# Machine-Learning Applications in Geosciences: Comparison of Different Algorithms and Vegetation Classes’ Importance Ranking in Wildfire Susceptibility

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## Abstract

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## 1. Introduction

## 2. Material and Methods

#### 2.1. Study Area

^{2}and is shaped as a narrow strip of land bordered by the Tyrrhenian sea along its entire southern extension (Figure 1). The territory is crossed by the two main Italian mountain ranges: the Alps to the west and the Apennines to the east. Liguria is a mainly mountainous (65%) and hilly (35%) region with no plains and a coast that almost always overlooks the sea. The resulting topography is very complex. The slope is higher than 40% over 50% of the territory, and there is dense and heterogeneous vegetation.

#### 2.2. Wildfires Dataset

#### 2.3. Predictor Variables

#### 2.4. The Methodological Workflow

- Elaboration of the input dataset: pre-processing of the raster describing the predictor variables (i.e., topographic, anthropogenic, and vegetation features) and the independent variable (i.e., the wildfire dataset).
- Selection of the testing and training subsets: 3 out of 17 years were randomly selected for the testing subset based on a clustering procedure, to ensure a fair representation of the possible wildfire trends.
- Selection of the validation subset (via spatial-cross validation): the training subset was then split into 5 parts, and the model was trained on the remaining four parts—the one left out was alternated.

- Implementation of the machine learning (ML) algorithms, namely, random forest (RF), multi-layer perceptron (MLP), and support vector machine (SVM), for the spatial prediction of wildfire susceptibility.
- Evaluation of the performance indicators for each ML algorithm and for the two seasons.
- The AUC (area under the curve) ROC (receiver operating characteristic) were evaluated over the testing dataset.
- The root mean-square error (RMSE) between the values resulting from the three ML-models and the testing subset was also evaluated.

- Elaboration of the wildfire susceptibility maps, based of the probabilistic outputs resulting from the three ML implemented models.
- Assessment of the importance of the predictor variables, obtained by evaluating their rankings and the marginal effect on the predicted outcome.
- This was achieved with RF, which can handle both numerical variables (e.g., the percentage of neighboring vegetation) and native categorical variables (e.g., the classes of vegetation at the pixel level).

#### 2.5. Machine-Learning Algorithms

#### 2.5.1. Random Forest

`mtry`) and the number of trees (

`ntree`) are the two parameters of the model that need to be optimized. In this study, these parameters were set to the rounded up square-root of the number of predictor factors for

`mtry`and 750 for

`ntree`following [15]. In order to perform a meaningful split, the prediction error is normally computed on a subset of observations that are not used in the training subsets (called “out-of-bag”—OOB). OOB include about one-third of the testing data, selected by bootstrapping (i.e., random sampling with replacement). At each split of a decision tree, the Gini impurity allows one to determine how the observations should split nodes to form the tree. This step is iterated until each node contains only one observation. For a classification problem, the prediction of new data is finally computed by counting the maximum voting.

`R`package

`randomForest`and the function

`partialPlot`to generate the partial dependence plots [49].

#### 2.5.2. Multi-Layer Perceptron

`mlp`implemented in the

`R`package

`RSNNS`[51]. The number of epochs and the learning rate of our model were set to 500 and 0.1, and for the other parameters we kept the default values (i.e., one hidden layer of 50 neurons).

#### 2.5.3. Support Vector Machine

`ksvm`implemented in the R package Kernlab [56].

`rminer`[57].

#### 2.6. Model Evaluation

#### 2.6.1. Spatial Cross-Validation

#### 2.6.2. Selection of the Testing Subset

#### 2.6.3. Performance Metrics

## 3. Results and Discussion

#### 3.1. Comparison of the Three ML Algorithms

#### 3.2. Susceptibility Maps

#### 3.3. Assessment of the Predictor Variables

#### 3.3.1. Effect of the Neighboring Vegetation

`R`implementation (available in

`randomForest`package), there is no need for transforming “categorical” variables into “numerical” variables, thereby limiting the number of predictors. The resulting ROC curves clearly show that accounting for the “neighboring vegetation” allows one to enhance the performance of the model (see Figure 8), resulting in increasing values of AUC from 0.906 to 0.944 in winter and from 0.911 to 0.953 in summer. The “global model”, which includes both the local and the neighboring vegetation, has a similar performance to the neighboring model (AUC equal to 0.939 in winter and 0.952 in summer) (Figure 8) and was considered for the final assessment of the importance of the predictor variables.

