{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T23:14:34Z","timestamp":1774998874271,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,5,6]],"date-time":"2023-05-06T00:00:00Z","timestamp":1683331200000},"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>Identifying floods and flood susceptibility mapping are critical for decision-makers and disaster management. Machine learning and deep learning have emerged as powerful tools for flood prevention, whereas they confront the drawbacks of overfitting and biased prediction due to the difficulty in obtaining real data. Therefore, this study presents a novel approach for flood susceptibility prediction by integrating ResNet-18 with a 2D hydrological model for global flood susceptibility mapping using remote sensing datasets. The three main contributions of this study are outlined below. First, a new perspective integrating hydrological simulation and deep learning is presented to overcome the inherent drawbacks of deep learning. Second, the model performance is improved through physics-based initialization. Third, the pretrained model achieves better performance than the original model with incomplete training labels. This experiment demonstrates that the physics-based initialized ResNet-18 model achieves satisfactory prediction performance in terms of accuracy and area under the receiver operating characteristic (ROC) curve (0.854 and 0.932, respectively) and is extremely robust according to a sensitivity analysis.<\/jats:p>","DOI":"10.3390\/rs15092447","type":"journal-article","created":{"date-parts":[[2023,5,8]],"date-time":"2023-05-08T02:03:31Z","timestamp":1683511411000},"page":"2447","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["A Residual Neural Network Integrated with a Hydrological Model for Global Flood Susceptibility Mapping Based on Remote Sensing Datasets"],"prefix":"10.3390","volume":"15","author":[{"given":"Junfei","family":"Liu","sequence":"first","affiliation":[{"name":"School of National Safety and Emergency Management, Beijing Normal University, Beijing 100875, China"},{"name":"Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7687-7824","authenticated-orcid":false,"given":"Kai","family":"Liu","sequence":"additional","affiliation":[{"name":"School of National Safety and Emergency Management, Beijing Normal University, Beijing 100875, China"}]},{"given":"Ming","family":"Wang","sequence":"additional","affiliation":[{"name":"School of National Safety and Emergency Management, Beijing Normal University, Beijing 100875, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"074014","DOI":"10.1088\/1748-9326\/aacc76","article-title":"Flood damage costs under the sea level rise with warming of 1.5 \u00b0C and 2 \u00b0C","volume":"13","author":"Jevrejeva","year":"2018","journal-title":"Environ. Res. Lett."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3527","DOI":"10.1038\/s41467-022-30727-4","article-title":"Flood exposure and poverty in 188 countries","volume":"13","author":"Rentschler","year":"2022","journal-title":"Nat. Commun."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1109\/JSTARS.2013.2284607","article-title":"Near Real-Time Flood Volume Estimation From MODIS Time-Series Imagery in the Indus River Basin","volume":"7","author":"Kwak","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_4","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":"2018","journal-title":"Geocarto Int."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Swain, K.C., Singha, C., and Nayak, L. (2020). Flood Susceptibility Mapping through the GIS-AHP Technique Using the Cloud. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9120720"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"126846","DOI":"10.1016\/j.jhydrol.2021.126846","article-title":"Flood hazard mapping methods: A review","volume":"603","author":"Mudashiru","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_7","first-page":"25","article-title":"Ways for flood hazard mapping in urbanised environments: A short","volume":"4","author":"Bellos","year":"2012","journal-title":"Water Util. J."},{"key":"ref_8","first-page":"293","article-title":"Chapter 11: Prediction and modeling of flood hydrology and hydraulics","volume":"Volume 498","author":"Wohl","year":"2000","journal-title":"Inland Flood Hazards: Human, Riparian and Aquatic Communities"},{"key":"ref_9","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_10","first-page":"221","article-title":"One- and Two-Dimensional Hydrological Modelling and Their Uncertainties","volume":"11","author":"Anees","year":"2017","journal-title":"Flood Risk Manag."