Agapiou, A. (2021). UNESCO World Heritage properties in changing and dynamic environments: change detection methods using optical and radar satellite data. Heritage Science, 9, 64.https://doi.org/10.1186/s40494-021-00542-z

Agapiou, A., & Lysandrou, V. (2022). Remote sensing studies on monitoring natural hazards over cultural heritage sites in Cyprus. In M. M. Al Saud (Ed.), Applications of Space Techniques on the Natural Hazards in the MENA Region (pp. 167–194). Springer International Publishing.https://doi.org/10.1007/978-3-030-88874-9_8

Ahmad, M., Al Mehedi, M. A., Yazdan, M. M. S., & Kumar, R. (2022). Development of machine learning flood model using artificial neural network (ANN) at Var River. Liquids, 2(3), 147-160.https://doi.org/10.3390/liquids2030010

Ahmadlou, M., Al-Fugara, A., Al-Shabeeb, A. R., Arora, A., Al-Adamat, R., Pham, Q. B., Al-Ansari, N., Linh, N. T. T., & Sajedi, H. (2021). Flood susceptibility mapping and assessment using a novel deep learning model combining multilayer perceptron and autoencoder neural networks. Journal of Flood Risk Management, 14(1), 1–22.https://doi.org/10.1111/jfr3.12683

Ahmed, I. A., Talukdar, S., Shahfahad, Parvez, A., Rihan, M., Baig, M. R. I., & Rahman, A. (2022). Flood susceptibility modeling in the urban watershed of Guwahati using improved metaheuristic-based ensemble machine learning algorithms. Geocarto International, 37(26), 12238–12266.https://doi.org/10.1080/10106049.2022.2066200

Ali, S. A., Parvin, F., Pham, Q. B., Vojtek, M., Vojtekova, J., Costache, R., Linh, N. T. T., Nguyen, H. Q., Ahmad, A., & Ghorbani, M. A. (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. Ecological Indicators, 117, 106620.https://doi.org/10.1016/j.ecolind.2020.106620

Altaf, F., Meraj, G., & Romshoo, S. A. (2013). Morphometric analysis to infer hydrological behaviour of lidder watershed, Western Himalaya, India. Geographical Journal, 1–14.https://doi.org/10.1155/2013/178021

Arora, A., Pandey, M., Siddiqui, M. A., Hong, H., & Mishra, V. N. (2021). Spatial flood susceptibility prediction in Middle Ganga Plain: comparison of frequency ratio and Shannon’s entropy models. Geocarto International, 36(18), 2085–2116.https://doi.org/10.1080/10106049.2020.1764619

Arrighi, C. (2021). A global scale analysis of river flood risk of UNESCO World Heritage Sites. Frontiers in Water, 3, 764459.https://doi.org/10.3389/frwa.2021.764459

Arrighi, C., Brugioni, M., Castelli, F., Franceschini, S., & Mazzanti, B. (2018). Flood risk assessment in art cities: the exemplary case of Florence (Italy). Journal of Flood Risk Management, 11, S616–S631.https://doi.org/10.1111/jfr3.12226

Arnous, M. O., El-Rayes, A. E., El-Nady, H., & Helmy, A. M. (2022). Flash flooding hazard assessment, modeling, and management in the coastal zone of Ras Ghareb City, Gulf of Suez, Egypt. Journal of Coastal Conservation, 26(6).https://doi.org/10.1007/s11852-022-00916-w

Aryal, S. K., Mein, R. G., & O’Loughlin, E. M. (2003). The concept of effective length in hillslopes: assessing the influence of climate and topography on the contributing areas of catchments. Hydrological Processes, 17(1), 131–151.https://doi.org/10.1002/hyp.1137

Baalousha, H. M., Younes, A., & Yassin, M. A., & Fahs, M. (2023). Comparison of the fuzzy analytic hierarchy process (F-AHP) and fuzzy logic for flood exposure risk assessment in arid regions. Hydrology, 10(7).https://doi.org/10.3390/hydrology10070136

Beven, K. J., & Kirkby, M. J. (1979). A physically based, variable contributing area model of basin hydrology/Un modele a base physique de zone d ’appel variable de l’hydrologie du bassin versant. Hydrological Sciences Journal, 24(1), 43–69.https://doi.org/10.1080/02626667909491834

