwa, Pakistan. Sustainability 13(6):3126. https://doi.org/10.3390/su13063126
Joshi A, Dhumka A, Dhiman Y, Rawat C, Ritika (2022) A comparative study of supervised learning techniques for remote sensing image classification. In: Sharma TK, Ahn CW, Verma OP, Panigrahi BK (eds) Soft computing: theories and applications: proceedings of SoCTA 2020, Volume 1. Springer Singapore, Singapore, pp 49–61. https://doi.org/10.1007/978-981-16-1740-9_6
Khashei-Siuki A, Keshavarz A, Sharifan H (2020) Comparison of AHP and FAHP methods in determining suitable areas for drinking water harvesting in Birjand aquifer. Iran Groundw Sustain Dev. https://doi.org/10.1016/j.gsd.2019.100328
Lee JY, Kim JS (2021) Detecting areas vulnerable to flooding using hydrological-topographic factors and logistic regression. Appl Sci. https://doi.org/10.3390/app11125652
Liu Y, Eckert CM, Earl C (2020) A review of fuzzy AHP methods for decision-making with subjective judgements. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2020.113738
Liu T, Shi P, Fang J (2022) Spatiotemporal variation in global floods with different affected areas and the contribution of influencing factors to flood-induced mortality (1985–2019). Nat Hazards. https://doi.org/10.1007/s11069-021-05150-5
Mishra V, Shah HL (2018) Hydroclimatological perspective of the Kerala flood of 2018. J Geol Soc India 92:645–650. https://doi.org/10.1007/s12594-018-1079-3
Nicholls R, Zanuttigh B, Vanderlinden JP, Weisse R, Silva R, Hanson S, Narayan S, Hoggart S, Thompson RC, de Vries W, Koundouri P (2015) Developing a holistic approach to assessing and managing coastal flood risk. Coastal risk management in a changing climate. Elsevier, pp 9–53. https://doi.org/10.1016/B978-0-12-397310-8.00002-6
Nikhil S, Danumah JH, Saha S, Prasad MK, Rajaneesh A, Mammen PC, Ajin RS, Kuriakose SL (2021) Application of GIS and AHP method in forest fire risk zone mapping: a study of the Parambikulam Tiger Reserve, Kerala, India. J Geovisualiz Spatial Anal. https://doi.org/10.1007/s41651-021-00082-x
Ochoa C, Bolon I, Durso AM, de Castañeda RR, Alcoba G, Martins SB, Chappuis F, Ray N (2020) Assessing the increase of snakebite incidence in relationship to flooding events. J Environ Public Health. https://doi.org/10.1155/2020/6135149
Okaka FO, Odhiambo BDO (2018) Relationship between flooding and out break of infectious diseases in Kenya: a review of the literature. J Environ Public Health. https://doi.org/10.1155/2018/5452938
Olson DL (1988) Opportunities and limitations of AHP in multiobjective programming. Math Comput Model 11:206–209. https://doi.org/10.1016/0895-7177(88)90481-5
Paranjothy S, Gallacher J, Amlôt R, Rubin GJ, Page L, Baxter T, Wight J, Kirrage D, McNaught R, Palmer SR (2011) Psychosocial impact of the summer 2007 floods in England. BMC Public Health. https://doi.org/10.1186/1471-2458-11-145
Peek L (2008) Children and disasters: understanding vulnerability, developing capacities, and promoting resilience: an introduction. Child Youth Environ 18(1):1–29. https://doi.org/10.7721/chilyoutenvi.18.1.0001
Pradeep GS, Danumah JH, Nikhil S, Prasad MK, Patel N, Mammen PC, Rajaneesh A, Oniga VE, Ajin RS, Kuriakose SL (2022) Forest fire risk zone mapping of Eravikulam national park in India: a comparison between frequency ratio and analytic hierarchy process methods. Croatian J For Eng 43(1):199–217. https://doi.org/10.5552/crojfe.2022.1137
Putra MSD, Andryana S, Fauziah GA (2018) Fuzzy analytical hierarchy process method to determine the quality of gemstones. Adv Fuzzy Syst. https://doi.org/10.1155/2018/9094380
Qazi WA, Abushammala MFM (2020) Multi-criteria decision analysis of waste-to-energy technologies. waste-to-energy. Elsevier, pp 265–316. https://doi.org/10.1016/B978-0-12-816394-8.00010-0
Radmehr A, Araghinejad S (2015) Flood vulnerability analysis by fuzzy spatial multi criteria decision making. Water Resour Manag 29:4427–4445. https://doi.org/10.1007/s11269-015-1068-x
Rahman M, Ningsheng C, Islam MM, Dewan A, Iqbal J, Washakh RMA, Shufeng T (2019) Flood susceptibility assessment in Bangladesh using machine learning and multi-criteria decision analysis. Earth Syst Environ 3:585–601. https://doi.org/10.1007/s41748-019-00123-y
