dynamic-frcm 1.0.1

DYNAMIC fire risk computational model

DYNAMIC Fire risk indicator implementation

This repository contains the implementation of the dynamic fire risk indicator is based upon the research paper:

R.D. Strand and L.M. Kristensen: An implementation, evaluation and validation of a dynamic fire and conflagration risk indicator for wooden homes. In volume 238 of Procedia Computer Science, pp. 49-56, 2024. Proceedings of the 15th International Conference on Ambient Systems, Networks and Technologies Networks (ANT). Online: https://www.sciencedirect.com/science/article/pii/S187705092401233X

The fire risk indicator uses forecast and weather data observation for computing fire risk indication in the form of time-to-flash-over (ttf) for wooden houses.

Weather Data Sources

The implementation has been designed to be independent of any particular cloud-based weather data service.

This library contains an implementation that use the weather data services provided by the Norwegian Meteorological Institute (MET):

• MET Frost API for weather data observations: https://frost.met.no/index.html
• MET Forecasting API for weather data forecasts: https://api.met.no/weatherapi/locationforecast/2.0/documentation

To use these pre-implemented clients a file name .env must be place in your project folder having the following content:

MET_CLIENT_ID = '<INSERT CLIENT ID HERE>'
MET_CLIENT_SECRET = '<INSERT CLIENT SECRET HERE>'

Credentials for using the MET APIs can be obtained via: https://frost.met.no/auth/requestCredentials.html

Please make sure that you conform to the terms of service which includes restrictions on the number of API calls.

Example usage

The following example shows how to use the FireRiskPrediction class to compute fire risks for a given location:

import datetime

from frcm.frcapi import METFireRiskAPI
from frcm.datamodel.model import Location

# sample code illustrating how to use the Fire Risk Computation API (FRCAPI)
if __name__ == "__main__":

    frc = METFireRiskAPI()

    location = Location(latitude=60.383, longitude=5.3327)  # Bergen
    # location = Location(latitude=59.4225, longitude=5.2480)  # Haugesund

    # days into the past to retrieve observed weather data
    obs_delta = datetime.timedelta(days=2)

    wd = frc.get_weatherdata_now(location, obs_delta)
    print (wd)

    predictions = frc.compute_now(location, obs_delta)

    print(predictions)

and should result in an output similar to what is listed below showing hourly computed fire risks for the given location.

FireRiskPrediction[latitude=60.383 longitude=5.3327]
FireRisks[2025-01-20 00:00:00+00:00 TTF(6.072481167177002) WindSpeed(3.1)]
FireRisks[2025-01-20 01:00:00+00:00 TTF(5.99332738001279) WindSpeed(2.4)]
FireRisks[2025-01-20 02:00:00+00:00 TTF(5.967689363087894) WindSpeed(2.7)]
FireRisks[2025-01-20 03:00:00+00:00 TTF(5.9478787329422635) WindSpeed(2.1)]
FireRisks[2025-01-20 04:00:00+00:00 TTF(5.928838874113078) WindSpeed(2.8)]
FireRisks[2025-01-20 05:00:00+00:00 TTF(5.912460820817552) WindSpeed(3.4)]

[ ... ]

FireRisks[2025-01-31 23:00:00+00:00 TTF(6.273135517647007) WindSpeed(4.566666666666666)]
FireRisks[2025-02-01 00:00:00+00:00 TTF(6.290499308219714) WindSpeed(4.6)]
FireRisks[2025-02-01 01:00:00+00:00 TTF(6.303115875685305) WindSpeed(4.533333333333333)]
FireRisks[2025-02-01 02:00:00+00:00 TTF(6.30566616713959) WindSpeed(4.466666666666667)]
FireRisks[2025-02-01 03:00:00+00:00 TTF(6.301802227245228) WindSpeed(4.3999999999999995)]
FireRisks[2025-02-01 04:00:00+00:00 TTF(6.2932319226402) WindSpeed(4.333333333333333)]
FireRisks[2025-02-01 05:00:00+00:00 TTF(6.281008572496977) WindSpeed(4.266666666666667)]
FireRisks[2025-02-01 06:00:00+00:00 TTF(6.265861044817225) WindSpeed(4.2)]

API and implementation

The following methods are the main services currently being provided by the API:

• get_weatherdata_now(location: Location, obs_delta: datetime.timedelta) -> WeatherData - which provided with a location and a weather data observation time delta fetches weather data observations obs_delta into the past and concatenates this with the weather data from the current weather forecast for the location.
• compute(wd: WeatherData) -> FireRiskPrediction - which computes a fire risk predication based on the provided weather data.
• compute_now(location: Location, obs_delta: datetime.timedelta) -> FireRiskPrediction - which computes a fire risk predication for the current point in time using weather data observations obs_delta into the past.

The source code for the library is available at via the Download files and is organised into the following main folders:

• datamodel - contains an implementation of the data model used for weather data and fire risk indications
• weatherdata contains an client implementations and interfaces for fetching weather data from cloud services.
• fireriskmodel contains an implementation of the underlying fire risk model T The main API for the implementation is in the file frcapi.py