Bhuyan, M. J., Deka, N., & Saikia, A. (2023). Micro-spatial flood risk assessment in Nagaon district, Assam (India) using GIS-based multi-criteria decision analysis (MCDA) and analytical hierarchy process (AHP). Risk Analysis, 1–16.https://doi.org/10.1111/risa.14191

Bui, D. T., Hoang, N. D., Pham, T. D., Ngo, P. T. T., Hoa, P. V., Minh, N. Q., et al. (2019). A new intelligence approach based on GIS-based Multivariate Adaptive Regression Splines and metaheuristic optimization for predicting flash flood susceptible areas at high-frequency tropical typhoon area. Journal of Hydrology, 575, 314–326.https://doi.org/10.1016/j.jhydrol.2019.05.023

Bunmi Mudashiru, R., Sabtu, N., Abdullah, R., & Saleh, A., & Abustan, I. (2022). Optimality of flood influencing factors for flood hazard mapping: an evaluation of two multicriteria decision-making methods. Journal of Hydrology, 612, 128055.https://doi.org/10.1016/j.jhydrol.2022.128055

Caruso, G. D. (2017). The legacy of natural disasters: The intergenerational impact of 100 years of disasters in Latin America. Journal of Development Economics, 127, 209–233.https://doi.org/10.1016/j.jdeveco.2017.06.002

Chakraborty, S., & Mukhopadhyay, S. (2019). Assessing flood risk using analytical hierarchy process (AHP) and geographical information system (GIS): application in Coochbehar district of West Bengal, India. Natural Hazards, 99(1), 247–274.https://doi.org/10.1007/s11069-019-03737-7

Chapi, K., Singh, V. P., Shirzadi, A., Shahabi, H., Bui, D. T., Pham, B. T., & Khosravi, K. (2017). A novel hybrid artificial intelligence approach for flood susceptibility assessment. Environmental Modelling & Software, 95, 229–245.https://doi.org/10.1016/j.envsoft.2017.06.012

Chen, F., Zhou, W., Tang, Y., Li, R., Lin, H., Balz, T., Luo, J., Shi, P., Zhu, M., & Fang, C. (2022). Remote sensing-based deformation monitoring of pagodas at the Bagan cultural heritage site, Myanmar. International Journal of Digital Earth, 15, 770–788.https://doi.org/10.1080/17538947.2022.2062466

Chen, W., Cutter, S. L., & Emrich, C. T. (2013). Measuring social vulnerability to natural hazards in the Yangtze River Delta region, China. International Journal of Disaster Risk Science, 4(4), 169–181.https://doi.org/10.1007/s13753-013-0018-6

Chen, Y. (2022). Flood hazard zone mapping incorporating geographic information system (GIS) and multi-criteria analysis (MCA) techniques. Journal of Hydrology, 612(PC), 128268.https://doi.org/10.1016/j.jhydrol.2022.128268

Chen, Y. R., Yeh, C. H., & 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. Natural Hazards, 59(3), 1261–1276.https://doi.org/10.1007/s11069-011-9831-7

Costache, R., Popa, M. C., Tien Bui, D., Diaconu, D. C., Ciubotaru, N., Minea, G., & Pham, Q. B. (2020). Spatial predicting of flood potential areas using novel hybridizations of fuzzy decision-making, bivariate statistics, and machine learning. Journal of Hydrology, 585(March), 124808.https://doi.org/10.1016/j.jhydrol.2020.124808

Cunha, L. K., Krajewski, W. F., Mantilla, R., & Cunha, L. (2011). A framework for flood risk assessment under nonstationary conditions or in the absence of historical data. Journal of Flood Risk Management, 4(1), 3–22.https://doi.org/10.1111/j.1753-318X.2010.01085.x

Dandapat, K., & Panda, G. K. (2017). Flood vulnerability analysis and risk assessment using analytical hierarchy process. Modeling Earth Systems and Environment, 3(4), 1627–1646.https://doi.org/10.1007/s40808-017-0388-7

Darabi, H., Choubin, B., Rahmati, O., Torabi Haghighi, A., Pradhan, B., & Kløve, B. (2019). Urban flood risk mapping using the GARP and QUEST models: a comparative study of machine learning techniques. Journal of Hydrology, 569(December 2018), 142–154.https://doi.org/10.1016/j.jhydrol.2018.12.002

Das, S., & Gupta, A. (2021). Multi-criteria decision based geospatial mapping of flood susceptibility and temporal hydro-geomorphic changes in the Subarnarekha basin, India. Geoscience Frontiers, 12(5), 101206.https://doi.org/10.1016/j.gsf.2021.101206