Rasch RJ (2016) Assessing urban vulnerability to flood hazard in Brazilian municipalities. Environ Urban 28(1):145–168. https://doi.org/10.1177/0956247815620961
Rouse JW, Haas RH, Schell JA, Deering DW (1974) Monitoring vegetation systems in the Great Plains with ERTS. In: Freden SC, Mercanti EP, Becker MA (eds) Proceedings of the Third Earth Resources Technology Satellite-1 Symposium. NASA, Washington D.C., USA, pp. 309–317
Saaty TL (1980) The analytic hierarchy process: planning, priority setting, resource allocation (Decision making series). McGraw Hill, New York
Saha S, Sarkar D, Mondal P (2021) Efficiency exploration of frequency ratio, entropy and weights of evidence-information value models in flood vulnerability assessment: a study of Raiganj subdivision. Stochastic Environ Res Risk Assess Eastern India. https://doi.org/10.1007/s00477-021-02115-9
Sajinkumar KS, Arya A, Rajaneesh A, Oommen T, Yunus Ali P, Rani VR, Thrivikramji KP (2022) Migrating rivers, consequent paleochannels: the unlikely partners and hotspots of flooding. Sci Total Environ 807:150842. https://doi.org/10.1016/j.scitotenv.2021.150842
Samanta S, Pal DK, Palsamanta B (2018) Flood susceptibility analysis through remote sensing, GIS and frequency ratio model. Appl Water Sci. https://doi.org/10.1007/s13201-018-0710-1
Sam AS, Kumar R, Kächele H, Müller K (2017) Vulnerabilities to flood hazards among rural households in India. Nat Hazards 88:1133–1153. https://doi.org/10.1007/s11069-017-2911-6
Sarkar D, Mondal P (2020) Flood vulnerability mapping using frequency ratio (FR) model: a case study on Kulik river basin. Indo-Bangladesh Barind Region Applied Water Sci. https://doi.org/10.1007/s13201-019-1102-x
Seyedin H, HabibiSaravi R, Sayfouri N, Djenab VH, Hamedani FG (2017) Psychological sequels of flood on residents of southeast Caspian region. Nat Hazards 88:965–975. https://doi.org/10.1007/s11069-017-2926-z
Shahfahad MM, Kumari B, Tayyab M, Paarcha A, Asif RA (2021) Indices based assessment of built-up density and urban expansion of fast growing Surat city using multi-temporal Landsat data sets. GeoJournal 86:1607–1623. https://doi.org/10.1007/s10708-020-10148-w
Shen L, Li C (2010) Water body extraction from Landsat ETM+ imagery using adaboost algorithm. In: Proceedings of 18th International Conference on Geoinformatics. Beijing, China, pp 1–4. https://doi.org/10.1109/GEOINFORMATICS.2010.5567762
Sisodia PS, Tiwari V, Kumar A (2014) Analysis of supervised maximum likelihood classification for remote sensing image. In: Proceedings of the International Conference on Recent Advances and Innovations in Engineering (ICRAIE-2014), pp. 1–4. https://doi.org/10.1109/ICRAIE.2014.6909319
Smith K, Ward R (1998) Floods: Physical process and human impacts. Wiley, Chichester
Sunar Erbek F, Özkan C, Taberner M (2004) Comparison of maximum likelihood classification method with supervised artificial neural network algorithms for land use activities. Int J Remote Sens 25(9):1733–1748. https://doi.org/10.1080/0143116031000150077
Sun R, An D, Wei L, Shi Y, Wang L, Zhang C, Zhang P, Qi H, Wang Q (2016) Impacts of a flash flood on drinking water quality: case study of areas most affected by the 2012 Beijing flood. Heliyon 2(2):e00071. https://doi.org/10.1016/j.heliyon.2016.e00071
Swain DL, Wing OEJ, Bates PD, Done JM, Johnson KA, Cameron DR (2020a) Increased flood exposure due to climate change and population growth in the United States. Earth’s Future. https://doi.org/10.1029/2020EF001778
Swain KC, Singha C, Nayak L (2020) Flood susceptibility mapping through the GIS-AHP technique using the cloud. ISPRS Int J Geo-Inf 9(12):720. https://doi.org/10.3390/ijgi9120720
Tabari H (2020) Climate change impact on flood and extreme precipitation increases with water availability. Sci Rep. https://doi.org/10.1038/s41598-020-70816-2
Tascón-González L, Ferrer-Julià M, Ruiz M, García-Meléndez E (2020) Social vulnerability assessment for flood risk analysis. Water 12(2):558. https://doi.org/10.3390/w12020558
Tehrany MS, 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
