#Embers

Embers is a stochastic fire model that runs on top of CrowdProcess and is written in node. We use existing fire models (ie, firelib, farsite) which require deterministic input parameters and run monte carlo simulations with probability distribution functions for the input data. This method allows for uncertainty quantification of the solution and, much like probabilistic weather forecast, you end up with a probabilistic fire front forecast.

You can install this demo and run it using node. Get started by trying the example below.

##Install

git clone git@github.com:FireEmbers/embers.git; cd embers; npm install

##Example

You can skip ahead and run a fully functional example with node example/example.js. You will be producing worst case and best case forecasts based on a 95% confidence interval. Just import the resulting .kml files with google earth.

###Credentials You'll need to register your email in CrowdProcess in order to run the module. Then, just put the registered email and password in the credentials.json file, in the root of the module with the following format:

{
    "email": "your@email.com",
    "password": "yourpassword1"
}

###Require

var embers = require('embers');

###Define parameters

Every parameter bellow is only an example. Go ahead and fiddle with the values

var opts = {
  ignitionPt: [41.7718400422817, -7.9167833239285], //[latitude, longitude] of ignition point
  u: 2, //mid flame wind speed
  alpha: 115, //wind direction in degrees, clockwise from north
  std: 50, //standard deviation in percentage of average speed
  moisture: 5, // fuel moisture in % of total fuel mass
  height: 10000, // Computational domain dimension in km
  width: 10000,
  rows: 200, //Computational resolution size
  cols: 200,
  n: 100 //Number of Monte Carlo simulations
};

###Call Embers

embers(opts, function(err, kmlMaps){
  if (err) throw err;

  for ( var param in kmlMaps ) {
    fs.writeFileSync( '' + param + '.kml'), kmlMaps[param], {encoding: 'utf8'});
  }

});

###Results

The following image show worst case and best case scenarios, respectively in red and orange.