De Brito, M. M., & Evers, M. (2016). Multi-criteria decision-making for flood risk management: a survey of the current state of the art. Natural Hazards and Earth System Sciences, 16(4), 1019–1033.https://doi.org/10.5194/nhess-16-1019-2016

Debnath, J., Meraj, G., Pan, N. Das, Chand, K., Debbarma, S., Sahariah, D., Gualtieri, C., Kanga, S., Singh, S. K., Farooq, M., Sahu, N., & Kumar, P. (2022). Integrated remote sensing and field-based approach to assess the temporal evolution and future projection of meanders: a case study on River Manu in North-Eastern India. PLoS ONE, 17(Issue 7 July).https://doi.org/10.1371/journal.pone.0271190

Debnath, J., Sahariah, D., Lahon, D., Nath, N., Islam, M. N., Meraj, G., Hashimoto, S., Kumar, P., Singh, S. K., Kanga, S., Chand, K., & Saikia, A. (2023). Modelling on assessment of flood risk susceptibility at the Jia Bharali River basin in Eastern Himalayas by integrating multicollinearity tests and geospatial techniques. Modeling Earth Systems and Environment, 1–27.https://doi.org/10.1007/s41748-023-00378-6

Debnath, J., Sahariah, D., Mazumdar, M., Lahon, D., Meraj, G., Hashimoto, S., Kumar, P., Singh, S. K., Kanga, S., Chand, K., & Saikia, A. (2023). Evaluating flood susceptibility in the Brahmaputra River basin: an insight into Asia’s eastern Himalayan floodplains using machine learning and multi-criteria decision-making. Earth Systems and Environment, 7(4), 733–760.https://doi.org/10.1007/s41748-023-00358-w

Degiorgis, M., Gnecco, G., Gorni, S., Roth, G., Sanguineti, M., & Taramasso, A. C. (2012). Classifiers for the detection of flood-prone areas using remote sensed elevation data. Journal of Hydrology, 470–471, 302–315.https://doi.org/10.1016/j.jhydrol.2012.09.006

Edamo, M. L., Ukumo, T. Y., Lohani, T. K., Ayana, M. T., Ayele, M. A., Mada, Z. M., & Abdi, D. M. (2022). A comparative assessment of multi-criteria decision-making analysis and machine learning methods for flood susceptibility mapping and socio-economic impacts on flood risk in Abela-Abaya floodplain of Ethiopia. Environmental Challenges.https://doi.org/10.1016/j.envc.2022.100629

Eghtesadifard, M., Afkhami, P., & Bazyar, A. (2020). An integrated approach to the selection of municipal solid waste landfills through GIS, K-Means and multi-criteria decision analysis. Environmental Research, 185, 109348.https://doi.org/10.1016/j.envres.2020.109348

Farhadi, H., Esmaeily, A., & Najafzadeh, M. (2022). Flood monitoring by integration of remote sensing technique and multi-criteria decision-making method. Computers & Geosciences, 160, 105045.https://doi.org/10.1016/j.cageo.2022.105045

Fenta, A. A., Kifle, A., Gebreyohannes, T., & Hailu, G. (2015). Spatial analysis of groundwater potential using remote sensing and GIS-based multi-criteria evaluation in Raya Valley, northern Ethiopia. Hydrogeology Journal, 23(1), 195–206.https://doi.org/10.1007/s10040-014-1198-x

Fernandez, D. S., & Lutz, M. A. (2010). Urban flood hazard zoning in Tucumán Province, Argentina, using GIS and multicriteria decision analysis. Engineering Geology, 111(1–4), 90–98.https://doi.org/10.1016/j.enggeo.2009.09.011

Figueiredo, R., Romão, X., & Paupério, E. (2020). Flood risk assessment of cultural heritage at large spatial scales: framework and application to mainland Portugal. Journal of Cultural Heritage, 43, 163–174.https://doi.org/10.1016/j.culher.2019.11.007

Agapiou, A. (2021). UNESCO World Heritage properties in changing and dynamic environments: change detection methods using optical and radar satellite data. Heritage Science, 9, 64.https://doi.org/10.1186/s40494-021-00542-z

