Data Analysis Of Forest Fire In Montesinho Natural Park
The skills we demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.
Montesinho is a beautiful protected area located in the municipalities of Vinhais and Bragança, northeastern Portugal. Data from the map shows sections of the southern slopes of the Serra da Coroa (Sierra de la Culebra) fall within the park.
It is home to many different kinds of animals. Its biodiversity includes the Iberian wolf, roe deer, wild boar, Iberian lynx, common genet, red fox and European otter.
What a disaster it would be if there were a forest fire!
Today I am going to analyze the Forest Fire Predictors In Montesinho Natural Park. The forest fire data concerns burned areas of the forests in Montesinho Natural park due to forest fires. It was collected from January 2000 to December 2003 . It contains 517 instances
13 variables (1 dependent variable, 4 discrete attributes and 8 continuous attributes). These are the variables:
- Response: area: Burned area of a forest fire (ha)
- X: x-axis coordinate of the Montesinho park map: 1 to 9
- Y: u-axis coordinate of the Montesinho park map: 2 to 9
- FFMC: A numerical rating of the moisture content of litter and other cured fine fuels: 18.7 to 96.2
- DMC: A numerical rating of the average moisture content of loosely compacted organic layers and medium-size woody material: 1.1 to 291.3
- DC: A numerical rating of the average moisture content of deep, compact, organic layers: 7.9 to 860.6
- ISI: A numerical rating of the expected rate of fire spread: 0.0 to 56.10
- Month: month of the year: 1 to 12
- Day: day of the week: 1 to 7
- Temp: temperature in Celsius degrees: 2.2 to 33.30
- RH: relative humidity in %: 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
- Here we raise some questions:
- How are the forest fires distributed in the park?
- What are the significant variables to predict forest fires?
- How are these variables related to the area of forest fires?
What can we suggest to tourists and the fire department? Data
- First, how are forest fires distributed in the park.
- Here we raise some questions:
How large can a forest fire be? We did a summary on the burned down area data and categorized the area as 'small' if the area is under first quantile, as 'median' if the area is between first and third quantile, as 'large' if it if above the third quantile. So the summary is the following:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.09 2.14 6.37 24.60 15.42 1091.00
Categorize area by the above information:
(0,2.14) size of area = ‘small’
(2.14,15.42) size of area = ‘median’
(15.42,1091.00) size of area = ‘large’
We did a boxplot of each category and found out that there are some outliers. After taking out the outliers, we zoom in and do a box plot for category median and small.
Second, we come to the question: which variables are the most significant? In order to achieve this goal, I did some statistical analysis. It turns out that the
fit = glm(log(area+1)~FFMC+DMC+DC+ISI+temp+RH+wind+rain,data=mydata_new,family=Gamma())
glm(formula = log(area + 1) ~ FFMC + DMC + DC + ISI + temp +
RH + wind + rain, family = Gamma(), data = mydata_new)
Min 1Q Median 3Q Max
-2.11417 -0.52704 -0.08414 0.28686 1.39097
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.405e-01 6.776e-01 0.798 0.426
FFMC -2.129e-03 7.666e-03 -0.278 0.781
DMC -6.427e-04 3.960e-04 -1.623 0.106
DC 8.486e-05 1.036e-04 0.819 0.413
ISI 1.043e-02 6.706e-03 1.556 0.121
temp 7.194e-04 4.458e-03 0.161 0.872
RH 1.994e-03 1.420e-03 1.405 0.161
wind -1.043e-02 1.004e-02 -1.039 0.300
rain -1.099e-02 4.172e-02 -0.263 0.792
Based on the above analysis, DMC, ISI,RH are the three most significant variables, having critical values of 0.106,0.121 and 0.161 respectively.
What can we suggest to tourists and to the fire department? Summer and Fall are the seasons when there are the most tourists, thus we suggest tourists be more careful when using fire (camping, BBQ etc).The fire department should look closer at measurements of RH, ISI, DMC variables and prepare accordingly.
One of the drawbacks of this data set is that it does not record human activity which, I believe, does have a huge impact on the occurrence of forest fires.
What times do forest fires happen the most?
Indeed, fall and summer are the most dangerous times due to high temperature and low relative humidity. Most importantly, people usually go camping and hiking in this two seasons, which brings potential danger.
- More closely analyze the relationship of variables to each other.
- Develop a more precise model for predicting the area based on the variables we have now.
I will keep on improving this project. Thank you very much for reading this post. Please feel free to give any advice!