# Forest Fire Susceptibility Assessment and Mapping Using Support Vector Regression and Adaptive Neuro-Fuzzy Inference System-Based Evolutionary Algorithms

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## Abstract

**:**

## 1. Introduction

## 2. Study Area

^{2}. In 2019, its population was estimated at 194,700 capita. As such, it is one of the smallest governorates in Jordan (the second smallest governorate in area after Jerash). Nevertheless, the percentage of the land area of the governorate of Ajloun that is covered by vegetation is the highest among all governorates. The vegetation cover in this governorate is highly variable. It includes forests, cultivated trees, seasonal crops, pastures, and grass.

#### 2.1. Forest Fire Inventory Map and Conditioning Factors

#### 2.1.1. Elevation

#### 2.1.2. Slope

#### 2.1.3. Aspect

#### 2.1.4. Land Use

#### 2.1.5. The Normalized Difference Vegetation Index (NDVI)

#### 2.1.6. Rainfall Rate

#### 2.1.7. Temperature

#### 2.1.8. Wind Speed

#### 2.1.9. Radiation

^{2}. They were categorized into four classes (Figure 2i). A radiation map was then created for the study area.

#### 2.1.10. Soil Texture

#### 2.1.11. Topographic Wetness Index (TWI)

^{2}) and $\alpha $ is the slope gradient in degrees. The values of the TWI in the area under study were positive. They increased as the catchment area increased and the slope gradient decreased. They were classified into three classes (Figure 2k).

#### 2.1.12. Distance to Drainage

#### 2.1.13. Population Density

#### 2.1.14. Distance to Roads

## 3. Methods

#### 3.1. Adaptive Neuro-Fuzzy Inference System (ANFIS)

#### 3.1.1. The First Layer

_{A}represents a membership function. Meanwhile, the parameters a

_{i}, b

_{i}, and c

_{i}are parameter of the triangular membership function, also commonly known as the premise parameters.

#### 3.1.2. The Second Layer

#### 3.1.3. The Third Layer

#### 3.1.4. The Fourth Layer

#### 3.1.5. The Fifth Layer

#### 3.2. Support Vector Regression (SVR)

_{i}[54]. This regression method is based on statistical learning. It uses structural risk minimization (SRM) and the Vapnik–Cherkassky theory in the modeling [55]. An important feature of any statistical-based learning approach is extensibility of the statistical learning, which is a notable advantage of SVMs [56]. In this model, the objective is to specify the value of the function f, which can be obtained from Equation (10):

#### 3.3. The Genetic Algorithm (GA)

#### 3.4. Shuffled Frog-Leaping Algorithm (SFLA)

- At first, an initial population is randomly generated to represent a number of solutions, N, for the problem, and, then, a fitness function is defined to allocate a fitness value to each solution in this population.
- The population members are sorted in an ascending order based on their fitness values.
- The obtained solutions are divided into m sub-groups, named memeplexes, containing n solutions. To assign the solutions to the memeplexes, the first solution with the highest fitness value is allocated to the first memeplex, the second solution is assigned to the second memeplex, and the m-th solution is allotted to the m-th memeplex. Subsequently, the (m + 1)th solution is allocated to the first memeplex. This allocation process continues until n solutions are assigned to each of the m sub-groups.
- To perform a local search at this stage, the position of the ith solution is first determined in each memeplex based on differences in fitness values of each ith solution from the best fitness (${X}_{b}$) and the worst fitness (${X}_{w}$) values using the following equation:

- 5.
- After finishing the local search, all the population members are combined and sorted in descending order of fitness values. The population is, again, divided into several sub-groups and the aforementioned procedure continues until the stopping criterion (e.g., the number of iterations and/or value of a specific error measure) is met, in which case the SFLA finishes its operation and the solution having the highest fitness value is returned as the best solution.

## 4. Proposed Methodology

- Preparing training data;
- Creating the basic fuzzy model;
- Adjusting values of the premise and consequent parameters of the basic fuzzy model using the error function and the two optimization methods, GA and SFLA; and
- Identifying the fuzzy system with the best values of the parameters as a final result.

## 5. Results

^{2}values for the training and testing data subsets. A comparison between the RMSE values of the SVR- and ANFIS-based hybrid models indicates that both the SVR–GA and SVR–SFLA models have relatively low RMSE values.

## 6. Discussion

## 7. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 2.**Conditioning factors for forest fire susceptibility mapping: (

**a**) elevation, (

**b**) slope, (

**c**) aspect, and (

**d**) land use. Conditioning factors for forest fire susceptibility mapping: (

**e**) NDVI, (

**f**) rainfall, (

**g**) temperature, and (

**h**) wind speed. Conditioning factors for forest fire susceptibility mapping: (

**i**) radiation; (

**j**) soil texture; (

**k**) TWI; and (

**l**) distance to drainage. Conditioning factors for forest fire susceptibility mapping: (

**m**) population density; and (

**n**) distance to roads.

**Figure 4.**Target and output values of the ANFIS–GA model for the training and testing, respectively, (

**a**,

**b**), the MSE and RMSE values of the training and testing samples for ANFIS–GA models, respectively (

**c**,

**e**), frequency of errors of the training and testing samples for ANFIS–GA model, respectively (

**d**,

**f**).

**Figure 5.**Target and output values of the ANFIS–SFLA model for the training and testing, respectively, (

**a**,

**b**), the MSE and RMSE values of the training and testing samples for ANFIS–SFLA models, respectively (

**c**,

**e**), frequency of errors of the training and testing samples for ANFIS–SFLA model, respectively (

**d**,

**f**).

**Figure 7.**The receiver operating characteristic curves of the four hybrid models in the training and testing runs.

**Figure 8.**The forest fire susceptibility maps obtained from the SVR–GA, SVR–SFLA, ANFIS–GA, and ANFIS–SFLA models.

Hyper-Parameter | GA | SFLA |
---|---|---|

Cost (C) | 8 | 0.790 |

Gamma (γ) | 0.03125 | 0.90473 |

RMSE (training) | 0.2826 | 0.2881 |

RMSE (testing) | 0.3831 | 0.3969 |

R^{2} (training) | 0.79 | 0.76 |

R^{2} (testing) | 0.68 | 0.66 |

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**MDPI and ACS Style**

Mabdeh, A.N.; Al-Fugara, A.; Khedher, K.M.; Mabdeh, M.; Al-Shabeeb, A.R.; Al-Adamat, R.
Forest Fire Susceptibility Assessment and Mapping Using Support Vector Regression and Adaptive Neuro-Fuzzy Inference System-Based Evolutionary Algorithms. *Sustainability* **2022**, *14*, 9446.
https://doi.org/10.3390/su14159446

**AMA Style**

Mabdeh AN, Al-Fugara A, Khedher KM, Mabdeh M, Al-Shabeeb AR, Al-Adamat R.
Forest Fire Susceptibility Assessment and Mapping Using Support Vector Regression and Adaptive Neuro-Fuzzy Inference System-Based Evolutionary Algorithms. *Sustainability*. 2022; 14(15):9446.
https://doi.org/10.3390/su14159446

**Chicago/Turabian Style**

Mabdeh, Ali Nouh, A’kif Al-Fugara, Khaled Mohamed Khedher, Muhammed Mabdeh, Abdel Rahman Al-Shabeeb, and Rida Al-Adamat.
2022. "Forest Fire Susceptibility Assessment and Mapping Using Support Vector Regression and Adaptive Neuro-Fuzzy Inference System-Based Evolutionary Algorithms" *Sustainability* 14, no. 15: 9446.
https://doi.org/10.3390/su14159446