Gerster, J., Boret, S. P., Morimoto, R., Gordon, A., & Shibayama, A. (2022). The potential of disaster digital archives in disaster education: the case of the Japan disasters digital archive (JDA) and its geo-location functions. International Journal of Disaster Risk Reduction, 77, 103085.https://doi.org/10.1016/j.ijdrr.2022.103085

Ghawana, T., Pashova, L., & Zlatanova, S. (2021). Geospatial data utilisation in national disaster management frameworks and the priorities of multilateral disaster management frameworks: case studies of India and Bulgaria. ISPRS International Journal of Geo-Information, 10, 610.https://doi.org/10.3390/ijgi10090610

Ghosh, A., Chatterjee, U., Pal, S. C., Towfiqul Islam, A. R. M., Alam, E., & Islam, M. K. (2023). Flood hazard mapping using GIS-based statistical model in vulnerable riparian regions of sub-tropical environment. Geocarto International, 38(1).https://doi.org/10.1080/10106049.2023.2285355

Ghosh, A., & Dey, P. (2021). Flood severity assessment of the coastal tract situated between Muriganga and Saptamukhi estuaries of Sundarban delta of India using frequency ratio (FR), fuzzy logic (FL), logistic regression (LR) and random forest (RF) models. Regional Studies in Marine Science, 42, 101624.https://doi.org/10.1016/j.rsma.2021.101624

Ghosh, A., & Kar, S. K. (2018). Application of analytical hierarchy process (AHP) for flood risk assessment: a case study in Malda district of West Bengal, India. Natural Hazards, 94(1), 349–368.https://doi.org/10.1007/s11069-018-3392-y

Glenn, E. P., Morino, K., Nagler, P. L., Murray, R. S., Pearlstein, S., & Hultine, K. R. (2012). Roles of saltcedar (Tamarix spp.) and capillary rise in salinizing a non-flooding terrace on a flow-regulated desert river. Journal of Arid Environments, 79, 56–65.https://doi.org/10.1016/j.jaridenv.2011.11.025

Gupta, L., & Dixit, J. (2022). A GIS-based flood risk mapping of Assam, India, using the MCDA-AHP approach at the regional and administrative level. Geocarto International, 37(26), 11867–11899.https://doi.org/10.1080/10106049.2022.2060329

Hadjimitsis, D., Agapiou, A., Alexakis, D., & Sarris, A. (2013). Exploring natural and anthropogenic risk for cultural heritage in Cyprus using remote sensing and GIS. International Journal of Digital Earth, 6, 115–142.https://doi.org/10.1080/17538947.2011.602119

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). Arabian Journal of Geosciences, 12(21).https://doi.org/10.1007/s12517-019-4754-9

Hasan, M. M., Mondol Nilay, M. S., Jibon, N. H., & Rahman, R. M. (2023). LULC changes to riverine flooding: a case study on the Jamuna River, Bangladesh using the multilayer perceptron model. Results in Engineering, 18(September 2022), 101079.https://doi.org/10.1016/j.rineng.2023.101079

Hishe, S., Bewket, W., Nyssen, J., & Lyimo, J. (2020). Analysing past land use land cover change and CA-Markov-based future modelling in the Middle Suluh Valley, Northern Ethiopia. Geocarto International, 35(3), 225–255.https://doi.org/10.1080/10106049.2018.1516241

Jahan, C. S., Rahaman, M. F., Arefin, R., Ali, M. S., & Mazumder, Q. H. (2019). Delineation of groundwater potential zones of Atrai–Sib River basin in north-west Bangladesh using remote sensing and GIS techniques. Sustainable Water Resources Management, 5(2), 689–702.https://doi.org/10.1007/s40899-018-0240-x

Jahangir, M. H., Mousavi Reineh, S. M., & Abolghasemi, M. (2019). Spatial predication of flood zonation mapping in Kan River Basin, Iran, using artificial neural network algorithm. Weather and Climate Extremes, 25(June), 100215.https://doi.org/10.1016/j.wace.2019.100215

Jenks, G. F. (1967). The data model concept in statistical mapping. International Yearbook of Cartography, 7, 186–190.

