{"authors":[{"id":"orcid_______::41bf6ea3c541f67a7467a4a71c14042a","fullName":"Panagiotou, C F","name":"C. F.","surname":"Panagiotou","rank":1,"pid":{"id":{"scheme":"orcid","value":"0000-0002-5258-359x"},"provenance":null}},{"id":null,"fullName":"Guerrisi, G","name":"G.","surname":"Guerrisi","rank":2,"pid":null},{"id":"orcid_______::16c576e6ea7cce854848aee770403b62","fullName":"De Santis, D","name":"D.","surname":"Santis","rank":3,"pid":{"id":{"scheme":"orcid","value":"0000-0002-5123-8161"},"provenance":null}},{"id":null,"fullName":"Del Frate, F","name":"F.","surname":"Del Frate","rank":4,"pid":{"id":{"scheme":"orcid_pending","value":"0000-0002-1655-0643"},"provenance":null}},{"id":null,"fullName":"Tzouvaras, M","name":"M.","surname":"Tzouvaras","rank":5,"pid":null}],"openAccessColor":"gold","publiclyFunded":false,"eoscIfGuidelines":null,"type":"publication","language":{"code":"eng","label":"English"},"countries":[{"code":"CY","label":"Cyprus","provenance":null},{"code":"IT","label":"Italy","provenance":null}],"subjects":[{"subject":{"scheme":"keyword","value":"NATURAL SCIENCES"},"provenance":null},{"subject":{"scheme":"keyword","value":"Data-scarce environments"},"provenance":null},{"subject":{"scheme":"keyword","value":"Flood risk management"},"provenance":null},{"subject":{"scheme":"keyword","value":"Shallow neural network"},"provenance":null},{"subject":{"scheme":"keyword","value":"Machine learning"},"provenance":null},{"subject":{"scheme":"keyword","value":"Data-scarce environments; Flood risk management; Generalized models; Machine learning; Shallow neural network; Simplified models"},"provenance":null},{"subject":{"scheme":"keyword","value":"Generalized models"},"provenance":null},{"subject":{"scheme":"keyword","value":"Simplified models"},"provenance":null},{"subject":{"scheme":"keyword","value":"Natural Sciences"},"provenance":null}],"mainTitle":"Investigating the mechanisms of flood susceptibility with the use of multi-basin machine learning models in data-scarce environments in Cyprus","subTitle":null,"descriptions":["ABSTRACT Study region: The island of Cyprus is dominated by small-scale watersheds that favor the occurrence of flash floods. Climate projections indicate the increase in frequency and intensity of these events. Study focus: The development of rapid flood screening tools is essential for better urban planning. This study uses four different machine learning algorithms, namely support vector machine (SVM), extreme gradient boosting (XGBoost), random forest (RF), and multilayer perceptron (MLP), to build models based on data collected from eight watersheds to enhance their withinregion (Cyprus) generalization. Seven features were selected for tuning and testing the performance of these models. T-based confidence intervals were calculated to quantify uncertainty. New hydrological insights for the region: All models achieved good agreement with the inventory database. RF model was selected to build multi-level susceptibility maps. Half of the Georskipou watershed is classified as highly susceptible to flooding, mostly urban and semi-urban regions, whereas 38 % of the test watershed is not expected to experience severe flood events. Simplified RF models were developed by selecting different combinations of the most important features, revealing that land-use, terrain slope, terrain elevation, and flow accumulation are sufficient to achieve good accuracy (95 %) with flood inventory data. The results highlight the ability of simple, computationally efficient data-driven models to provide rapid predictions, thus avoiding the compilation of fully detailed physically-based models.","Study region: The island of Cyprus is dominated by small-scale watersheds that favor the occurrence of flash floods. Climate projections indicate the increase in frequency and intensity of these events. Study focus: The development of rapid flood screening tools is essential for better urban planning. This study uses four different machine learning algorithms, namely support vector machine (SVM), extreme gradient boosting (XGBoost), random forest (RF), and multilayer perceptron (MLP), to build models based on data collected from eight watersheds to enhance their withinregion (Cyprus) generalization. Seven features were selected for tuning and testing the performance of these models. T-based confidence intervals were calculated to quantify uncertainty. New hydrological insights for the region: All models achieved good agreement with the inventory database. RF model was selected to build multi-level susceptibility maps. Half of the Georskipou watershed is classified as highly susceptible to flooding, mostly urban and semi-urban regions, whereas 38 % of the test watershed is not expected to experience severe flood events. Simplified RF models were developed by selecting different combinations of the most important features, revealing that land-use, terrain slope, terrain elevation, and flow accumulation are sufficient to achieve good accuracy (95 %) with flood inventory data. The