168 citations as recorded by crossref.

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48. Mapping Discourses of Climate Change Adaptation in the United Kingdom M. Cotton & E. Stevens https://doi.org/10.1175/WCAS-D-18-0024.1
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50. A geological disaster risk prediction method using susceptibility and ensemble learning algorithms: direct economic loss prediction D. He et al. https://doi.org/10.1007/s12665-026-12970-w
51. Multi-hazard analysis of flood and tsunamis on the western Mediterranean coast of Turkey C. Yavuz et al. https://doi.org/10.5194/nhess-22-3725-2022
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57. Spatial-economic scenarios to increase resilience to urban flooding M. Lippera et al. https://doi.org/10.1016/j.wroa.2024.100284
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59. Transferability of data-driven models to predict urban pluvial flood water depth in Berlin, Germany O. Seleem et al. https://doi.org/10.5194/nhess-23-809-2023
60. Satellite-based data driven quantification of pluvial floods over Europe under future climatic and socioeconomic changes P. Hosseinzadehtalaei et al. https://doi.org/10.1016/j.scitotenv.2020.137688
61. Spatio-temporal cross-validation to predict pluvial flood events in the Metropolitan City of Venice Z. Marco et al. https://doi.org/10.1016/j.jhydrol.2022.128150
62. An assessment framework for evaluating urban flood vulnerabilities using Night-Time Light data N. Kangana & N. Kankanamge https://doi.org/10.3934/urs.2025003
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70. Predicting flood events in Kathmandu Metropolitan City under climate change and urbanisation S. KC et al. https://doi.org/10.1016/j.jenvman.2020.111894
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72. Fast-Processing DEM-Based Urban and Rural Inundation Scenarios from Point-Source Flood Volumes K. Kyaw et al. https://doi.org/10.3390/su16020875
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77. Continuous subpixel monitoring of urban impervious surface using Landsat time series C. Deng & Z. Zhu https://doi.org/10.1016/j.rse.2018.10.011
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