Groundbreaking Research: Tel Aviv University Reveals How Smartphones Can Predict Wildfires and Extreme Weather

In a groundbreaking study, Tel Aviv University researchers propose harnessing the power of smartphones to gather weather data and improve early warning systems for extreme weather, including wildfires.

Smartphones, ubiquitous devices teeming with micro-sensors, collect a wealth of environmental data in real time. While this data is typically used for device functions and lost, researchers from Tel Aviv University envision a future where it could be harnessed to improve early warning systems for extreme weather events like wildfires.

These sensors, capable of measuring temperature, pressure, humidity, light, sound, and location, are constantly at work. By aggregating data from millions of smartphones worldwide, researchers believe it’s possible to fill gaps in traditional weather monitoring systems, especially in remote areas. This vast, user-generated dataset could provide valuable insights into local weather conditions, potentially leading to more accurate and timely forecasts.

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While data privacy and ethical considerations are paramount, the potential benefits of using smartphone data for public good are significant. By working with technology companies and policymakers, researchers aim to unlock the power of this massive data source to safeguard communities from natural disasters.

Prof. Colin Price and PhD student Hofit Shachaf from the Department of Geophysics in the Porter School of the Environment and Earth Sciences at Tel Aviv University used data collected from the global public via the WeatherSignal app (OpenSignal) to develop a methodology for assessing wildfire risk based solely on smartphone data collected by the public. The results were recently published in the journal Natural Hazards and Earth System Sciences (NHESS).

One key parameter determining the likelihood of a wildfire is the moisture content in vegetation (essentially the fuel available for the fire) – which, in turn, is determined by the temperature and relative humidity of the surrounding air. Both the temperature and relative humidity can be easily obtained from the public’s smartphones.

Hofit Shachaf explains: “We developed an index based on VPD (vapor pressure deficit), which reflects the dryness of the vegetation based on environmental parameters (temperature and humidity) . In hot and dry atmospheric conditions more moisture is drawn from the plants, due to enhanced evaporation and transpiration (evapotranspiration) that essentially facilitates ignition of fires. In cooler, more humid air, forests do not usually catch fire since their moisture level is too high. Previous studies have also used VPD to predict wildfire risk levels – though the data for these calculations is mostly obtained from local weather stations. The novelty of our study is the utilization of data collected from smartphones, without their users’ active participation, to calculate VPD over large areas and at high spatial and temporal resolutions – providing important insights into wildfire risk evaluation.”

However, smartphone data do contain errors. The temperature reading might reflect the air conditioning in your office, while the humidity sensor might identify moisture when the user is taking a shower. But the huge amount of data collected from smartphones allows us to remove outliers in the data set. Furthermore, since the micro-sensors are not calibrated before they are put in our phones, it was necessary to first calibrate the local smartphone data against commercial meteorological stations. This procedure turned out to be relatively straightforward, with just a single calibration needed to correct a smartphone’s readings. After calibrating or “training” the device, the researchers analyzed two major wildfire events: fires in Israel in November 2016 and the massive fire in Portugal in July 2013. The results were surprising, with smartphone data collected from the public showing significant VPD anomalies before and during these major fires.

Hofit Shachaf adds: “It’s surprising, but even though each smartphone has its own errors and biases, with large amounts of data from many smartphones, we can average out the errors and still retain useful data. The large volume of data helps overcome issues associated with individual smartphones.”