112 citations as recorded by crossref.

1. A rainfall threshold‐based approach to early warnings in urban data‐scarce regions: A case study of pluvial flooding in Alexandria, Egypt A. Young et al. 10.1111/jfr3.12702
2. Citizen science in hydrological monitoring and ecosystem services management: State of the art and future prospects N. Njue et al. 10.1016/j.scitotenv.2019.07.337
3. Integrating Community Science Research and Space‐Time Mapping to Determine Depth to Groundwater in a Remote Rural Region A. Gómez et al. 10.1029/2020WR029519
4. Automatic water-level class estimation from repeated crowd-based photos of streams Z. Wang et al. 10.1080/02626667.2023.2240312
5. Enhancing Building Services in Higher Education Campuses through Participatory Science M. Itair et al. 10.3390/buildings14092784
6. Flood modelling using synthesised citizen science urban streamflow observations M. Fava et al. 10.1111/jfr3.12498
7. Recent Advances and New Frontiers in Riverine and Coastal Flood Modeling K. Jafarzadegan et al. 10.1029/2022RG000788
8. Extraction and analysis of natural disaster-related VGI from social media: review, opportunities and challenges Y. Feng et al. 10.1080/13658816.2022.2048835
9. How can flood modelling advance in the “big data” age? S. Maskey 10.1111/jfr3.12560
10. Integrated community-based approaches to urban pluvial flooding research, trends and future directions: A review K. Azizi et al. 10.1016/j.uclim.2022.101237
11. The Critical Importance of Citizen Science Data A. de Sherbinin et al. 10.3389/fclim.2021.650760
12. A method to reconstruct flood scenarios using field interviews and hydrodynamic modelling: application to the 2017 Suleja and Tafa, Nigeria flood M. Malgwi et al. 10.1007/s11069-021-04756-z
13. Investigating pedestrian behavioral patterns under different floodwater conditions: A video analysis on real flood evacuations E. Quagliarini et al. 10.1016/j.ssci.2023.106083
14. Analysis of Mumbai floods in recent years with crowdsourced data S. Tripathy et al. 10.1016/j.uclim.2024.101815
15. The value of citizen science for flood risk reduction: cost–benefit analysis of a citizen observatory in the Brenta-Bacchiglione catchment M. Ferri et al. 10.5194/hess-24-5781-2020
16. The evaluation method of college teachers’ morality considering intelligent emotion recognition and data mining algorithm X. Hu 10.1080/08839514.2023.2186413
17. Capturing Flood Risk Perception via Sketch Maps C. Klonner et al. 10.3390/ijgi7090359
18. State of continental discharge estimation and modelling: challenges and opportunities for Africa K. Akpoti et al. 10.1080/02626667.2024.2402938
19. Flood Hazard Mapping and Flood Preparedness Literacy of the Elderly Population Residing in Bangkok, Thailand C. Sawangnate et al. 10.3390/w14081268
20. Supporting participative pre‐flood risk reduction in a UNESCO biosphere M. O'Grady et al. 10.1111/jfr3.12520
21. The Top-Viewed Cryosphere Videos on YouTube: An Overview A. Emmer et al. 10.3390/geosciences9040181
22. Geoinformation Harvesting From Social Media Data: A community remote sensing approach X. Zhu et al. 10.1109/MGRS.2022.3219584
23. Physics-Guided Deep Learning for Spatiotemporal Evolution of Urban Pluvial Flooding H. Woo et al. 10.3390/w17081239
24. Social and geographical disparities in Twitter use during Hurricane Harvey L. Zou et al. 10.1080/17538947.2018.1545878
25. Citizens’ Campaigns for Environmental Water Monitoring: Lessons From Field Experiments T. Assumpcao et al. 10.1109/ACCESS.2019.2939471
26. Citizen Science and the Sustainable Development Goals: Building Social and Technical Capacity through Data Collection in the Upper Blue Nile Basin, Ethiopia G. Rigler et al. 10.3390/su14063647
27. Pluvial flood risk and opportunities for resilience B. Rosenzweig et al. 10.1002/wat2.1302
28. A Mixed-Methods Investigation into Barriers for Sharing Geospatial and Resilience Flood Data in the UK L. Waterman et al. 10.3390/w13091235
29. Evaluation of factors affecting the quality of citizen science rainfall data in Akaki catchment, Addis Ababa, Ethiopia H. Tedla et al. 10.1016/j.jhydrol.2022.128284
30. A massive rock and ice avalanche caused the 2021 disaster at Chamoli, Indian Himalaya D. Shugar et al. 10.1126/science.abh4455