#### 3.3.2. Predictor Variables Importance Ranking

## 4. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 2.**Yearly burned area (

**a**) and number of fires (

**b**) in Liguria during the summer and winter in the period 1997–2017.

**Figure 5.**Clustering of the wildfire dataset for winter and summer, resulting from K-means (with K = 3).

**Figure 9.**Variable importance rankings for RF model (global vegetation), based on the mean decrease accuracy (MDA), of the predictor variables. Summer and winter are portrayed on the top and the bottom, respectively. The blue bars represent the means of the importance score among all the folds, and black lines represent the standard deviation.

**Figure 10.**Variable importance ranking (based on the MDA) of each neighboring vegetation variable, using the RF model (global vegetation). Summer and Winter are portrayed on the top and the bottom, respectively. The blue bars represent the mean of the importance score among all the folds, and black lines represent the standard deviation.

**Figure 11.**Partial dependence plot of the categorical variable corresponding to the local vegetation, for the RF model with global vegetation input set. Summer and winter are portrayed on the top and the bottom, respectively. The blue bars represent the means of the importance score among all the folds, and black lines represent the standard deviation.

**Table 1.**List of the predictor variables adopted by the different ML models implemented in this work.

Variable Group | Variable Name | Type | Unit of Measure | Model |
---|---|---|---|---|

Topographic | Elevation | Continuous | [m] | All |

Slope | Continuous | [°] | All | |

Northing | Continuous | - | All | |

Easting | Continuous | - | All | |

Anthropic | Distance from urban areas | Continuous | [m] | All |

Distance from Crops | Continuous | [m] | All | |

Distance from Roads | Continuous | [m] | All | |

Distance from Tracks | Continuous | [m] | All | |

Vegetational | Vegetation (local) | Categorical (30 cat.) | - | RF Global Vegetation, RF local vegetation |

Neighboring vegetation (30 variables) | Continuous | [%] | RF Global Vegetation, RF neighbouring vegetation, SVM, MLP |

**Table 2.**RMSE and AUC values for testing burned area, for both winter and summer, and for all the implemented modes.

Winter | RMSE | AUC | |

Random Forest | Neighboring Vegetation | 0.335 | 0.944 |

Local vegetation | 0.367 | 0.906 | |

Global vegetation | 0.342 | 0.939 | |

SVM | Neighboring Vegetation | 0.36 | 0.916 |

MLP | Neighboring Vegetation | 0.353 | 0.921 |

Summer | RMSE | AUC | |

Random Forest | Neighboring Vegetation | 0.329 | 0.953 |

Local Vegetation | 0.37 | 0.911 | |

Global vegetation | 0.328 | 0.952 | |

SVM | Neighboring Vegetation | 0.358 | 0.931 |

MLP | Neighboring Vegetation | 0.344 | 0.94 |

**Table 3.**Percentages of burned area in the testing subset belonging to different output percentile class ranges for SVM, MLP, and RF with neighboring vegetation. The last line for each season represents the top 25%.

Winter Season | SVM | MLP | RF | ||||

Classes | Total Area (%) | Testing BA | Prob. Value | Testing BA | Prob. Value | Testing BA | Prob. Value |

25% | 25 | 0.42 | 0.13 | 0.48 | 0.13 | 0.27 | 0.10 |

50% | 25 | 2.14 | 0.22 | 1.55 | 0.25 | 1.43 | 0.21 |

75% | 25 | 10.14 | 0.46 | 8.43 | 0.46 | 4.65 | 0.41 |

90% | 15 | 19.97 | 0.74 | 21.67 | 0.68 | 17.67 | 0.67 |

95% | 5 | 19.05 | 0.85 | 18.57 | 0.81 | 17.40 | 0.81 |

100% | 5 | 48.27 | 0.99 | 49.30 | 0.99 | 58.58 | 1.00 |

>75% | 25 | 87.30 | 89.54 | 93.65 | |||

Summer Season | SVM | MLP | RF | ||||

Classes | Total Area (%) | Testing BA | Prob. Value | Testing BA | Prob. Value | Testing BA | Prob. Value |

