{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T05:34:38Z","timestamp":1775021678017,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,8,19]],"date-time":"2022-08-19T00:00:00Z","timestamp":1660867200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMSE"],"abstract":"<jats:p>Floods have become more and more severe and frequent with global climate change. The present study focuses on the Black Sea\u2019s immediate riparian area over which the Danube Delta extends. Due to the accelerated increase in the severity of floods, the vulnerability of the deltaic areas is augmenting. Therefore, it is very important to adopt measures to mitigate the negative effects of these phenomena. The basis of the measures to limit the negative effects is the activity of identifying areas prone to flooding. Thus, this research paper presents a methodology for estimating flood susceptibility using the Analytical Hierarchy Process (AHP) and Fuzzy-Analytical Hierarchy Process (FAHP) models. To determine the susceptibility to these natural risk phenomena, the following eight flood predictors were taken into account: slope, elevation, altitude above channel, land use, hydrological soil group, lithology distance from the river, and distance from water bodies. Furthermore, the weights that each flood predictor has in terms of determining flood susceptibility were determined through the previously mentioned models. The results revealed that the slope is the most important predictor, followed by elevation, distance from the river, and land use. These weights were used in the GIS environment to evaluate the susceptibility to floods from a spatial point of view. The areas with a high\/very high value for these phenomena occupy over 70% of the surface of the Danube Delta.<\/jats:p>","DOI":"10.3390\/jmse10081149","type":"journal-article","created":{"date-parts":[[2022,8,21]],"date-time":"2022-08-21T23:51:01Z","timestamp":1661125861000},"page":"1149","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Modern Techniques for Flood Susceptibility Estimation across the Deltaic Region (Danube Delta) from the Black Sea\u2019s Romanian Sector"],"prefix":"10.3390","volume":"10","author":[{"given":"Anca","family":"Cr\u0103ciun","sequence":"first","affiliation":[{"name":"Doctoral School in Ecology, Faculty of Biology, University of Bucharest, 91\u201395 Splaiul Independentei, 050095 Bucharest, Romania"},{"name":"Danube Delta National Institute for Research and Development, 165 Babadag Street, 820112 Tulcea, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6876-8572","authenticated-orcid":false,"given":"Romulus","family":"Costache","sequence":"additional","affiliation":[{"name":"Danube Delta National Institute for Research and Development, 165 Babadag Street, 820112 Tulcea, Romania"},{"name":"Department of Civil Engineering, Transilvania University of Brasov, 5, Turnului Street, 500152 Brasov, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9840-2443","authenticated-orcid":false,"given":"Alina","family":"B\u0103rbulescu","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Transilvania University of Brasov, 5, Turnului Street, 500152 Brasov, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Subodh Chandra","family":"Pal","sequence":"additional","affiliation":[{"name":"Department of Geography, The University of Burdwan, Bardhaman 713104, West Bengal, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Iulia","family":"Costache","sequence":"additional","affiliation":[{"name":"Faculty of Geography, University of Bucharest, 010041 Bucharest, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2862-5515","authenticated-orcid":false,"given":"Cristian \u0218tefan","family":"Dumitriu","sequence":"additional","affiliation":[{"name":"Doctoral School, Technical University of Civil Engineering, Bucharest, 124 Lacul Tei Bd., 020396 Bucharest, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1537","DOI":"10.1002\/hyp.6692","article-title":"Comparing a 1D Hydraulic Model with a 2D Hydraulic Model for the Simulation of Extreme Glacial Outburst Floods","volume":"22","author":"Alho","year":"2008","journal-title":"Hydrol. Process."