31. Introduction to section 2 X. Cai & Z. Zheng 10.1080/02508060.2024.2343178
32. Filling data gaps using citizen science for flood modeling in urbanized catchment of Akaki A. Alemu et al. 10.1016/j.nhres.2023.05.002
33. Assessment of the Susceptibility of Urban Flooding Using GIS with an Analytical Hierarchy Process in Hanoi, Vietnam H. Nguyen et al. 10.3390/su16103934
34. Integrating Qualitative Flow Observations in a Lumped Hydrologic Routing Model M. Mazzoleni et al. 10.1029/2018WR023768
35. Can the Quality of the Potential Flood Risk Maps be Evaluated? A Case Study of the Social Risks of Floods in Central Spain J. Garrote et al. 10.3390/w11061284
36. Citizen Scientists Contribute to Real-Time Monitoring of Lake Water Quality Using 3D Printed Mini Secchi Disks G. George et al. 10.3389/frwa.2021.662142
37. Leveraging catchment scale automated novel data collection infrastructure to advance urban hydrologic-hydraulic modeling A. Shrestha et al. 10.1016/j.envsoft.2024.106046
38. Rapid high-resolution impact-based flood early warning is possible with RIM2D: a showcase for the 2023 pluvial flood in Braunschweig S. Khosh Bin Ghomash et al. 10.5194/nhess-25-1737-2025
39. AI-Driven Climate Modeling: Validation and Uncertainty Mapping – Methodologies and Challenges F. Kayusi et al. 10.62486/latia2025332
40. Estimating the likelihood of roadway pluvial flood based on crowdsourced traffic data and depression-based DEM analysis A. Safaei-Moghadam et al. 10.5194/nhess-23-1-2023
41. Rapid Mapping for Simple Flood Mitigation Using Commercial Drone at Way Galih Village, Lampung, Indonesia A. Rohman & D. Prasetya 10.23917/forgeo.v33i1.8421
42. Investigating Pedestrian Behavioral Patterns Under Different Floodwater Conditions: A Video Analysis on Real Flood Evacuations E. Quagliarini et al. 10.2139/ssrn.4167684
43. Predictive multi-watershed flood monitoring using deep learning on integrated physical and social sensors data S. Dong et al. 10.1177/23998083211069140
44. Utilization of social media in floods assessment using data mining techniques Q. Khan et al. 10.1371/journal.pone.0267079
45. Opportunities for crowdsourcing in urban flood monitoring A. Helmrich et al. 10.1016/j.envsoft.2021.105124
46. Quantifying Flood Water Levels Using Image-Based Volunteered Geographic Information Y. Lin et al. 10.3390/rs12040706
47. Value of quality controlled citizen science data for rainfall-runoff characterization in a rapidly urbanizing catchment G. Kebede Mengistie et al. 10.1016/j.jhydrol.2024.130639
48. Generative deep learning for data generation in natural hazard analysis: motivations, advances, challenges, and opportunities Z. Ma et al. 10.1007/s10462-024-10764-9
49. An automatic quality evaluation procedure for third-party daily rainfall observations and its application over Australia M. Li et al. 10.1007/s00477-023-02401-8
50. Exploring the Emotional Factors in College English Classroom Teaching Based on Computer-Aided Model F. Fu & Q. Li 10.1155/2022/4383254
51. CNS: Application of Distributed Indexing Based on Large-Scale Graph Data in Legal Protection of Network Personal Information P. Han 10.1142/S0218843021500039
52. Urban hydrological model (UHM) developed for an urban flash flood simulation and analysis of the flood intensity sensitivity to urbanization H. Hu et al. 10.1080/19475705.2024.2302561
53. An integrated socio-environmental framework for mapping hazard-specific vulnerability and exposure in urban areas P. Alves et al. 10.1080/1573062X.2021.1913505
54. Computational socioeconomics J. Gao et al. 10.1016/j.physrep.2019.05.002
55. Flood Risk in Urban Areas: Modelling, Management and Adaptation to Climate Change. A Review L. Cea & P. Costabile 10.3390/hydrology9030050
56. Flood severity mapping from Volunteered Geographic Information by interpreting water level from images containing people: A case study of Hurricane Harvey Y. Feng et al. 10.1016/j.isprsjprs.2020.09.011
57. Stochastic Modeling for Estimating Real-Time Inundation Depths at Roadside IoT Sensors Using the ANN-Derived Model S. Wu et al. 10.3390/w13213128
58. Anomalous human activity fluctuations from digital trace data signal flood inundation status H. Farahmand et al. 10.1177/23998083211069990
