This section introduces Sensitivity analysis of the proposed method and the results of the experiment.
3.4.2. Experimental Results
Experiment 1 worked to predict the forest fires in Xichang City in March and April 2020 based on the fire data of Xichang City from March and April of 2015 to 2019.
Figure 13a,d show a fire map of Xichang in March and April 2015–2019 and 2020 [
12].
Based on rule set 1, we extracted the spatio-temporal knowledge of fire, meteorology, terrain, vegetation, and human factors from the KGFFP in 2015–2019 and 2020. We used this spatio-temporal knowledge to produce training samples and test samples for machine learning-based forest fire prediction models. First, the real fire point was extracted according to the land cover type of the fire point data in March and April 2015–2020. Next, positive samples were made based on real fire points in March and April 2015–2020, and positive samples were constructed based on the fire point data in March and April of 2015–2020 in Xichang City. Then, negative samples were made based on unfired areas and in March and April 2015–2020, and negative samples were constructed in the unfired area as a training set. After that, the samples from March and April 2015–2019 were set as the training samples and the samples from March and April 2020 were set as the test samples. Finally, training samples and test samples were obtained. The ratio of negative samples to positive samples was .
Based on rule set 2, we trained and tested the RF-based and the DF-based forest fire models, respectively, with a step size of 0.1. We then obtained the predicted results of RF-based and DF-based forest fire models.
Based on rule set 3, we compared the actual and predicted values of the Xichang fire point in March and April 2020 and evaluated the predicted results. The meanings of
and
are shown in Formulas (1) and (2).
where
(True Positive) means that the prediction is correct and the sample is positive;
(False Positive) means that the prediction is wrong and the sample is predicted to be positive, but the sample is actually negative;
(False Negative) means that the prediction is wrong and the sample is predicted to be negative, but the sample is actually positive
For the RF model, when
= 1.3, the difference between
and
reaches the minimum value. Moreover, the values of
and
are relatively high, which is satisfactory. The definition of the metric
is shown in Formula (3). The result of the
F1 of the RF-based forest fire model is 0.7839. For the DF-based forest fire model, when
= 1.5, the difference between
and
reaches the minimum value, and
.7957.
Figure 14 shows the experimental results.
Table 3 shows the statistical information of samples.
Figure 15 shows the probability map of forest fire risk for March and April 2020 for Experiment 1. The prediction results of between the prediction methods with RF and with DF are not drastically different because both are decision tree-based machine learning models. The prediction methods with both models are significantly different from the prediction results with SVM. In our experiment, we select RF and DF for forest fire models because they have relatively high prediction accuracies.
where
is the harmonic mean of
and
,
indicates
, and
indicates
. The larger the metric
, the better the overall performance of the model. High
value means both
and
are good.
Based on data used by the above experiment, we added the data of Xichang City from March and April of 2010 to 2014. Based on the fire point data of Xichang City from March and April of 2010 to 2019, it can predict the forest fires of Xichang City in March and April of 2020.
Figure 13b,d show the fire map of Xichang in March and April 2010–2019 and 2020 [
12].
Positive samples (quantity is 659) are constructed based on the fire point data in March and April of 2010–2019 in Xichang City, and negative samples are constructed in the unfired area as a training set. Positive samples (quantity is 45) are constructed based on the fire data of Yanyuan County in March and April 2020, and negative samples are constructed in the unfired area as a test set.
Figure 16 shows the metric
and the accuracy of the RF model and the DF model.
Table 4 shows the statistical information of samples.
Figure 17 shows the probability map of forest fire risk for March and April 2020 based on the data of Xichang City from March and April of 2010 to 2019. The prediction results between the prediction methods with RF and with DF are less different because both are decision tree-based machine learning models. The prediction methods with both models are significant different from the prediction results with SVM. In our experiment, we select RF and DF for forest fire models because they have relatively high prediction accuracies.
- 2.
Experiment 2
Since Xichang City and Yanyuan County are adjacent in space, factors such as meteorology, topography, and vegetation are similar, to a certain extent. In that case, the data of Yanyuan County is used as a part of the training samples of Xichang City to predict the accuracy of fire points in Xichang City.
Figure 13c,d shows a fire map of Xichang and Yanyuan in March and April 2015–2019 and a fire map of Xichang in March and April 2020 [
12].
Positive (quantity is 150) and negative samples were constructed in the unfired area as a training set. Positive samples (quantity is 45) were constructed based on the fire point data in March and April 2020 in Xichang City, and negative samples were constructed in the unfired area as a test set.
Figure 18 shows the metric
and accuracy of the RF model and DF model.
Table 5 shows the statistical information of samples.
Figure 19 shows the probability map of forest fire risk for March–April 2020 for Experiment 2. The prediction results between the prediction methods with RF and with DF are not drastically different because both are decision tree-based machine learning models. The prediction methods with both models are significant different from the prediction results with SVM. In our experiment, we select RF and DF for forest fire models because they have relatively high prediction accuracies.