Flood Water Depth Prediction Model (U-Net)

A U-Net regression model that predicts flood water depth (meters) given terrain and climate inputs.

Architecture

Model: U-Net with ResNet-34 encoder (ImageNet pretrained)
Task: Pixel-wise regression — continuous water depth ≥ 0 meters
Input: 9-channel raster (128×128 patches)
Output: Single-channel depth map (meters)

Input Channels

#	Channel	Unit
0	DEM (elevation)	meters
1	Slope	degrees
2	HAND (Height Above Nearest Drainage)	meters
3	TWI (Topographic Wetness Index)	unitless
4	Manning's roughness	unitless
5	Soil permeability	mm/hr
6	Rainfall intensity	mm/hr
7	Storm duration	hours
8	Return period	years

Performance (Test Set)

Metric	Value
RMSE	1.0404 m
MAE	0.4178 m
R²	0.7916
Extent F1	0.7549
CSI@0.5m	0.6450
CSI@1.0m	0.6594
CSI@2.0m	0.6893

How to Use

1. Install dependencies

pip install torch segmentation-models-pytorch huggingface_hub rasterio numpy scipy

2. Load the model

python

import torch
import numpy as np
import json
import segmentation_models_pytorch as smp
from huggingface_hub import hf_hub_download

# Download model weights and normalization stats
weights_path = hf_hub_download("peyterho/flood-depth-unet", "model.pt")
stats_path   = hf_hub_download("peyterho/flood-depth-unet", "stats.json")

# Build and load model
model = smp.Unet(
    encoder_name="resnet34",
    encoder_weights=None,
    in_channels=9,
    classes=1,
    activation=None,
)
model.load_state_dict(torch.load(weights_path, map_location="cpu"))
model.eval()

# Load normalization constants
with open(stats_path) as f:
    stats = json.load(f)

means = np.array(stats["means"], dtype=np.float32).reshape(9, 1, 1)
stds  = np.array(stats["stds"],  dtype=np.float32).reshape(9, 1, 1)

3. Prepare your input

The model expects a 9-channel raster of shape (9, 128, 128). Six channels come from your terrain data, three you set yourself as your climate scenario:

python

# === YOUR TERRAIN DATA (channels 0–5) ===
# Each is a 2D numpy array of shape (128, 128) at ~30 m resolution

dem       = ...  # elevation in meters (from SRTM, Copernicus DEM, or LiDAR)
slope     = ...  # terrain slope in degrees
hand      = ...  # Height Above Nearest Drainage in meters
twi       = ...  # Topographic Wetness Index
manning   = ...  # Manning's roughness (0.03=smooth, 0.06=vegetated, 0.15=dense forest)
soil_perm = ...  # soil permeability in mm/hr (5=clay, 15=loam, 50=sand)

# === YOUR CLIMATE SCENARIO (channels 6–8) ===
# These are scalar values broadcast to the full 128×128 grid

rainfall_intensity = 80.0   # mm/hr  — how hard it's raining
storm_duration     = 12.0   # hours  — how long the storm lasts
return_period      = 100.0  # years  — rarity of the event (2=common, 500=extreme)

H, W = 128, 128
rain_ch = np.full((H, W), rainfall_intensity, dtype=np.float32)
dur_ch  = np.full((H, W), storm_duration,     dtype=np.float32)
rp_ch   = np.full((H, W), return_period,      dtype=np.float32)

# === Stack and normalize ===
raw = np.stack([dem, slope, hand, twi, manning, soil_perm,
                rain_ch, dur_ch, rp_ch])          # shape: (9, 128, 128)
normalized = (raw - means) / (stds + 1e-8)
x = torch.from_numpy(normalized).unsqueeze(0).float()  # (1, 9, 128, 128)

4. Predict flood depth

python

with torch.no_grad():
    depth_map = torch.relu(model(x)).squeeze().numpy()  # (128, 128), values in meters

# depth_map[i, j] = predicted water depth at that pixel
# 0.0 = dry, 1.5 = 1.5 meters of water, etc.

5. Compare climate scenarios

Hold the terrain fixed and vary the storm to see how depth changes:

python

scenarios = {
    "Moderate storm":     ( 40.0,  6.0,   10),
    "Heavy storm":        ( 80.0, 12.0,   50),
    "Extreme (1-in-500)": (160.0, 24.0,  500),
}

for name, (rain, dur, rp) in scenarios.items():
    raw[6, :, :] = rain
    raw[7, :, :] = dur
    raw[8, :, :] = rp
    normed = (raw - means) / (stds + 1e-8)
    x = torch.from_numpy(normed).unsqueeze(0).float()

    with torch.no_grad():
        depth = torch.relu(model(x)).squeeze().numpy()

    print(f"{name}: max depth = {depth.max():.2f}m, "
          f"flooded area = {(depth > 0.01).mean()*100:.1f}%")

Where to Get the Terrain Inputs

Channel	Free source	How to compute
DEM	Copernicus DEM 30 m or SRTM	Download GeoTIFF for your area
Slope	Derived from DEM	`np.degrees(np.arctan(np.sqrt(dx2 + dy2)))` where `dy, dx = np.gradient(dem, 30.0)`
HAND	Derived from DEM	Use `pysheds` or `whitebox` (see below)
TWI	Derived from DEM	`np.log(contributing_area / np.tan(slope_radians))`
Manning's n	ESA WorldCover land use → lookup table	Forest = 0.12, grass = 0.05, urban = 0.02, water = 0.03
Soil perm.	SoilGrids or HWSD	Clay = 5, loam = 15, sand = 50 mm/hr