results highlight the ability of simple, computationally efficient data-driven models to provide rapid predictions, thus avoiding the compilation of fully detailed physically-based models","The present work was conducted in the framework of AI-OBSERVER project (https://ai-observer.eu/) titled “Enhancing Earth Observation capabilities of the Eratosthenes Centre of Excellence on Disaster Risk Reduction through Artificial Intelligence”, that has received funding from the European Union’s Horizon Europe Framework Programme HORIZON-WIDERA-2021-ACCESS-03 (Twinning) under the Grant Agreement No. 101079468. The authors also acknowledge the ’EXCELSIOR’: ERATOSTHENES: Excellence Research Centre for Earth Surveillance and Space-Based Monitoring of the Environment H2020 Widespread Teaming project (www. excelsior2020.eu). The ’EXCELSIOR’ project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 857510, from the Government of the Republic of Cyprus through the Directorate General for the European Programmes, Coordination and Development and the Cyprus University of Technology"],"publicationDate":"2026-02-01","publisher":"Elsevier BV","embargoEndDate":null,"sources":["Crossref","Journal of Hydrology: Regional Studies"],"formats":["PDF"],"contributors":null,"coverages":null,"bestAccessRight":{"code":"c_abf2","label":"OPEN","scheme":"http://vocabularies.coar-repositories.org/documentation/access_rights/"},"container":{"name":"Journal of Hydrology: Regional Studies","issnPrinted":"2214-5818","issnOnline":null,"issnLinking":null,"ep":null,"iss":null,"sp":"103075","vol":"63","edition":null,"conferencePlace":null,"conferenceDate":null},"documentationUrls":null,"codeRepositoryUrl":null,"programmingLanguage":null,"contactPeople":null,"contactGroups":null,"tools":null,"size":null,"version":null,"geoLocations":null,"id":"doi_dedup___::fa3da28dc334e4838d5a2fe7e9811918","originalIds":["S2214581825009048","10.1016/j.ejrh.2025.103075","50|doiboost____|fa3da28dc334e4838d5a2fe7e9811918","oai:ktisis.cut.ac.cy:20.500.14279/36225","50|od______1540::e04547e0c9a01929cab195ec48dd6683","oai:art.torvergata.it:2108/458524","50|od______3667::e772b01e636e633f01b3cadf32489a95","10.1016/J.EJRH.2025.103075","50|r3c4b2081b22::e2640c63d9295026a64f8b98ca3e2adb","50|sygma_______::fa3da28dc334e4838d5a2fe7e9811918"],"pids":[{"scheme":"doi","value":"10.1016/j.ejrh.2025.103075"},{"scheme":"handle","value":"20.500.14279/36225"},{"scheme":"handle","value":"2108/458524"}],"dateOfCollection":null,"lastUpdateTimeStamp":null,"indicators":{"citationImpact":{"citationCount":1.0,"influence":2.233602E-9,"popularity":3.368279E-9,"impulse":1.0,"citationClass":"C5","influenceClass":"C5","impulseClass":"C5","popularityClass":"C4"}},"projects":[{"id":"corda_____he::c50d767ad9bbb91868a17bc2bf7ef3a6","code":"101079468","acronym":"AI-OBSERVER","title":"Enhancing Earth Observation capabilities of the Eratosthenes Centre of Excellence on Disaster Risk Reduction through Artificial Intelligence","funder":"European Commission","pids":[{"scheme":"doi","value":"10.3030/101079468"}]}],"organizations":[{"legalName":"ERATOSTHENES CENTRE OF EXCELLENCE","acronym":"ERATOSTHENES CENTRE OF EXCELLENCE","id":"openorgs____::e9978b4b995e0d51f7a671f954656105","pids":[{"scheme":"PIC","value":"894252701"},{"scheme":"ISNI","value":"0000000509752978"},{"scheme":"ROR","value":"https://ror.org/03fh6pa75"}]},{"legalName":"Cyprus University of Technology","acronym":"CUT","id":"openorgs____::526468206bca24c1c90da6a312295cf4","pids":[{"scheme":"ROR","value":"https://ror.org/05qt8tf94"},{"scheme":"OrgReg","value":"CY0003"},{"scheme":"mag_id","value":"163151358"},{"scheme":"fundref","value":"100008543"},{"scheme":"FundRef","value":"100008543"},{"scheme":"wikidata","value":"Q1518320"},{"scheme":"Wikidata","value":"Q1518320"},{"scheme":"GRID","value":"grid.15810.3d"},{"scheme":"ISNI","value":"0000000099953899"},{"scheme":"OrgRef","value":"8090545"},{"scheme":"PIC","value":"999597223"}]},{"legalName":"University of Rome Tor Vergata","acronym":"University of Rome Tor Vergata","id":"openorgs____::ef75c0c233bf4c38dbf5af407109c813","pids":[{"scheme":"FundRef","value":"501100007642"},{"scheme":"wikidata","value":"Q1031803"},{"scheme":"fundref","value":"501100007642"},{"scheme":"mag_id","value":"116067653"},{"scheme":"OrgRef","value":"2097467"},{"scheme":"ISNI","value":"0000000123000941"},{"scheme":"OrgReg","value":"IT0070"},{"scheme":"ROR","value":"https://ror.org/02p77k626"},{"scheme":"Wikidata","value":"Q1031803"},{"scheme":"PIC","value":"999844864"},{"scheme":"RRID","value":"RRID:SCR_007751"},{"scheme":"GRID","value":"grid.6530.0"}]}],"communities":[{"code":"eut","label":"EUt+","provenance":null}],"collectedFrom":[{"key":"openaire____::a8db6f6b2ce4fe72e8b2314a9a93e7d9","value":"Sygma"},{"key":"openaire____::081b82f96300b6a6e3d282bad31cb6e2","value":"Crossref"},{"key":"opendoar____::cda72177eba360ff16b7f836e2754370","value":"Ktisis"},{"key":"opendoar____::f095cedd23b99f1696fc8caecbcf257e","value":"Archivio della Ricerca - 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