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2511","DOI":"10.1007\/s11069-020-04283-3","article-title":"Application of probabilistic method in maximum tsunami height prediction considering stochastic seabed topography","volume":"104","author":"Zhang","year":"2020","journal-title":"Nat. Hazards"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"146312","DOI":"10.1016\/j.scitotenv.2021.146312","article-title":"Assessment of the sustainability of Gymnocypris eckloni habitat under river damming in the source region of the Yellow","volume":"778","author":"Quan","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3208","DOI":"10.1029\/2018JG004589","article-title":"The sensitivity of North American terrestrial carbon fluxes to spatial and temporal variation in soil moisture: An analysis using radar-derived estimates of root-zone soil moisture","volume":"124","author":"Zhang","year":"2019","journal-title":"J. Geophys. Res. Biogeosci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1252","DOI":"10.1080\/10106049.2018.1474276","article-title":"Flood Susceptibility Assessment Using Integration of Adaptive Network-Based Fuzzy Inference System (ANFIS) and Biogeography-Based Optimization (BBO) and BAT Algorithms (BA)","volume":"34","author":"Ahmadlou","year":"2019","journal-title":"Geocarto Int."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1038\/nclimate3179","article-title":"IPCC Reasons for Concern Regarding Climate Change Risks","volume":"7","author":"Oppenheimer","year":"2017","journal-title":"Nat. Clim. Chang."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1139\/er-2020-0019","article-title":"A Review of Machine Learning Applications in Wildfire Science and Management","volume":"28","author":"Jain","year":"2020","journal-title":"Environ. Rev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"93","DOI":"10.5194\/nhess-19-93-2019","article-title":"Characteristics and influencing factors of rainfall-induced landslide and debris flow hazards in Shaanxi Province, China","volume":"19","author":"Zhang","year":"2019","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"105279","DOI":"10.1016\/j.envsoft.2021.105279","article-title":"An integrated flood risk assessment approach based on coupled hydrological-hydraulic modeling and bottom-up hazard vulnerability analysis","volume":"148","author":"Zhang","year":"2022","journal-title":"Environ. Model Softw."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"125440","DOI":"10.1016\/j.jhydrol.2020.125440","article-title":"A hybrid runoff generation modelling framework based on spatial combination of three runoff generation schemes for semi-humid and semi-arid watersheds","volume":"590","author":"Liu","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1016\/j.scitotenv.2017.12.256","article-title":"Application of Fuzzy Weight of Evidence and Data Mining Techniques in Construction of Flood Susceptibility Map of Poyang County, China","volume":"625","author":"Hong","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"138868","DOI":"10.1016\/j.scitotenv.2020.138868","article-title":"Inferencing the Land Subsidence in the Nile Delta Using Sentinel-1 Satellites and GPS between 2015 and 2019","volume":"729","author":"Rateb","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"126964","DOI":"10.1016\/j.jhydrol.2021.126964","article-title":"Exploring the utility of radar and satellite-sensed precipitation and their dynamic bias correction for integrated prediction of flood and landslide hazards","volume":"603","author":"Wang","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"212","DOI":"10.3390\/w11020212","article-title":"Rainfall-Runoff Modelling Using Hydrological Connectivity Index and Artificial Neural Network Approach","volume":"11","author":"Asadi","year":"2019","journal-title":"Water"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"101005","DOI":"10.1016\/j.uclim.2021.101005","article-title":"Simulation and design of joint distribution of rainfall and tide level in Wuchengxiyu Region, China","volume":"40","author":"Gao","year":"2021","journal-title":"Urban Clim."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhao, F., Song, L., Peng, Z., Yang, J., Luan, G., Chu, C., Ding, J., Feng, S., Jing, Y., and Xie, Z. (2021). Night-time light remote sensing mapping: Construction and analysis of ethnic minority development index. Remote Sens., 13.","DOI":"10.3390\/rs13112129"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3700","DOI":"10.1080\/01431161.2014.915595","article-title":"Attribution of divergent northern vegetation growth responses to lengthening non-frozen seasons using satellite optical-NIR and microwave remote sensing","volume":"35","author":"Kim","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e08048","DOI":"10.1016\/j.heliyon.2021.e08048","article-title":"A GIS Based Flood Vulnerability Modelling of Anambra State Using an Integrated IVFRN-DEMATEL-ANP Model","volume":"7","author":"Chukwuma","year":"2021","journal-title":"Heliyon"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1007\/s12665-015-4795-7","article-title":"GIS-Based Landslide Susceptibility Mapping Using Analytical Hierarchy Process (AHP) and Certainty Factor (CF) Models for the Baozhong Region of Baoji City, China","volume":"75","author":"Chen","year":"2016","journal-title":"Environ. Earth Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.resconrec.2011.08.004","article-title":"An AHP-Based Fuzzy Interval TOPSIS Assessment for Sustainable Expansion of the Solid Waste Management System in Set\u00fabal Peninsula, Portugal","volume":"56","author":"Pires","year":"2011","journal-title":"Resour. Conserv. Recy."