cb-flooddy 0.4.9

A cluster-based temporal attention approach for predicting cyclone-induced compound flood dynamics

Cb_FloodDy

A cluster-based temporal-attention framework with utilities for Voronoi cluster generation and Optuna-driven hyperparameter tuning.

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Highlights

• End-to-end training pipeline built on ConvLSTM + CBAM (channel & spatial attention) with a custom temporal attention layer and cluster-aware spatial modulation.
• Voronoi clustering toolkit to partition a floodplain into station-informed regions, save shapefiles, and produce publication-ready plots.
• Lazy module loading at package import time to keep interactive workflows snappy (heavy modules are only loaded when needed).
• Packaged for PyPI; standard build metadata included.

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Installation

# (Optional) create a clean env
conda create -n cb_flooddy -y
conda activate cb_flooddy

# install from PyPI
pip install Cb-FloodDy

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Model Workflow

1) Voronoi clusters (create station-informed polygons)

from pyproj import CRS
from Cb_FloodDy.voronoi_clusters import run_workflow

artifacts = run_workflow(
    src_crs=CRS.from_epsg(4326),                  # lon/lat
    station_dir="path/to/water_level_stations",   # files like station_1.csv, station_2.csv, ...
    station_range=(1, 21),                        # i.e., 21 stations available, should be set to the available number of stations
    shapefile_path="GBay_cells_polygon.shp",      # domain/flood extent polygon(s)
    combine_pairs=[(1, 19), (12, 21), (3, 18)],   # optional unions
    x_ticks=[-95.5, -95.0, -94.5],                # optional map ticks
    y_ticks=[29.0, 29.4, 29.8],
    out_shapefile="voronoi_clusters.shp",         # optional outputs
    out_fig="voronoi_map.png",
    reorder_by_station=True,                      # ensure polygon i matches station i
)

• Under the hood: station CSVs are parsed (with robust lon/lat detection), a bounded Voronoi tessellation is built and clipped to your floodplain, optional polygons get unioned, and outputs can be saved/visualized.

2) Train the flood-depth model with Optuna

from Cb_FloodDy import bayesian_opt_tuning as bo

summary = bo.run_optimization(
    train_atm_pressure_dir="data/atm_pressure_tifs/",
    train_wind_speed_dir="data/wind_speed_tifs/",
    train_precipitation_dir="data/precip_tifs/",
    train_water_depth_dir="data/water_depth_tifs/",  # y
    train_river_discharge_dir="data/river_discharge_tifs/",
    water_level_dir="data/water_levels_csvs/",
    polygon_clusters_path="voronoi_clusters.shp",                # from step 1
    sequence_length=6,
    n_trials=30,
    study_name="cb_flooddy_study",
    checkpoint_dir_BO="checkpoints/optuna",
    seed_value=3,
    convlstm_filters=[16, 32, 48],                               # search grids/ranges
    lstm_units=[32, 48],
    dense_units=[64, 128],
    l2_reg_range=(1e-7, 1e-4),
    lr_range=(1e-4, 5e-3),
    dropout_range=(0.1, 0.5),
    es_monitor="val_loss",
    early_stopping=10,
    es_restore_best=True,
    epochs=100,
    batch_size=2,
    val_split=0.2,
    dem_files=["data/dem_t0.tif","data/dem_t1.tif"],              # tiled across time
    dem_timesteps=[120, 240],
    visualize=True
)
print(summary)

• The pipeline stacks multi-source rasters (atm pressure, wind, precip, discharge, DEM) into sequences, normalizes with NaN-aware masks, aligns water-level histories per station, ensures #clusters == #stations, and launches Optuna trials.
• The model: 3×ConvLSTM → CBAM blocks (masked channel+spatial attention), shared LSTMs on water-level sequences → custom temporal attention → ClusterBasedApplication to project station context back into the spatial domain → modulation + dense head to predict flood depth rasters.
• Artifacts written per trial (e.g., best_model.h5, best_val_loss.txt, viz/ with prediction vs. truth and spatial attention maps; study-level study_summary.csv). Temporal attention weights for the best epoch are also exported.

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Data Expectations

• Raster inputs (.tif): Each meteorological/hydrologic variable is a time-stack (one file per timestep), same shape & transform. The DEM can change by regime; provide dem_files + dem_timesteps whose counts sum to the total number of timesteps. Shape checks and tiling are handled for you.
• Water levels (CSV): One CSV per station (naturally sorted), with a water_level column; sequences are normalized per global min/max and aligned to the raster sequence length.
• Cluster polygons (SHP): Produced by voronoi_clusters.run_workflow(...). Each pixel is assigned to at most one cluster; overlaps are checked and rejected.

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Key APIs

Cb_FloodDy.voronoi_clusters

• load_station_points(station_dir, start_idx, end_idx, lon_name=None, lat_name=None) -> list[(lon, lat)]
• load_floodmap(shapefile_path) -> (gdf, boundary_union)
• build_voronoi(stations, boundary_union) -> list[Polygon]
• combine_specified_polygons(polygons, pairs) -> list[Polygon]
• plot_voronoi_on_floodmap(...) -> (fig, ax)
• save_polygons_as_shapefile(polygons, crs, out_path)
• run_workflow(...) -> dict

Cb_FloodDy.bayesian_opt_tuning

• Data utilities: TIFF loaders, NaN-aware normalization, mask verification/visualization, natural sort, water-level ingestion.
• Attention: StandardCBAM (masked), CustomAttentionLayer (top-k emphasis), ClusterBasedApplication (station-to-grid projection).
• Loss/metrics: masked_mse, TrueLoss (averaged over valid pixels).
• Model factory: build_model_with_cbam_weighted(...) returns a compiled Keras model.
• Training & search: run_optimization(...) orchestrates Optuna trials, callbacks (EarlyStopping/LR-plateau, custom checkpoint that also extracts attention), and result logging/visualization.

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Outputs & Artifacts

• checkpoints/optuna/trial_###/best_model.h5 — best epoch per trial.
• .../best_val_loss.txt — scalar.
• .../params_table.csv — single-trial hyperparams; study_summary.csv — all trials.
• .../viz/pred_vs_actual_val0.png, .../viz/spatial_attention_val0.png — qualitative inspection.
• .../artifacts/cluster_masks.npy, .../artifacts/normalization_params.npz — reproducibility.

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Tips & Gotchas

• GPU & precision: TensorFlow GPU memory growth is enabled; global precision set to float32 for stability.
• Valid-pixel masking: Loss/metrics and CBAM attention paths respect masked/invalid pixels (NaNs in inputs become zeros; a complementary mask is carried through).
• Clusters ↔ stations: The model asserts num_clusters == num_stations. Ensure your Voronoi workflow (possibly after combine_pairs) yields a 1:1 mapping.

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References

Refer to these papers for a detailed explanation:

• Daramola, S., et al. (2025). A Cluster-based Temporal Attention Approach for Predicting Cyclone-induced Compound Flood Dynamics. Environmental Modelling & Software 191, 106499. https://doi.org/10.1016/j.envsoft.2025.106499
• Muñoz, D.F., et al. (2024). Quantifying cascading uncertainty in compound flood modeling with linked process-based and machine learning models. Hydrology and Earth System Sciences, 28, 2531–2553. https://doi.org/10.5194/hess-28-2531-2024