59. Participatory Mapping and Visualization of Local Knowledge: An Example from Eberbach, Germany C. Klonner et al. 10.1007/s13753-020-00312-8
60. Role of Artificial Intelligence in Disseminating Climate Information Services in Africa P. Chavula & F. Kayusi 10.62486/latia202576
61. Reconstituting past flood events: the contribution of citizen science B. Sy et al. 10.5194/hess-24-61-2020
62. Citizens AND HYdrology (CANDHY): conceptualizing a transdisciplinary framework for citizen science addressing hydrological challenges F. Nardi et al. 10.1080/02626667.2020.1849707
63. Opportunities and risks of disaster data from social media: a systematic review of incident information M. Wiegmann et al. 10.5194/nhess-21-1431-2021
64. GFPLAIN and Multi-Source Data Assimilation Modeling: Conceptualization of a Flood Forecasting Framework Supported by Hydrogeomorphic Floodplain Rapid Mapping A. Annis & F. Nardi 10.3390/hydrology8040143
65. Citizen science-based WEAP modeling of water use impact on environmental flow in urbanized Big Akaki catchment, Ethiopia G. Mengistie et al. 10.1016/j.ejrh.2025.102435
66. A Review of Citizen Science and Crowdsourcing in Applications of Pluvial Flooding L. See 10.3389/feart.2019.00044
67. A Probabilistic, Parcel‐Level Inundation Prediction Tool for Medium‐Range Flood Forecasting in Large Lake Systems K. Semmendinger et al. 10.1111/1752-1688.12893
68. Hydrologic–hydraulic assessment of SUDS control capacity using different modeling approaches: a case study in Bogotá, Colombia A. Ortega Sandoval et al. 10.2166/wst.2023.173
69. Blue and grey urban water footprints through citizens’ perception and time series analysis of Brazilian dynamics F. Souza et al. 10.1080/02626667.2021.1879388
70. Utilising social media data to evaluate urban flood impact in data scarce cities K. Guo et al. 10.1016/j.ijdrr.2023.103780
71. Success factors for citizen science projects in water quality monitoring A. San Llorente Capdevila et al. 10.1016/j.scitotenv.2020.137843
72. Citizen science flow – an assessment of simple streamflow measurement methods J. Davids et al. 10.5194/hess-23-1045-2019
73. Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions F. Zheng et al. 10.1029/2018RG000616
74. The Techno-politics of Crowdsourced Disaster Data in the Smart City E. Wolff & F. Muñoz 10.3389/frsc.2021.695329
75. Floodplain reconstitution based on data collected via smartphones: a methodological approach to hydrological risk mapping A. Ribeiro et al. 10.1590/2318-0331.252020190179
76. Flood resilience through crowdsourced rainfall data collection: Growing engagement faces non-uniform spatial adoption A. Chen et al. 10.1016/j.jhydrol.2022.127724
77. Evaluating terrain-based HAND-SRC flood mapping model in low-relief rural plains using high resolution topography and crowdsourced data H. Sabeh et al. 10.1016/j.jhydrol.2024.132649
78. Analysing the value of environmental citizen-generated data: Complementarity and cost per observation L. Alfonso et al. 10.1016/j.jenvman.2021.114157
79. Testing the theoretical principles of citizen science in monitoring stream water levels through photo-trap frames A. Spasiano et al. 10.3389/frwa.2023.1050378
80. Participatory Framework for Urban Pluvial Flood Modeling in the Digital Twin Era S. Park et al. 10.1016/j.scs.2024.105496
81. Improving Hydrological Models With the Assimilation of Crowdsourced Data P. Avellaneda et al. 10.1029/2019WR026325
82. From Extreme Weather Events to ‘Cascading Vulnerabilities’: Participatory Flood Research Methodologies in Brazil During COVID-19 N. Calvillo et al. 10.1142/S2345737622410020
83. Terrestrial and airborne laser scanning and 2-D modelling for 3-D flood hazard maps in urban areas: new opportunities and perspectives P. Costabile et al. 10.1016/j.envsoft.2020.104889
84. Identification of flood depth levels in urban waterlogging disaster caused by rainstorm using a CBAM-improved ResNet50 L. Wu et al. 10.1016/j.eswa.2024.124382
85. The utility of using Volunteered Geographic Information (VGI) for evaluating pluvial flood models M. Drews et al. 10.1016/j.scitotenv.2023.164962