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1007\/s11069-008-9337-0","article-title":"Perception of Flood Risk in Danube Delta, Romania","volume":"50","author":"Avram","year":"2009","journal-title":"Nat. Hazards"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhou, G., Song, B., Liang, P., Xu, J., and Yue, T. (2022). Voids Filling of DEM with Multiattention Generative Adversarial Network Model. Remote Sens., 14.","DOI":"10.3390\/rs14051206"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/S0022-1694(01)00478-4","article-title":"Slope Runoff Processes and Flow Generation in a Subarctic, Subalpine Catchment","volume":"253","author":"Carey","year":"2001","journal-title":"J. Hydrol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3239","DOI":"10.1007\/s11269-019-02301-z","article-title":"Flood Susceptibility Assessment by Using Bivariate Statistics and Machine Learning Models-A Useful Tool for Flood Risk Management","volume":"33","author":"Costache","year":"2019","journal-title":"Water Resour. Manag."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Costache, R., Bao Pham, Q., Corodescu-Ro\u0219ca, E., C\u00eempianu, C., Hong, H., Thi Thuy Linh, N., Ming Fai, C., Najah Ahmed, A., Vojtek, M., and Muhammed Pandhiani, S. (2020). Using GIS, Remote Sensing, and Machine Learning to Highlight the Correlation between the Land-Use\/Land-Cover Changes and Flash-Flood Potential. Remote Sens., 12.","DOI":"10.3390\/rs12091422"},{"key":"ref_26","first-page":"91","article-title":"The Vulnerability of the Territorial-Administrative Units to the Hydrological Phenomena of Risk (Flash-Floods). Case Study: The Subcarpathian Sector of Buz\u0103u Catchment","volume":"23","author":"Costache","year":"2013","journal-title":"An. Univ. Oradea\u2013Ser. Geogr."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"109939","DOI":"10.1016\/j.apradiso.2021.109939","article-title":"Research on recognition of gas saturation in sandstone reservoir based on capture mode","volume":"178","author":"Dong","year":"2021","journal-title":"Appl. Radiat. Isot."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"107138","DOI":"10.1016\/j.petrol.2020.107138","article-title":"Geological conditions and exploration potential of shale gas reservoir in Wufeng and Longmaxi Formation of southeastern Sichuan Basin, China","volume":"191","author":"Fan","year":"2020","journal-title":"J. Pet. Sci. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Xie, W., Li, X., Jian, W., Yang, Y., Liu, H., Robledo, L.F., and Nie, W. (2021). A novel hybrid method for landslide susceptibility mapping-based geodetector and machine learning cluster: A case of Xiaojin county, China. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10020093"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1007\/s12040-013-0309-8","article-title":"Delineation of Groundwater Potential Zone: An AHP\/ANP Approach","volume":"122","author":"Agarwal","year":"2013","journal-title":"J. Earth Syst. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Costache, R., Ali, S.A., Parvin, F., Pham, Q.B., Arabameri, A., Nguyen, H., Cr\u0103ciun, A., and Anh, D.T. (2021). Detection of Areas Prone to Flood-Induced Landslides Risk Using Certainty Factor and Its Hybridization with FAHP, XGBoost and Deep Learning Neural Network. Geocarto Int., 1\u201336.","DOI":"10.1080\/10106049.2021.1973115"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Gigovi\u0107, L., Pamu\u010dar, D., Baji\u0107, Z., and Drobnjak, S. (2017). Application of GIS-Interval Rough AHP Methodology for Flood Hazard Mapping in Urban Areas. Water, 9.","DOI":"10.3390\/w9060360"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Cao, Y., Jia, H., Xiong, J., Cheng, W., Li, K., Pang, Q., and Yong, Z. (2020). Flash Flood Susceptibility Assessment Based on Geodetector, Certainty Factor, and Logistic Regression Analyses in Fujian Province, China. