Integrating Machine Learning and Subsurface Characterization for Improved Urban Flood Modelling and Parameter Optimization

References (31)

1. 1 Support Vector Machines for Classification [DOI]
2. 2 Comparison of 1D/1D and 1D/2D Coupled (Sewer/Surface) Hydraulic Models for Urban Flood Simulation [DOI]
3. 3 Flood Risk in Urban Areas: Modelling, Management and Adaptation to Climate Change. A Review [DOI]
4. 4 A review of the current status of flood modelling for urban flood risk management in the developing countries [DOI]
5. 5 Sensitivity analysis approaches in multi-criteria decision analysis: A systematic review [DOI]
6. 6 Sensitivity analysis in optimization and reliability problems [DOI]
7. 7 The influence of grid shape and grid size on hydraulic river modelling performance [DOI]
8. 8 Flood Prediction and Uncertainty Estimation Using Deep Learning [DOI]
9. 9 flood forecasting based on machine learning pattern recognition and dynamic migration of parameters [DOI]
10. 10 Urban storm flood simulation using improved SWMM based on K‐means clustering of parameter samples [DOI]
11. 11 Machine learning applications in flood forecasting and predictions, challenges, and way-out in the perspective of changing environment [DOI]
12. 12 Optimization Algorithms and Their Applications and Prospects in Manufacturing Engineering [DOI]
13. 13 Improvement of hydrological model calibration by selecting multiple parameter ranges [DOI]
14. 14 Study on the Classification of Urban Waterlogging Rainstorms and Rainfall Thresholds in Cities Lacking Actual Data [DOI]
15. 15 Using the present to estimate the future: A simplified approach for the quantification of climate change effects on urban flooding by scenario analysis [DOI]
16. 16 Modelling Waterlogging Impacts on Crop Growth: A Review of Aeration Stress Definition in Crop Models and Sensitivity Analysis of APSIM [DOI]
17. 17 Application of machine learning models in assessing the hydrological changes under climate change in the transboundary 3S River Basin [DOI]
18. 18 A Comprehensive Analysis of Sensitivity in Simulation Models for Enhanced System Understanding and Optimisation [DOI]
19. 19 Integration of an improved transformer with physical models for the spatiotemporal simulation of urban flooding depths [DOI]
20. 20 Parameter Automatic Calibration Approach for Neural-Network-Based Cyclonic Precipitation Forecast Models [DOI]
21. 21 Investigation and simulation of flood inundation hazard in urban areas in Iran [DOI]
22. 22 Integrated Optimization of Pipe Routing and Clamp Layout for Aeroengine Using Improved MOALO [DOI]
23. 23 An intelligent framework for spatiotemporal simulation of flooding considering urban underlying surface characteristics [DOI]
24. 24 Counterfactual analysis of extreme events in urban flooding scenarios [DOI]
25. 25 A New Urban Waterlogging Simulation Method Based on Multi-Factor Correlation [DOI]
26. 26 Simulating Flood Urban Drainage Networks through 1D/2D Model Analysis [DOI]
27. 27 An automatic parameter calibration method for the SWAT model in runoff simulation [DOI]
28. 28 Finite volume simulation of unsteady water pipe flow [DOI]
29. 29 The Description Length of Deep Learning Models [DOI]
30. 30 Water Resource Management: Hydrological Modelling, Hydrological Cycles, and Hydrological Prediction [DOI]
31. 31 Application of Geophysical Methods for Hydrogeology [DOI]

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