Jong, H. K. (2019). Multicollinearity and misleading statistical result. Korean Journal of Anesthesiology, 0, 2–31.https://doi.org/10.4097/kja.d.18.00212

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. Science of the Total Environment, 538, 555–563.https://doi.org/10.1016/j.scitotenv.2015.08.055

Khosravi, K., Nohani, E., Maroufinia, E., & Pourghasemi, H. R. (2016). 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. Natural Hazards, 83(2), 947–987.https://doi.org/10.1007/s11069-016-2357-2

Khosravi, K., Pham, B. T., Chapi, K., Shirzadi, A., Shahabi, H., Revhaug, I., Prakash, I., & Tien Bui, D. (2018). A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran. Science of the Total Environment, 627, 744–755.https://doi.org/10.1016/j.scitotenv.2018.01.266

Kittipongvises, S., Phetrak, A., Rattanapun, P., Brundiers, K., Buizer, J. L., & Melnick, R. (2020). AHP-GIS analysis for flood hazard assessment of the communities nearby the world heritage site on Ayutthaya Island, Thailand. International Journal of Disaster Risk Reduction, 48(November 2019), 101612.https://doi.org/10.1016/j.ijdrr.2020.101612

Kotecha, M. J., Tripathi, G., Singh, S. K., Kanga, S., Sajan, B., Meraj, G., & Misra, R. K. (2020). Geospatial modelling for identification of ground water potential zones in Luni River Basin, Rajasthan. In River Conservation and Water Resource Management (pp. 315–338). Springer Nature Singapore.https://doi.org/10.1007/978-981-15-1011-1_15

Kumar, S., Snehmani, Srivastava, P. K., Gore, A., & Singh, M. K. (2016). Fuzzy–frequency ratio model for avalanche susceptibility mapping. International Journal of Digital Earth, 9(12), 1168–1184.https://doi.org/10.1080/17538947.2016.1157140

Kundzewicz, Z. W., Kanae, S., Seneviratne, S. I., Handmer, J., Nicholls, N., Peduzzi, P., Mechler, R., Bouwer, L. M., Arnell, N., & Mach, K., et al. (2014). Flood risk and climate change: global and regional perspectives. Hydrological Sciences Journal, 59(1), 1–28.https://doi.org/10.1080/02626667.2013.856213

Lahon, D., Sahariah, D., Debnath, J., Nath, N., Meraj, G., Kumar, P., et al. (2023). Assessment of ecosystem service value in response to LULC changes using geospatial techniques: a case study in the merbil wetland of the Brahmaputra valley, Assam, India. ISPRS International Journal of Geo-Information, 12(4), 165.https://doi.org/10.3390/ijgi12040165

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. Journal of Hydrology, 572(February), 108–120.https://doi.org/10.1016/j.jhydrol.2019.03.002

Lin, J. M., & Billa, L. (2021). Spatial prediction of flood-prone areas using geographically weighted regression. Environmental Advances, 6, 100118.https://doi.org/10.1016/j.envadv.2021.100118

Mahmoud, S. H., & Gan, T. Y. (2018). Multi-criteria approach to develop flood susceptibility maps in arid regions of Middle East. Journal of Cleaner Production, 196, 216–229.https://doi.org/10.1016/j.jclepro.2018.06.047

Mangini, W., Viglione, A., Hall, J., Hundecha, Y., Ceola, S., Montanari, A., Rogger, M., Salinas, J. L., Borzì, I., & Parajka, J. (2018). Detection of trends in magnitude and frequency of flood peaks across Europe. Hydrological Sciences Journal, 63(4), 493–512.https://doi.org/10.1080/02626667.2018.1444122

Mekonnen, T. M., Mitiku, A. B., & Woldemichael, A. T. (2023). Flood hazard zoning of upper Awash River basin, Ethiopia, using the analytical hierarchy process (AHP) as compared to sensitivity analysis. The Scientific World Journal, 15.https://doi.org/10.1155/2023/1675634

Mishra, K., & Sinha, R. (2020). Flood risk assessment in the Kosi megafan using multi- criteria decision analysis: a hydro-geomorphic approach. Geomorphology, 350, 106861.https://doi.org/10.1016/j.geomorph.2019.106861

Mitra, R., & Das, J. (2023). A comparative assessment of flood susceptibility modelling of GIS-based TOPSIS, VIKOR, and EDAS techniques in the Sub-Himalayan foothills' region of Eastern India. Environmental Science and Pollution Research Series, 30(6), 16036–16067.https://doi.org/10.1007/s11356-022-23377-5

Mitra, R., Saha, P., & Das, J. (2022). Assessment of the performance of GIS-based analytical hierarchical process (AHP) approach for flood modelling in Uttar Dinajpur district of West Bengal, India. Geomatics, Natural Hazards and Risk, 13(1), 2183–2226.https://doi.org/10.1080/19475705.2022.2112094

