{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T00:51:26Z","timestamp":1778806286880,"version":"3.51.4"},"reference-count":91,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,12,27]],"date-time":"2019-12-27T00:00:00Z","timestamp":1577404800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Concerning the significant increase in the negative effects of flash-floods worldwide, the main goal of this research is to evaluate the power of the Analytical Hierarchy Process (AHP), fi (kNN), K-Star (KS) algorithms and their ensembles in flash-flood susceptibility mapping. To train the two stand-alone models and their ensembles, for the first stage, the areas affected in the past by torrential phenomena are identified using remote sensing techniques. Approximately 70% of these areas are used as a training data set along with 10 flash-flood predictors. It should be remarked that the remote sensing techniques play a crucial role in obtaining eight out of 10 flash-flood conditioning factors. The predictive capability of predictors is evaluated through the Information Gain Ratio (IGR) method. As expected, the slope angle results in the factor with the highest predictive capability. The application of the AHP model implies the construction of ten pair-wise comparison matrices for calculating the normalized weights of each flash-flood predictor. The computed weights are used as input data in kNN\u2013AHP and KS\u2013AHP ensemble models for calculating the Flash-Flood Potential Index (FFPI). The FFPI also is determined through kNN and KS stand-alone models. The performance of the models is evaluated using statistical metrics (i.e., sensitivity, specificity and accuracy) while the validation of the results is done by constructing the Receiver Operating Characteristics (ROC) Curve and Area Under Curve (AUC) values and by calculating the density of torrential pixels within FFPI classes. Overall, the best performance is obtained by the kNN\u2013AHP ensemble model.<\/jats:p>","DOI":"10.3390\/rs12010106","type":"journal-article","created":{"date-parts":[[2019,12,27]],"date-time":"2019-12-27T11:42:47Z","timestamp":1577446967000},"page":"106","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":224,"title":["Flash-Flood Susceptibility Assessment Using Multi-Criteria Decision Making and Machine Learning Supported by Remote Sensing and GIS Techniques"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6876-8572","authenticated-orcid":false,"given":"Romulus","family":"Costache","sequence":"first","affiliation":[{"name":"Research Institute of the University of Bucharest, 90-92 Sos. Panduri, 5th District, 050663 Bucharest, Romania"},{"name":"National Institute of Hydrology and Water Management, 97E Sos. Bucure\u0219ti-Ploie\u0219ti, 1st District, 013686 Bucharest, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Quoc Bao","family":"Pham","sequence":"additional","affiliation":[{"name":"Department of Hydraulic and Ocean Engineering, National Cheng-Kung University, Tainan 701, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7181-8406","authenticated-orcid":false,"given":"Ehsan","family":"Sharifi","sequence":"additional","affiliation":[{"name":"Department of Meteorology and Geophysics, University of Vienna, 1090 Vienna, Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nguyen Thi Thuy","family":"Linh","sequence":"additional","affiliation":[{"name":"Department of Hydraulic and Ocean Engineering, National Cheng-Kung University, Tainan 701, Taiwan"},{"name":"Faculty of Water Resource Engineering, Thuyloi University, Hanoi 100000, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9356-2798","authenticated-orcid":false,"given":"S.I.","family":"Abba","sequence":"additional","affiliation":[{"name":"Department of Physical Planning Development, Yusuf Maitama Sule University, Kano 700231, Nigeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9369-3173","authenticated-orcid":false,"given":"Matej","family":"Vojtek","sequence":"additional","affiliation":[{"name":"Department of Geography and Regional Development, Faculty of Natural Sciences, Constantine the Philosopher University in Nitra, Trieda A. Hlinku 1, 94974 Nitra, Slovakia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8904-9673","authenticated-orcid":false,"given":"Jana","family":"Vojtekov\u00e1","sequence":"additional","affiliation":[{"name":"Department of Geography and Regional Development, Faculty of Natural Sciences, Constantine the Philosopher University in Nitra, Trieda A. Hlinku 1, 94974 Nitra, Slovakia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pham Thi Thao","family":"Nhi","sequence":"additional","affiliation":[{"name":"Institute of Research and Development, Duy Tan University, Danang 550000, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dao Nguyen","family":"Khoi","sequence":"additional","affiliation":[{"name":"Faculty of Environment, University of Science, Vietnam National University Ho Chi Minh City, Ho Chi Minh City 700000, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,27]]},"reference":[{"key":"ref_1","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. 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