Cloud-based Implementation and Validation of a Predictive Fire Risk Indication Model

The high representation of wooden houses in Norwegian cities combined with periods of dry and cold climate during the winter time often results in a high risk of severe fires. This makes it important for public authorities and fire departments to have an accurate estimate of the current fire risk in order to take proper precautions. We report on the implementation of a predictive mathematical model based on first order principles which exploits cloud-provided measurements from weather stations and weather forecasts from the Norwegian Meteorological Institute to predict the current and future fire risk at a given geographical location. We have experimentally validated the model during the winter 2018-2019 at selected geographical locations, and by considering weather data from the time of several historical fires. Our results show that our cloud and web-based implementation is both time and storage efficient, and capable of being able to accurately predict the fire risk measured in terms of the estimated time to ashover. The paper demonstrates that our methodology in the near future may become a valuable risk predicting tool for Norwegian fire brigades

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Validation of a Predictive Fire Risk Indication Model using Cloud-based Weather Data Services

The high and dense representation of wooden homes in Norway, combined with periods of dry and cold climate during the winter season resulting in very dry indoor conditions, have historically resulted in severe fires. Thus, it is important to have an accurate estimate of the current and near future fire risk to take proper planning precautions. Cloud computing services providing access to weather data in the form of measurements and forecasts combined with recent developments in fire risk modelling may enable smart and fine-grained fire risk predication services. The main contribution of this study is implementation and experimental validation of a predictive fire risk indication model, which exploits cloud-provided measurements from weather stations and weather forecasts to predict the current and future fire risk for wooden homes at a given geographical location. The basic idea of the model is to estimate the indoor climate using measured and forecasted outdoor climate for computing indoor wooden fuel moisture content and an estimated time to flashover as indication of the fire risk. The model implementation was integrated into a micro-service based software system and experimentally validated during one winter at selected geographical locations, relying on weather data provided by the RESTful API of the Norwegian Meteorological Institute. Additionally, weather data from several historical fires were considered to relate our predictions to known fire incidents. Our evaluation demonstrates the ability to provide trustworthy and accurate fire risk indications using a combination of weather data measurements and forecast data. Furthermore, our cloud-and micro-service based software system implementation is efficient with respect to data storage and computation time