Moise, C., Dana Negula, I., Mihalache, C. E., Lazar, A. M., Dedulescu, A. L., Rustoiu, G. T., Inel, I. C., & Badea, A. (2021). Remote sensing for cultural heritage assessment and monitoring: the case study of Alba Iulia. Sustainability, 13, 1406.https://doi.org/10.3390/su13031406

Mojaddadi, H., Pradhan, B., Nampak, H., Ahmad, N., & Ghazali, A. H. bin. (2017). Ensemble machine-learning-based geospatial approach for flood risk assessment using multi- sensor remote-sensing data and GIS. Geomatics, Natural Hazards and Risk, 8(2), 1080–1102.https://doi.org/10.1080/19475705.2017.1294113

Mukherjee, I., & Singh, U. K. (2020). Delineation of groundwater potential zones in a drought-prone semi-arid region of east India using GIS and analytical hierarchical process techniques. Catena, 194(December 2019), 104681.https://doi.org/10.1016/j.catena.2020.104681

Onuşluel Gül, G. (2013). Estimating flood exposure potentials in Turkish catchments through index-based flood mapping. Natural Hazards, 69(1), 403–423.https://doi.org/10.1007/s11069-013-0717-8

Pan, W., Yan, M., Zhao, Z., & Gulzar, M. A. (2022). Flood risk assessment and management in urban communities: the case of communities in Wuhan. Land, 12(1), 112.https://doi.org/10.3390/land12010112

Pathan, A. I., Girish Agnihotri, P., Said, S., & Patel, D. (2022). AHP and TOPSIS based flood risk assessment-a case study of the Navsari City, Gujarat, India. Environmental Monitoring and Assessment, 194(7), 509.https://doi.org/10.1007/s10661-022-10116-2

Peng, J., & Zhang, J. (2022). Urban flooding risk assessment based on GIS- game theory combination weight: a case study of Zhengzhou City. International Journal of Disaster Risk Reduction, 77(January), 103080.https://doi.org/10.1016/j.ijdrr.2022.103080

Pham, B. T., Jaafari, A., Phong, T. Van, Yen, H. P. H., Tuyen, T. T., Luong, V. Van, Nguyen, H. D., Le, H. Van, & Foong, L. K. (2021). Improved flood susceptibility mapping using a best first decision tree integrated with ensemble learning techniques. Geoscience Frontiers, 12(3), 101105.https://doi.org/10.1016/j.gsf.2020.11.003

Pham, V.-M., Van Nghiem, S., Van Pham, C., & Luu, M. P. T. (2021). Urbanization impact on landscape patterns in cultural heritage preservation sites: a case study of the complex of Huế Monuments, Vietnam. Landscape Ecology, 36, 1235–1260.https://doi.org/10.1007/s10980-020-01189-0

Pradhan, B., & Lee, S. (2010). Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environmental Earth Sciences, 60(5), 1037–1054.https://doi.org/10.1007/s12665-009-0245-8

Pradhan, B., & Youssef, A. M. (2010). Manifestation of remote sensing data and GIS on landslide hazard analysis using spatial-based statistical models. Arabian Journal of Geosciences, 3(3), 319–326.https://doi.org/10.1007/s12517-009-0089-2

Radwan, F., Alazba, A. A., & Mossad, A. (2019). Flood risk assessment and mapping using AHP in arid and semiarid regions. Acta Geophysica, 67(1), 215–229.https://doi.org/10.1007/s11600-018-0233-z

Rahman, M., Ningsheng, C., Islam, M. M., Dewan, A., Iqbal, J., Washakh, R. M. A., & Shufeng, T. (2019). Flood susceptibility assessment in Bangladesh using machine learning and multi-criteria decision analysis. Earth Systems and Environment, 3(3), 585–601.https://doi.org/10.1007/s41748-019-00123-y

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.https://doi.org/10.1080/10106049.2015.1041559

Rahmati, O., Zeinivand, H., & Besharat, M. (2016). Flood hazard zoning in Yasooj region, Iran, using GIS and multi-criteria decision analysis. Geomatics, Natural Hazards and Risk, 7(3), 1000–1017.https://doi.org/10.1080/19475705.2015.1045043

Rather, M. A., Meraj, G., Farooq, M., Shiekh, B. A., Kumar, P., Kanga, S., et al. (2022). Identifying the potential dam Sites to avert the risk of catastrophic floods in the Jhelum basin, Kashmir, NW Himalaya, India. Remote Sensing, 14(7), 1538.https://doi.org/10.3390/rs14071538

Rosser, J. F., Leibovici, D. G., & Jackson, M. J. (2017). Rapid flood inundation mapping using social media, remote sensing and topographic data. Natural Hazards, 87(1), 103–120.https://doi.org/10.1007/s11069-017-2751-2

Roszkowska, E. (2013). Rank ordering criteria weighting methods–a comparative overview. Optimum. Studia Ekonomiczne, 5(65), 14–33.