Thomas AV, Saha S, Danumah JH, Raveendran S, Prasad MK, Ajin RS, Kuriakose SL (2021) Landslide susceptibility zonation of Idukki district using GIS in the aftermath of 2018 Kerala floods and landslides: A comparison of AHP and frequency ratio methods. J Geovisualiz Spatial Anal. https://doi.org/10.1007/s41651-021-00090-x
Thompson JA, Roecker S, Grunwald S, Owens PR (2012) Digital soil mapping. hydropedology. Elsevier, pp 665–709. https://doi.org/10.1016/B978-0-12-386941-8.00021-6
Tripathi AK, Agrawal S, Gupta RD (2021) Comparison of GIS-based AHP and fuzzy AHP methods for hospital site selection: a case study for Prayagraj City. GeoJournal, Prayagraj. https://doi.org/10.1007/s10708-021-10445-y
UNDP (2018) Kerala post disaster needs assessment: floods and landslides - August 2018. Available at https://www.undp.org/publications/post-disaster-needs-assessment-kerala
Vanama VSK, Rao YS, Bhatt CM (2021) Change detection based flood mapping using multi-temporal earth observation satellite images: 2018 flood event of Kerala. India Eur J Remote Sens 54(1):42–58. https://doi.org/10.1080/22797254.2020.1867901
Viana CM, Oliveira S, Oliveira SC, Rocha J (2019) Land use/land cover change detection and urban sprawl analysis. Spatial modeling in GIS and R for earth and environmental sciences. Elsevier, pp 621–651. https://doi.org/10.1016/B978-0-12-815226-3.00029-6
Vishnu CL, Sajinkumar KS, Oommen T, Coffman RA, Thrivikramji KP, Rani VR, Keerthy S (2019) Satellite-based assessment of the August 2018 Flood in parts of Kerala, India. Geomat Nat Haz Risk 10(1):758–767. https://doi.org/10.1080/19475705.2018.1543212
Vishnu CL, Rani VR, Sajinkumar KS, Oommen T, Bonali FL, Pareeth S, Thrivikramji K, McAdoo BG, Anilkumar Y, Rajaneesh A (2020) Catastrophic flood of August 2018, Kerala, India: partitioning role of geologic factors in modulating flood level using remote sensing data. Remote Sens Appl Soc Environ. https://doi.org/10.1016/j.rsase.2020.100426
Winter MG, Shearer B, Palmer D, Peeling D, Harmer C, Sharpe J (2016) The economic impact of landslides and floods on the road network. Procedia Eng 143:1425–1434. https://doi.org/10.1016/j.proeng.2016.06.168
Wubalem A (2021) Landslide susceptibility mapping using statistical methods in Uatzau catchment area, northwestern Ethiopia. Geoenviron Dis. https://doi.org/10.1186/s40677-020-00170-y
Xu H (2006) Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int J Remote Sens 27(14):3025–3033. https://doi.org/10.1080/01431160600589179
Yard EE, Murphy MW, Schneeberger C, Narayanan J, Hoo E, Freiman A, Lewis LS, Hill VR (2014) Microbial and chemical contamination during and after flooding in the Ohio River: Kentucky, 2011. J Environ Sci Health Part A 49(11):1236–1243. https://doi.org/10.1080/10934529.2014.910036
Yun D, Zhang Y, Ling F, Wang Q, Li W, Li X (2016) Water bodies’ mapping from Sentinel-2 imagery with modified normalized difference water index at 10-m spatial resolution produced by sharpening the SWIR band. Remote Sens 8(4):354. https://doi.org/10.3390/rs8040354
Yurdagül Kumcu S (2022) Flood management under changing climate. In: Bahadir M, Haarstrick A (eds) Water and wastewater management: global problems and measures. Springer International Publishing, Cham, pp 35–40. https://doi.org/10.1007/978-3-030-95288-4_4
Zha Y, Gao J, Ni S (2003) Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. Int J Remote Sens 24(3):583–594. https://doi.org/10.1080/01431160304987
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This study was supported by a research centre in Iran (Grant No. 54RCTR763542). The authors would like to express their gratitude to the editor and anonymous reviewers for their insightful comments on earlier versions of the manuscript.
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This study was supported by a research centre in Iran (Grant No. 54RCTR763542).
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Senan, C.P.C., Ajin, R.S., Danumah, J.H. et al. Flood vulnerability of a few areas in the foothills of the Western Ghats: a comparison of AHP and F-AHP models. Stoch Environ Res Risk Assess 37, 527–556 (2023). https://doi.org/10.1007/s00477-022-02267-2
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DOI: https://doi.org/10.1007/s00477-022-02267-2