86. Threshold-based flood early warning in an urbanizing catchment through multi-source data integration: Satellite and citizen science contribution H. Tedla et al. 10.1016/j.jhydrol.2024.131076
87. The citizen-led information practices of ICT4D in rural communities of China: A mixed-method study L. Ye et al. 10.1016/j.ijinfomgt.2020.102248
88. A simple framework for calibrating hydraulic flood inundation models using Crowd-sourced water levels A. Dasgupta et al. 10.1016/j.jhydrol.2022.128467
89. Experimental and Numerical Study of the Effect of Model Geometric Distortion on Laboratory Modeling of Urban Flooding X. Li et al. 10.1029/2021WR029666
90. Multiscale Observation Product (MOP) for Temporal Flood Inundation Mapping of the 2015 Dallas Texas Flood E. Sava et al. 10.3390/rs15061615
91. Collaborating With Communities: Citizen Science Flood Monitoring in Urban Informal Settlements E. Wolff et al. 10.17645/up.v6i4.4648
92. Using social cartographies for the calibration of two-dimensional hydraulic flood models B. Alarcón et al. 10.1007/s11069-024-06838-0
93. Flood data platform governance: Identifying the technological and socio-technical approach(es) differences M. Fadmastuti et al. 10.1016/j.envsci.2024.103938
94. The benefits and negative impacts of citizen science applications to water as experienced by participants and communities D. Walker et al. 10.1002/wat2.1488
95. Modelling support to citizen observatories for strategic Danube Delta planning: Sontea-Fortuna case study A. Venturini et al. 10.1080/09640568.2018.1523787
96. A Nationwide Flood Forecasting System for Saudi Arabia: Insights from the Jeddah 2022 Event G. Sofia et al. 10.3390/w16141939
97. High‐resolution urban flood model for risk mitigation validated with records collected by the affected community M. Re et al. 10.1111/jfr3.12524
98. Analysis of Dynamic Influence Mechanism of Network Public Opinion Based on Simulation Feature Extraction Y. Zhang & Z. Cai 10.1155/2022/8423643
99. Floodability: A New Paradigm for Designing Urban Drainage and Achieving Sustainable Urban Growth G. La Loggia et al. 10.1007/s11269-020-02620-6
100. Integrating VGI and 2D hydraulic models into a data assimilation framework for real time flood forecasting and mapping A. Annis & F. Nardi 10.1080/10095020.2019.1626135
101. Invited perspectives: How machine learning will change flood risk and impact assessment D. Wagenaar et al. 10.5194/nhess-20-1149-2020
102. The potential of proxy water level measurements for calibrating urban pluvial flood models M. Moy de Vitry & J. Leitão 10.1016/j.watres.2020.115669
103. The Role of Realistic Channel Geometry Representation in Hydrological Model Predictions J. Brackins et al. 10.1111/1752-1688.12865
104. Free Global DEMs and Flood Modelling—A Comparison Analysis for the January 2015 Flooding Event in Mocuba City (Mozambique) J. Garrote 10.3390/w14020176
105. Propagation of radar rainfall uncertainties into urban pluvial flood modeling during the North American monsoon A. Hjelmstad et al. 10.1080/02626667.2021.1980216
106. Crowdsourcing and interactive modelling for urban flood management E. Gebremedhin et al. 10.1111/jfr3.12602
107. Exploring an Innovative Moral Education Cultivation Model in Higher Education through Neural Network Perspective: A Preliminary Study X. Shi 10.1080/08839514.2023.2214767
108. The promise of a “people-centred” approach to floods: Types of participation in the global literature of citizen science and community-based flood risk reduction in the context of the Sendai Framework E. Wolff 10.1016/j.pdisas.2021.100171
109. Temporary flood marks proposal: What we learned after losing the baroque artifact from Cracow, Poland R. Szczepanek et al. 10.1016/j.ijdrr.2022.102942
110. Assessment of crowdsourced social media data and numerical modelling as complementary tools for urban flood mitigation M. Re et al. 10.1080/02626667.2022.2075266
111. Exploring the influence of citizen involvement on the assimilation of crowdsourced observations: a modelling study based on the 2013 flood event in the Bacchiglione catchment (Italy) M. Mazzoleni et al. 10.5194/hess-22-391-2018
112. Risk-Based Early Warning System for Pluvial Flash Floods: Approaches and Foundations J. Hofmann & H. Schüttrumpf 10.3390/geosciences9030127