ghana-flood-predictor 0.2.0

Python client for the Ghana Flood Warning System API

ghana-flood-predictor

Python client for the Accra Flood Warning System — predict flood risk from hourly weather data.

Installation

pip install ghana-flood-predictor

Quick Start

from ghana_flood_predictor import FloodClient

client = FloodClient()

# Option 1 — one-liner using historical data
result = client.predict_from_datetime("2023-06-15T14:00:00")
print(f"Flood probability: {result['probability']:.1%}")
print(f"Flood warning:     {result['flood_warning']}")

# Option 2 — supply your own weather data
meta = client.metadata()
seq_len = meta["seq_len"]

sequence = [
    {
        "rainfall": 12.5,
        "temperature": 28.0,
        "humidity": 85.0,
        "wind_speed": 3.2,
        "hour": 14,
        "month": 6,
    }
    for _ in range(seq_len)
]

result = client.predict(sequence)
print(f"Flood probability: {result['probability']:.1%}")
print(f"Flood warning:     {result['flood_warning']}")

Methods

client.metadata()

Returns model info including seq_len, threshold, and latest_datetime.

client.weather(target_datetime, hours=None)

Fetch historical weather sequence ending at target_datetime.

data = client.weather("2023-06-15T14:00:00")
# data["sequence"] → list of hourly weather dicts

client.predict(sequence, horizon_hours=1)

Predict flood risk from hourly weather sequence.

Each item in sequence:

Field	Type	Description
rainfall	float	Rainfall in mm
temperature	float	Temperature in °C
humidity	float	Humidity in %
wind_speed	float	Wind speed in m/s
hour	int	Hour of day (0–23)
month	int	Month (1–12)

Returns:

Field	Type	Description
probability	float	Flood probability (0–1)
flood_warning	bool	True if flood predicted
threshold_used	float	Decision threshold
horizon_hours	float	Forecast horizon

client.predict_from_datetime(target_datetime, horizon_hours=1)

Convenience method — fetches weather history and predicts in one call.

result = client.predict_from_datetime("2023-06-15T14:00:00")
print(result["probability"])   # 0.87
print(result["flood_warning"]) # True

Full Example

from ghana_flood_predictor import FloodClient

client = FloodClient()

# Check model info
meta = client.metadata()
print(f"Sequence length needed: {meta['seq_len']} hours")
print(f"Alert threshold: {meta['threshold']}")

# Predict using historical data
result = client.predict_from_datetime("2023-06-15T14:00:00")
print(f"Probability : {result['probability']:.1%}")
print(f"Flood warning: {result['flood_warning']}")

License

MIT