Computing slope from a DEM

python

dy, dx = np.gradient(dem, 30.0)          # 30 m pixel spacing
slope = np.degrees(np.arctan(np.sqrt(dx**2 + dy**2)))

Computing HAND from a DEM with `pysheds`

pip install pysheds

python

from pysheds.grid import Grid

grid = Grid.from_raster("dem.tif")
dem  = grid.read_raster("dem.tif")

pit_filled = grid.fill_pits(dem)
flooded    = grid.fill_depressions(pit_filled)
inflated   = grid.resolve_flats(flooded)
fdir       = grid.flowdir(inflated)
acc        = grid.accumulation(fdir)
hand       = grid.compute_hand(fdir, dem, acc > 500)  # threshold for channel cells

Handling Areas Larger Than 128×128

For a real locale your DEM will be much bigger. Use sliding-window inference with overlap to avoid tile-edge artefacts:

python

def predict_large_area(model, raw_input, means, stds,
                       tile_size=128, overlap=32):
    """Predict flood depth over a large raster using tiled inference."""
    C, H, W = raw_input.shape
    depth_map = np.zeros((H, W), dtype=np.float32)
    count_map = np.zeros((H, W), dtype=np.float32)
    stride = tile_size - overlap

    for y in range(0, H, stride):
        for x in range(0, W, stride):
            y1, y2 = y, min(y + tile_size, H)
            x1, x2 = x, min(x + tile_size, W)
            patch = raw_input[:, y1:y2, x1:x2]

            # Pad if smaller than tile_size
            ph, pw = patch.shape[1], patch.shape[2]
            if ph < tile_size or pw < tile_size:
                padded = np.zeros((C, tile_size, tile_size), dtype=np.float32)
                padded[:, :ph, :pw] = patch
                patch = padded

            normed = (patch - means) / (stds + 1e-8)
            t = torch.from_numpy(normed).unsqueeze(0).float()

            with torch.no_grad():
                out = torch.relu(model(t)).squeeze().numpy()

            depth_map[y1:y2, x1:x2] += out[:ph, :pw]
            count_map[y1:y2, x1:x2] += 1.0

    return depth_map / np.maximum(count_map, 1.0)

Saving Results as a GeoTIFF

python

import rasterio

# Copy CRS and transform from your input DEM
with rasterio.open("dem.tif") as src:
    meta = src.meta.copy()

meta.update(count=1, dtype="float32", nodata=-9999)

with rasterio.open("flood_depth.tif", "w", **meta) as dst:
    dst.write(depth_map, 1)

# → open flood_depth.tif in QGIS, ArcGIS, or Google Earth Engine

Climate Conditioning

Vary input channels to model different climate scenarios:

Rainfall intensity: 10–160 mm/hr (higher = heavier storms)
Duration: 1–24 hours (longer = more accumulation)
Return period: 2–500 years (higher = rarer, more severe events)

Training

Data: Physics-informed synthetic (DEM + Manning's equation + HAND)
Loss: Weighted MSE + L1 + Gradient loss
Optimizer: AdamW (lr=0.001)
Scheduler: Cosine Annealing
Epochs: 50
Parameters: ~24.5M

Limitations

Trained on synthetic data — fine-tune on regional hydraulic model outputs (HEC-RAS, LISFLOOD-FP) for production use
Simplified physics (no infrastructure, backwater effects, or 2D flow routing)
Best used as a screening tool, pre-training backbone, or educational tool

TODO / Roadmap

Make the model production-ready

Fine-tune on real hydraulic simulation data — run LISFLOOD-FP or HEC-RAS for a target locale and use those depth grids as training labels instead of the synthetic physics approximations
Add SAR imagery channels (Sentinel-1 VV/VH) — lets the model learn from actual observed flood events, not just terrain features
Swap backbone to a geospatial foundation model — replace ResNet-34 with Prithvi-EO-2.0 via TerraTorch for much better generalization, especially when labeled data is scarce

Improve what we have

Train on larger patches (256×256 or 512×512) with GPU — captures wider drainage patterns and river context
Generate more diverse synthetic terrain — add coastal, urban, and mountainous terrain types to the data generator
Build a Gradio demo Space — let users upload a DEM and get a flood depth map in the browser
Add temporal dimension — extend the model to predict depth evolution over time (multi-step forecast), not just peak depth

Validate on a real locale

Download Copernicus DEM for a target area and compute slope, HAND, TWI
Run inference on real terrain to see how the model performs outside synthetic data
Compare predictions against known flood extents (e.g., Sen1Floods11 or Copernicus EMS rapid activations)

Reproduce or extend training

All scripts are in scripts/:

generate_synthetic_data.py — standalone data generation
train_full_pipeline.py — end-to-end: data gen → train → evaluate → push to Hub
logs/training.log — full training log from the original run (50 epochs, best RMSE 0.94 m at epoch 33)

peyterho/flood-depth-unet

Flood Water Depth Prediction Model (U-Net)

Architecture

Input Channels

Performance (Test Set)

How to Use

1. Install dependencies

2. Load the model

3. Prepare your input

4. Predict flood depth

5. Compare climate scenarios

Where to Get the Terrain Inputs

Computing slope from a DEM

Computing HAND from a DEM with pysheds

Handling Areas Larger Than 128×128

Saving Results as a GeoTIFF

Climate Conditioning

Training

Limitations

TODO / Roadmap

Make the model production-ready

Improve what we have

Validate on a real locale

Reproduce or extend training

peyterho/
flood-depth-unet

Computing HAND from a DEM with `pysheds`