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"744","DOI":"10.1016\/j.scitotenv.2018.01.266","article-title":"A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran","volume":"627","author":"Khosravi","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.catena.2014.10.017","article-title":"Flood susceptibility assessment using GIS-based support vector machine model with different kernel types","volume":"125","author":"Tehrany","year":"2015","journal-title":"CATENA"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.ijsrc.2016.02.002","article-title":"Coupling the k-nearest neighbor procedure with the Kalman filter for real-time updating of the hydraulic model in flood forecasting","volume":"31","author":"Liu","year":"2016","journal-title":"Int. J. Sediment Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.jhydrol.2013.08.018","article-title":"Improvement of watershed flood forecasting by typhoon rainfall climate model with an ANN-based southwest monsoon rainfall enhancement","volume":"506","author":"Pan","year":"2013","journal-title":"J. Hydrol."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"La Salandra, M., Colacicco, R., Dellino, P., and Capolongo, D. (2023). An Effective Approach for Automatic River Features Extraction Using High-Resolution UAV Imagery. Drones, 7.","DOI":"10.3390\/drones7020070"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"135161","DOI":"10.1016\/j.scitotenv.2019.135161","article-title":"Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: Application of the simulated annealing feature selection method","volume":"711","author":"Hosseini","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1016\/j.scitotenv.2017.09.262","article-title":"Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms","volume":"615","author":"Kornejady","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"124379","DOI":"10.1016\/j.jhydrol.2019.124379","article-title":"Verification of novel integrations of swarm intelligence algorithms into deep learning neural network for flood susceptibility mapping","volume":"581","author":"Bui","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"17013","DOI":"10.3390\/rs71215871","article-title":"Preface: Remote sensing in flood monitoring and management","volume":"7","author":"Schumann","year":"2015","journal-title":"Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Mosavi, A., Ozturk, P., and Chau, K.-w. (2018). Flood Prediction Using Machine Learning Models: Literature Review. Water, 10.","DOI":"10.20944\/preprints201810.0098.v2"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"13393","DOI":"10.1007\/s00500-019-03878-8","article-title":"Analysis of remote sensing imagery for disaster assessment using deep learning: A case study of flooding event","volume":"23","author":"Yang","year":"2019","journal-title":"Soft Comput."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Liu, J., Wang, J., Xiong, J., Cheng, W., Sun, H., Yong, Z., and Wang, N. (2021). Hybrid Models Incorporating Bivariate Statistics and Machine Learning Methods for Flash Flood Susceptibility Assessment Based on Remote Sensing Datasets. Remote Sens., 13.","DOI":"10.3390\/rs13234945"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"127255","DOI":"10.1016\/j.jhydrol.2021.127255","article-title":"Short-term flood probability density forecasting using a conceptual hydrological model with machine learning techniques","volume":"604","author":"Zhou","year":"2022","journal-title":"J. Hydrol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"114027","DOI":"10.1088\/1748-9326\/ab4d5e","article-title":"Evaluation and machine learning improvement of global hydrological model-based flood simulations","volume":"14","author":"Yang","year":"2019","journal-title":"Environ. Res. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"265","DOI":"10.5194\/hess-26-265-2022","article-title":"Ensemble streamflow forecasting over a cascade reservoir catchment with integrated hydrometeorological modeling and machine learning","volume":"26","author":"Liu","year":"2022","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"126","DOI":"10.2166\/hydro.2022.114","article-title":"Machine learning for postprocessing ensemble streamflow forecasts","volume":"25","author":"Sharma","year":"2023","journal-title":"J. Hydroinform."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Jia, X., Zwart, J., Sadler, J., Appling, A., Oliver, S., Markstrom, S., Willard, J., Xu, S., Steinbach, M., and Read, J. (May, January 29). Physics-guided recurrent graph model for predicting flow and temperature in river networks. Proceedings of the 2021 SIAM International Conference on Data Mining (SDM), Virtual Event.","DOI":"10.1137\/1.9781611976700.69"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1186\/s40537-016-0043-6","article-title":"A survey of transfer learning","volume":"3","author":"Weiss","year":"2016","journal-title":"J. Big Data"},{"key":"ref_31","first-page":"4765","article-title":"A unified approach to interpreting model predictions","volume":"30","author":"Lundberg","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1002\/(SICI)1099-1085(199805)12:6<905::AID-HYP662>3.0.CO;2-2","article-title":"Modelling runoff and sediment transport in catchments using GIS","volume":"12","year":"1998","journal-title":"Hydrol. Process."