Roy, S., Bose, A., & Chowdhury, I. R. (2021). Flood risk assessment using geospatial data and multi-criteria decision approach: a study from historically active flood-prone region of Himalayan foothill, India. Arabian Journal of Geosciences, 14(11).https://doi.org/10.1007/s12517-021-07324-8

Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 15(3), 234–281.https://doi.org/10.1016/0022-2496(77)90033-5

Saaty, T. L. (1980). The Analytic Hierarchy Process: Planning, Priority Setting, Resources Allocation. Mcgraw-Hill.

Saaty, T. L. (1988). What Is the Analytic Hierarchy Process? In Analytic Hierarchy Process (pp. 109–121). Springer Berlin Heidelberg.https://doi.org/10.1007/978-3-642-83501-2_6

Saber, M., Boulmaiz, T., Guermoui, M., Abdrabo, K. I., Kantoush, S. A., Sumi, T., Boutaghane, H., Nohara, D., & Mabrouk, E. (2022). Examining LightGBM and CatBoost models for wadi flash flood susceptibility prediction. Geocarto International, 37(25), 7462–7487.https://doi.org/10.1080/10106049.2021.1974959

Sahana, M., & Patel, P. P. (2019). A comparison of frequency ratio and fuzzy logic models for flood susceptibility assessment of the lower Kosi River Basin in India. Environmental Earth Sciences, 78(10), 1–27.https://doi.org/10.1007/s12665-019-8285-1

Sahariah, D., Debnath, J., Nath, N., Saikia, A., Lahon, D., & Meraj, G., Kumar, P., et al. (2023). Assessment of ecosystem service value in response to LULC changes using geospatial techniques: a case study in the merbil wetland of the Brahmaputra valley, Assam, India. ISPRS International Journal of Geo-Information, 12(4), 165.https://doi.org/10.3390/ijgi12040165

Salahshouri, P., & Vafaei Nezhad, A. R. (2012). Change detection of Karkheh River floodplains of the Karkheh dam reservoir using remote sensing and GIS. Journal of Applied RS & GIS Techniques in Natural Resources Science, 3(3), 85-99.

Salajegheh, A., Moghaddam Nia, A., Salajegheh, A., Ghaderi, K., Talebpour Asl, D., Al-Ansari, N., et al. (2023). Flood susceptibility mapping using support vector regression and hyper-parameter optimization. Journal of Flood Risk Management, 16(4), e12920.https://doi.org/10.1111/jfr3.12920

Salvati, A., Moghaddam Nia, A., Salajegheh, A., Ghaderi, K., Talebpour Asl, D., Al-Ansari, N., et al. (2023). Flood susceptibility mapping using support vector regression and hyper-parameter optimization. Journal of Flood Risk Management, 16(4), e12920. (This appears to be a duplicate of 105, keeping for completeness but acknowledging it's the same DOI)

Sanyal, J., Densmore, A. L., & Carbonneau, P. (2014). Analysing the effect of land-use/cover changes at sub-catchment levels on downstream flood peaks: a semi-distributed modelling approach with sparse data. Catena, 118, 28–40.https://doi.org/10.1016/j.catena.2014.01.015

Sanyal, J., & Lu, X. X. (2005). Remote sensing and GIS-based flood vulnerability assessment of human settlements: a case study of Gangetic West Bengal, India. Hydrological Processes, 19(18), 3699–3716.https://doi.org/10.1002/hyp.5852

Sarigöl, M., & Nur Yesilyurt, S. (2022). Flood routing calculation with ANN, SVM, GPR, and RTE methods. Polish Journal of Environmental Studies, 31(6), 5221-5228.https://doi.org/10.15244/pjoes/152345

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 Science, 10(1), 1–13.https://doi.org/10.1007/s13201-019-1102-x