25% | 25 | 0.34 | 0.09 | 0.10 | 0.08 | 0.18 | 0.05 |

50% | 25 | 1.11 | 0.17 | 1.35 | 0.21 | 0.83 | 0.18 |

75% | 25 | 6.62 | 0.50 | 5.87 | 0.47 | 4.14 | 0.45 |

90% | 15 | 15.99 | 0.77 | 13.82 | 0.69 | 10.22 | 0.69 |

95% | 5 | 19.79 | 0.83 | 16.82 | 0.82 | 14.64 | 0.81 |

100% | 5 | 56.14 | 0.99 | 62.04 | 1.00 | 69.94 | 1.00 |

>75% | 25 | 91.93 | 92.68 | 94.80 |

**Table 4.**Percentage of burned area in the testing subset belonging to different output percentile classes for RF with different vegetation models. The last line for each season represents the top 25% percentile.

Winter Season | Global Vegetation | Neighboring Vegetation | Without Neighboring Vegetation | ||||

Classes | Total Area (%) | Testing BA (%) | Prob. Value | Testing BA (%) | Prob. Value | Testing BA (%) | Prob. Value |

25% | 25 | 0.34 | 0.09 | 0.27 | 0.10 | 0.94 | 0.12 |

50% | 25 | 1.47 | 0.21 | 1.43 | 0.21 | 2.75 | 0.26 |

75% | 25 | 4.70 | 0.43 | 4.65 | 0.41 | 8.72 | 0.46 |

90% | 15 | 18.09 | 0.68 | 17.67 | 0.67 | 23.79 | 0.70 |

95% | 5 | 19.83 | 0.82 | 17.40 | 0.81 | 17.94 | 0.84 |

100% | 5 | 55.59 | 1.00 | 58.58 | 1.00 | 45.84 | 1.00 |

>75% | 93.50 | 93.65 | 87.57 | ||||

Summer Season | Global Vegetation | Neighboring Vegetation | Without Neighboring Vegetation | ||||

Classes | Total Area (%) | Testing BA (%) | Prob. Value | Testing BA (%) | Prob. Value | Testing BA (%) | Prob. Value |

25% | 25 | 0.13 | 0.05 | 0.18 | 0.05 | 0.26 | 0.06 |

50% | 25 | 1.14 | 0.18 | 0.83 | 0.18 | 2.02 | 0.19 |

75% | 25 | 4.04 | 0.46 | 4.14 | 0.45 | 9.50 | 0.53 |

90% | 15 | 10.84 | 0.70 | 10.22 | 0.69 | 21.76 | 0.75 |

95% | 5 | 14.98 | 0.82 | 14.64 | 0.81 | 17.10 | 0.83 |

100% | 5 | 68.85 | 1.00 | 69.94 | 1.00 | 49.34 | 1.00 |

>75% | 94.67 | 94.80 | 88.20 |

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**MDPI and ACS Style**

Trucchia, A.; Izadgoshasb, H.; Isnardi, S.; Fiorucci, P.; Tonini, M. Machine-Learning Applications in Geosciences: Comparison of Different Algorithms and Vegetation Classes’ Importance Ranking in Wildfire Susceptibility. *Geosciences* **2022**, *12*, 424.
https://doi.org/10.3390/geosciences12110424

**AMA Style**

Trucchia A, Izadgoshasb H, Isnardi S, Fiorucci P, Tonini M. Machine-Learning Applications in Geosciences: Comparison of Different Algorithms and Vegetation Classes’ Importance Ranking in Wildfire Susceptibility. *Geosciences*. 2022; 12(11):424.
https://doi.org/10.3390/geosciences12110424

**Chicago/Turabian Style**

Trucchia, Andrea, Hamed Izadgoshasb, Sara Isnardi, Paolo Fiorucci, and Marj Tonini. 2022. "Machine-Learning Applications in Geosciences: Comparison of Different Algorithms and Vegetation Classes’ Importance Ranking in Wildfire Susceptibility" *Geosciences* 12, no. 11: 424.
https://doi.org/10.3390/geosciences12110424