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9120748"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.jhydrol.2010.07.002","article-title":"Sensitivity of the Hydrological Response to the Variability of Rainfall Fields and Soils for the Gard 2002 Flash-Flood Event","volume":"394","author":"Anquetin","year":"2010","journal-title":"J. Hydrol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"863","DOI":"10.5194\/nhess-15-863-2015","article-title":"Group Decision-Making Approach for Flood Vulnerability Identification Using the Fuzzy VIKOR Method","volume":"15","author":"Lee","year":"2015","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Sattar, A., Bonakdari, H., Gharabaghi, B., and Radecki-Pawlik, A. (2019). Hydraulic Modeling and Evaluation Equations for the Incipient Motion of Sandbags for Levee Breach Closure Operations. Water, 11.","DOI":"10.3390\/w11020279"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.jclepro.2018.06.047","article-title":"Multi-Criteria Approach to Develop Flood Susceptibility Maps in Arid Regions of Middle East","volume":"196","author":"Mahmoud","year":"2018","journal-title":"J. Clean. Prod."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"931","DOI":"10.1007\/s11069-021-04862-y","article-title":"Landslide hazard assessment based on Bayesian optimization\u2013support vector machine in Nanping City, China","volume":"109","author":"Xie","year":"2021","journal-title":"Nat. Hazards"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2816","DOI":"10.1080\/02626667.2020.1842412","article-title":"New Neural Fuzzy-Based Machine Learning Ensemble for Enhancing the Prediction Accuracy of Flood Susceptibility Mapping","volume":"65","author":"Costache","year":"2020","journal-title":"Hydrol. Sci. J."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"101085","DOI":"10.1016\/j.uclim.2022.101085","article-title":"Statistical analysis of regional air temperature characteristics before and after dam construction","volume":"41","author":"Chen","year":"2022","journal-title":"Urban Clim."},{"key":"ref_41","first-page":"1","article-title":"Spatial Flood Susceptibility Prediction in Middle Ganga Plain: Comparison of Frequency Ratio and Shannon\u2019s Entropy Models","volume":"36","author":"Arora","year":"2019","journal-title":"Geocarto Int."},{"key":"ref_42","first-page":"1","article-title":"Incorporating Multi-Criteria Decision-Making and Fuzzy-Value Functions for Flood Susceptibility Assessment","volume":"36","author":"Azareh","year":"2019","journal-title":"Geocarto Int."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1038","DOI":"10.1016\/j.scitotenv.2019.02.422","article-title":"Flash Flood Susceptibility Modeling Using an Optimized Fuzzy Rule Based Feature Selection Technique and Tree Based Ensemble Methods","volume":"668","author":"Bui","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2647","DOI":"10.5194\/hess-23-2647-2019","article-title":"Sensitivity of hydrological models to temporal and spatial resolutions of rainfall data","volume":"23","author":"Huang","year":"2019","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"106620","DOI":"10.1016\/j.ecolind.2020.106620","article-title":"GIS-Based Comparative Assessment of Flood Susceptibility Mapping Using Hybrid Multi-Criteria Decision-Making Approach, Na\u00efve Bayes Tree, Bivariate Statistics and Logistic Regression: A Case of Top\u013ea Basin, Slovakia","volume":"117","author":"Ali","year":"2020","journal-title":"Ecol. Indic."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1111\/1539-6924.00335","article-title":"Floods and Climate Change: Interactions and Impacts","volume":"23","author":"Bronstert","year":"2003","journal-title":"Risk Anal."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.envsoft.2017.06.012","article-title":"A Novel Hybrid Artificial Intelligence Approach for Flood Susceptibility Assessment","volume":"95","author":"Chapi","year":"2017","journal-title":"Environ. Model. Softw."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Yin, L., Wang, L., Keim, B.D., Konsoer, K., and Zheng, W. (2022). Wavelet analysis of dam injection and discharge in three gorges dam and reservoir with precipitation and river discharge. Water, 14.","DOI":"10.3390\/w14040567"},{"key":"ref_49","first-page":"105","article-title":"Radar Sensor Network Resource Allocation for Fused Target Tracking: A Brief Review","volume":"87\u201388","author":"Yan","year":"2022","journal-title":"Inf. Fusion"}],"container-title":["Journal of Marine Science and Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-1312\/10\/8\/1149\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:12:23Z","timestamp":1760141543000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-1312\/10\/8\/1149"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,19]]},"references-count":49,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["jmse10081149"],"URL":"https:\/\/doi.org\/10.3390\/jmse10081149","relation":{},"ISSN":["2077-1312"],"issn-type":[{"value":"2077-1312","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,19]]}}}