https://github.com/vivek3141/forest-fire-predictor

Predict the damage done by forest fires using Deep Learning.
https://github.com/vivek3141/forest-fire-predictor

flask heroku html js keras machine-learning python tensorflow webapp

Last synced: over 1 year ago
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Predict the damage done by forest fires using Deep Learning.

• Host: GitHub
• URL: https://github.com/vivek3141/forest-fire-predictor
• Owner: vivek3141
• Created: 2018-11-17T19:48:22.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2018-11-18T15:54:26.000Z (over 7 years ago)
• Last Synced: 2023-03-02T04:25:48.800Z (over 3 years ago)
• Topics: flask, heroku, html, js, keras, machine-learning, python, tensorflow, webapp
• Language: Python
• Homepage: https://vivek3141.github.io/forest-fire-predictor/
• Size: 259 KB
• Stars: 12
• Watchers: 1
• Forks: 5
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

          


![Logo](https://raw.githubusercontent.com/vivek3141/forest-fire-predictor/master/img/logo.png)

# Forest Fire Predictor

This webapp can be used to predict the amount of area burnt by a fire by selecting a point on a map.

## Built Using

* Flask API - hosted on Heroku

* Keras with a Tensorflow backend for predicting - computed on the cloud and sklearn for pre-processing

* JS and HTML for the websites

* Openweathermap API for the weather data

* Pandas, Numpy, Dill for csv reading, linear algebra and saving objects, respectively.

* Integrated and hosted on GitHub Pages

## How it works

* When the user clicks the button on the web page, the Heroku API is sent the latitude and longitude

of the place selected on the map.

* The python script then fetches weather data from the openweathermap API.

* This data is used to calculate the various Fire Weather Indices.

* The FWIs and the raw data from the API are fed into the Deep Regression model which 

outputs  

## Requirements

* `keras`, `tensorflow`, `sklearn` for machine learning

* `flask`, `flask-cors` for the API

* `numpy` for linear algebra

* `dill` for object saving

* `pandas` for reading the training data in the form of `*.csv`