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<!DOCTYPE html>
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<head>
<meta charset="UTF-8">
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<title>Project: Forest Fire in Algeria
</title>
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<p> <br> <br>
<font size="+2"> <b> Stepwise Regression Analysis on Probability of Forest Fire </b></font>
<br>
<br>
<font size="+1"> <b> Project Description: </b></font>
The relationships between the 'Probability of Forest Fire' in Algeria and its various weather components have been estimated. <b> Stepwise Linear Regression has been
performed for this purpose and K-fold Cross Validation (K=10) has been carried out to evaluate the performance of the model. </b>
SAS has been used for the regression and validation. Prior to that, Python was used for data cleaning. <br>
<font size="+1"> <b> Data Introduction: </b></font> The dataset includes 2,243 records of weather components' data.
<br />
<table cellspacing="0" cellpadding="0" border="1">
<!-- <caption><center style="color:#ece3e3";></center>>Attributes in the dataset</center></caption> -->
<tr>
<td style="text-align: left;">
<b>1. Date: </b>(DD/MM/YYYY) Day, month, and year. <br>
<b>2. Temperature: </b> Temperature at noon (temperature max) in Celsius degrees: 22 to 42. <br>
<b>3. Wind: </b> Wind speed in km/h: 6 to 29. <br>
<b>4. RainAmount: </b> Total rain on that day in mm: 0 to 16.8. <br>
<b>5. FineMoisture: </b> Fine Fuel Moisture Code (FFMC) index: 28.6 to 92.5. <br>
<b>6. DuffMoisture:</b> Duff Moisture Code (DMC) index: 1.1 to 65.9. <br>
<br />
</td>
<td style="text-align: left;">
<b>7. Drought: </b> Drought Code (DC) index: 7 to 220.4. <br>
<b>8. InitialSpeed:</b> Initial Speed Index (ISI) of the fire: 0 to 18.5. <br>
<b>9. BuildUp:</b> Buildup Index (BUI) index: 1.1 to 68. <br>
<b>10. WeatherIndex: </b>Fire Weather Index: 0 to 31.1. <br>
<b>11. FireProb:</b> The probability percentage of the occurrence of forest fire: 0 to 100. <br>
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</td>
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</table>
<br>
There are 9 predictor variables: Temperature, Wind, RainAmount, FineMoisture, DuffMoisture, Drought, InitialSpeed , BuildUp, and WeatherIndex. <br>
The response variable is FireProb.
<br> <br>
<font size="+1"> <b> Methods Used: </b></font>
* A least square regression has been carried out with all the predictor variables. <br>
* Stepwise Regressions and K-fold validations have been performed following this: <br>
Random_variable = Random integer between 1 and K; <br>
For i=1 to K do: <br>
 Training_DataSet = K-1 folds. (random_variable ≠ i); <br>
 Stepwise regression performed on Training_Dataset with entry significance level = remove significance level = 0.05; <br>
 Testing_DataSet = 1 fold. (random_variable =i); <br>
 Calculate the model fit statistics on the Testing_Dataset; <br>
End; <br>
Calculate the average test results from all the K-folds. <br>
<br> <br>
<font size="+1"> <b> Results: </b></font>
<br>
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<br> <br>
<font size="-1"> Note: The original dataset has been collected from UC Irvine Machine Learning Repository
(https://archive.ics.uci.edu/ml/index.php). <br>
The relevant research paper is: Abid, F., & Izeboudjen, N. (2020).
Predicting forest fire in Algeria using data mining techniques:
Case study of the decision tree algorithm. In International Conference on Advanced Intelligent Systems for Sustainable Development (pp. 363-370). Springer, Cham
</font>
<br> <br>
<font size="+2"><b> <a class="nav-link" href="https://github.com/rafsanRubaiyat/Forest-Fire-StepwiseRegression"
target="_blank" data-no="3">Link to the Github repository</a></b></font>
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