https://github.com/moindalvs/neural_networks_forest_fire_classification

PREDICT THE BURNED AREA OF FOREST FIRES WITH NEURAL NETWORKS
https://github.com/moindalvs/neural_networks_forest_fire_classification

auc-roc-curve binary-classification classification dropout-layers epochs hyperparameter-optimization hyperparameter-tuning keras-classification-models neural-networks tensorflow weight-initialization

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PREDICT THE BURNED AREA OF FOREST FIRES WITH NEURAL NETWORKS

• Host: GitHub
• URL: https://github.com/moindalvs/neural_networks_forest_fire_classification
• Owner: MoinDalvs
• Created: 2022-07-09T07:40:12.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-08-27T15:18:06.000Z (about 2 years ago)
• Last Synced: 2023-04-24T16:04:36.697Z (over 1 year ago)
• Topics: auc-roc-curve, binary-classification, classification, dropout-layers, epochs, hyperparameter-optimization, hyperparameter-tuning, keras-classification-models, neural-networks, tensorflow, weight-initialization
• Language: Jupyter Notebook
• Homepage:
• Size: 2.42 MB
• Stars: 6
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
## **Problem Statement**

# PREDICT THE BURNED AREA OF FOREST FIRES WITH NEURAL NETWORKS.

### About the Dataset

##### Attribute Information:

Data Description:

**Data Available at: http://archive.ics.uci.edu/ml/datasets/Forest+Fires**

### Forest fires can create ecological problems and endanger human lives and property. Understanding when they occur and what causes them is important for managing them. The data we'll be working with in this is associated with a scientific research paper on predicting the occurrence of forest fires in Portugal using modeling techniques. The focus will not only be about modelling for this project, but also on visualizing it. There will also be exploratory analysis on the data to better understand it and to find any relationships that might be present in it.

### Here are descriptions of the variables in the data set and the range of values for each taken from the research paper:

* **X**: X-axis spatial coordinate within the Montesinho park map: 1 to 9 

* **Y**: Y-axis spatial coordinate within the Montesinho park map: 2 to 9 

* **month**: Month of the year: 'jan' to 'dec' 

* **day**: Day of the week: 'mon' to 'sun' 

* **FFMC**: Fine Fuel Moisture Code index from the FWI system: 18.7 to 96.20 

* **DMC**: Duff Moisture Code index from the FWI system: 1.1 to 291.3 

* **DC**: Drought Code index from the FWI system: 7.9 to 860.6 

* **ISI**: Initial Spread Index from the FWI system: 0.0 to 56.10 

* **temp**: Temperature in Celsius degrees: 2.2 to 33.30 

* **RH**: Relative humidity in percentage: 15.0 to 100 

* **wind**: Wind speed in km/h: 0.40 to 9.40 

* **rain**: Outside rain in mm/m2 : 0.0 to 6.4 

* **area**: The burned area of the forest (in ha): 0.00 to 1090.84 

## Author

     

     

     [email protected]