Sarkar, D., Saha, S., & Mondal, P. (2022). GIS-based frequency ratio and Shannon’s entropy techniques for flood vulnerability assessment in Patna district, Central Bihar, India. International Journal of Environmental Science and Technology, 19(9), 8911–8932.https://doi.org/10.1007/s13762-021-03627-1

Shahri, A. A., Spross, J., Johansson, F., & Larsson, S. (2019). Landslide susceptibility hazard map in southwest Sweden using artificial neural network. Catena, 183, 104225.https://doi.org/10.1016/j.catena.2019.104225

Souissi, D., Zouhri, L., Hammami, S., Msaddek, M. H., Zghibi, A., & Dlala, M. (2020). GIS- based MCDM–AHP modeling for flood susceptibility mapping of arid areas, southeastern Tunisia. Geocarto International, 35(9), 991–1017.https://doi.org/10.1080/10106049.2019.1566405

Swets, J. A. (1988). Measuring the accuracy of diagnostic systems. Science, 240(4857), 1285–1293.https://doi.org/10.1126/science.3287615

Tapsell, S. M., Penning-Rowsell, E. C., Tunstall, S. M., & Wilson, T. L. (2002). Vulnerability to flooding : health. Philosophical Transactions of the Royal Society of London, 1511–1525.https://doi.org/10.1098/rsta.2002.1092

Termeh, S. V. R., Kornejady, A., Pourghasemi, H. R., & Keesstra, S. (2018). Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms. Science of the Total Environment, 615, 438–451.https://doi.org/10.1016/j.scitotenv.2017.09.205

Toosi, A. S., Calbimonte, G. H., Nouri, H., & Alaghmand, S. (2019). River basin-scale flood hazard assessment using a modified multi-criteria decision analysis approach: a case study. Journal of Hydrology, 574, 660–671.https://doi.org/10.1016/j.jhydrol.2019.05.006

Trizio, F., Torrijo, F. J., Mileto, C., & Vegas, F. (2021). Flood risk in a heritage city: Alzira as a case study. Water (Switzerland), 13(9).https://doi.org/10.3390/w13091138

United Nations. (2023). Office for Outer Space Affairs, United Nations Platform for Spacebased Information for Disaster Management and Emergency Response, Disaster Risk Management.https://www.un-spider.org/risks-and-disasters/disaster-risk-management

Vegad, U., Pokhrel, Y., & Mishra, V. (2023). Flood risk assessment for Indian sub-continental river basins. Hydrology and Earth System Sciences Discussions, 1–27.https://doi.org/10.5194/hess-2023-149

Vignesh, K. S., Anandakumar, I., Ranjan, R., & Borah, D. (2021). Flood vulnerability assessment using an integrated approach of multi-criteria decision-making model and geospatial techniques. Modeling Earth Systems and Environment, 7, 767–781.https://doi.org/10.1007/s40808-020-00898-1

Vivekananda, G. N., & Swathi, R., & Sujith, A. V. L. N. (2021). Multi-temporal image analysis for LULC classification and change detection. European Journal of Remote Sensing, 54(Suppl. 2), 189–199.https://doi.org/10.1080/22797254.2020.1771215

Vojinovic, Z., Hammond, M., Golub, D., Hirunsalee, S., Weesakul, S., Meesuk, V., Medina, N., Sanchez, A., Kumara, S., & Abbott, M. (2016). Holistic approach to flood risk assessment in areas with cultural heritage: a practical application in Ayutthaya, Thailand. Natural Hazards, 81, 589–616.https://doi.org/10.1007/s11069-015-2098-7

Wang, J.-J. (2015). Flood risk maps to cultural heritage: measures and process. Journal of Cultural Heritage, 16, 210–220.https://doi.org/10.1016/j.culher.2014.03.002

Zadeh, S. M., Burn, D. H., & O’Brien, N. (2020). Detection of trends in flood magnitude and frequency in Canada. Journal of Hydrology: Regional Studies, 28, 100673.https://doi.org/10.1016/j.ejrh.2020.100673

Zhang, K., Shalehy, M. H., Ezaz, G. T., Chakraborty, A., Mohib, K. M., & Liu, L. (2022). An integrated flood risk assessment approach based on coupled hydrological-hydraulic modeling and bottom-up hazard vulnerability analysis. Environmental Modelling & Software, 148, 105279.https://doi.org/10.1016/j.envsoft.2021.105279