},{"key":"ref_33","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_34","doi-asserted-by":"crossref","first-page":"864","DOI":"10.1016\/j.jhydrol.2019.05.089","article-title":"Flood susceptibility modelling using novel hybrid approach of reduced-error pruning trees with bagging and random subspace ensembles","volume":"575","author":"Chen","year":"2019","journal-title":"J. Hydrol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.catena.2018.12.011","article-title":"Identifying the essential flood conditioning factors for flood prone area mapping using machine learning techniques","volume":"175","author":"Tehrany","year":"2019","journal-title":"Catena"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"125734","DOI":"10.1016\/j.jhydrol.2020.125734","article-title":"Predicting flood susceptibility using LSTM neural networks","volume":"594","author":"Fang","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1080\/17538947.2011.596578","article-title":"Effects of LIDAR DEM resolution in hydrodynamic modelling: Model sensitivity for cross-sections","volume":"6","author":"Unucka","year":"2013","journal-title":"Int. J. Digit. Earth"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"156736","DOI":"10.1016\/j.scitotenv.2022.156736","article-title":"Probabilistic hydro-geomorphological hazard assessment based on UAV-derived high-resolution topographic data: The case of Basento river (Southern Italy)","volume":"842","author":"Roseto","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.jhydrol.2005.03.012","article-title":"Identifiability of distributed floodplain roughness values in flood extent estimation","volume":"314","author":"Werner","year":"2005","journal-title":"J. Hydrol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/S1001-6279(11)60086-3","article-title":"Flash flood sediment transport in a steep sand-bed ephemeral stream","volume":"26","author":"Billi","year":"2011","journal-title":"Int. J. Sediment Res."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1080\/02626667909491834","article-title":"A physically based, variable contributing area model of basin hydrology\/Un mod\u00e8le \u00e0 base physique de zone d\u2019appel variable de l\u2019hydrologie du bassin versant","volume":"24","author":"Beven","year":"1979","journal-title":"Hydrol. Sci. J."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"143785","DOI":"10.1016\/j.scitotenv.2020.143785","article-title":"Topographic Wetness Index calculation guidelines based on measured soil moisture and plant species composition","volume":"757","author":"Macek","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1038\/s41586-021-03695-w","article-title":"Satellite imaging reveals increased proportion of population exposed to floods","volume":"596","author":"Tellman","year":"2021","journal-title":"Nature"},{"key":"ref_44","first-page":"31","article-title":"The Dartmouth Flood Observatory: An electronic research tool and electronic archive for investigations of extreme flood events","volume":"27","author":"Brakenridge","year":"1996","journal-title":"Geosci. Inf. Soc. Proc."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"5349","DOI":"10.1109\/TNNLS.2020.2966319","article-title":"Why resnet works? residuals generalize","volume":"31","author":"He","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A survey on transfer learning","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.advwatres.2016.05.002","article-title":"Development and evaluation of a framework for global flood hazard mapping","volume":"94","author":"Dottori","year":"2016","journal-title":"Adv. Water Resour."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1023\/A:1010920819831","article-title":"A simple generalisation of the area under the ROC curve for multiple class classification problems","volume":"45","author":"Hand","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"E2086","DOI":"10.1175\/BAMS-D-20-0057.1","article-title":"Global Reach-Level 3-Hourly River Flood Reanalysis (1980\u20132019)","volume":"102","author":"Pan","year":"2021","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"115","DOI":"10.5194\/nhess-6-115-2006","article-title":"Landslide hazard assessment in the Collazzone area, Umbria, Central Italy","volume":"6","author":"Guzzetti","year":"2006","journal-title":"Nat. Hazards Earth Syst. Sci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/9\/2447\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:30:32Z","timestamp":1760124632000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/9\/2447"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,6]]},"references-count":50,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["rs15092447"],"URL":"https:\/\/doi.org\/10.3390\/rs15092447","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,6]]}}}