Physics-Informed Flash Flood Simulation in Mountainous Catchments via an AI-Driven Coupled Framework
Abstract
dataset_code-v2/│├── public_input_data/ # [CORE DATA] Do not rename or move│ ├── 2010-2025_Yuyao_Rain.csv # Long-term rainfall records│ ├── Yuyao_DEM_90m.tif # Digital Elevation Model│ ├── CLCD_2023_Yuyao_90m_WGS84.tif# Land Cover Data│ ├── yuyao_points_wgs84.* # Shapefiles for stations│ └── [Station_Name]-L1/L2.csv # High-freq water level data│├── Figure1/ # Study Area & Context│ ├── Figure1.pptx # Original vector graphics│ └── Yuyao_StudyArea.mpk # GIS map package│├── Figure2/ # Methodology│ └── Figure2.png # Framework Flowchart│├── Figure3/ # Hyperparameter Optimization│ ├── Figure3-code.py # Script to plot TPE optimization history│ ├── trials_params_log.csv # Log of optimization trials│ └── optimization_results.csv # Final Pareto frontier data│├── Figure4/ # Temporal Hydrographs & Benchmarks│ ├── Figure4-Hybrid-Benchmark.py # Full benchmark suite (Physical, Hybrid, HBV, LSTM, XGBoost)│ └── output-benchmark/ # Output directory for predictions and hydrograph plots│├── Figure5/ # Error Analysis│ ├── Figure5_code-plot.py # Script for absolute error boxplots & peak flow scatter│ └── Figure5_Output/ # Output directory for Figure 5│├── Figure6/ # Historical Validation (Comprehensive)│ ├── Figure6_abc_combined.py # Unified script for Timeline, Confusion Matrix & Metrics│ ├── Figure6a_labels.txt # Label configuration for historical events│ └── tp_corrected_yuyao_v3_distance_decay/ # Event filtering source data│├── Figure7/ # SHAP Sensitivity Analysis│ ├── Figure7-code.py # Script for Feature Importance & Beeswarm plots│ └── trials_params_log.csv # Input data for SHAP explainer│├── Figure8/ # Partial Dependence Plots│ ├── Figure8-code.py # Script for marginal effect curves│ └── trials_params_log.csv # Input data│├── Figure9/ # First Flush & Soil Moisture Dynamics│ └── Figure9-code.py # Script for initial saturation analysis│├── FigureS1/ # Supplementary: Data Source Validation│ ├── Lou-2025.pdf # Original reference paper (in Chinese)│ └── FigureS1.jpg # Visual validation dashboard│├── FigureS2/ # Supplementary: Kappa Sensitivity Analysis│ ├── FigureS2-code-plot.py # Script for recession inertia coefficient sweep│ ├── R1_Kappa_Analysis_Report.txt # Quantitative evaluation report│ ├── R1_Kappa_Metrics.csv # Full metrics table for varying kappa│ ├── Figure_R1a_Hydrograph-v2.png # Output hydrograph plots│ └── Figure_R1b_Metrics-v2.png # Output metric curves│├── FigureS3-S4/ # Supplementary: Quantitative Water-Balance Check│ ├── FigureS3-S4-code-plot.py # Script for cumulative mass budget and ML adjustments│ ├── R2_Water_Balance_Report.txt # Quantitative water balance report│ ├── R2_Water_Balance.csv # Volumetric deviation data│ ├── Figure_R2_Cumulative_Budget.png # Output cumulative volume plots│ └── Figure_R2_ML_Adjustment.png # Output net/gross adjustment bar charts│└── requirements.txt # Python dependencies for the entire repository
Fields of Study & Subjects
Hydrological Forecasting Using AI
Flood Risk Assessment and Management
Hydrology and Watershed Management Studies
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 7 February 2026
- Language
- EN