# Modeling the Susceptibility of Forest Fires Using a Genetic Algorithm: A Case Study in Mountain Areas of Southwestern China

Authors: Zhong Zheng; Yanghua Gao; Ju Zhang; Zhijun Chen
Journal: Scientific Programming
 (2022)

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## Abstract

Modeling fire susceptibility in fire-prone areas of forest ecosystems was essential for providing guidance to implement prevention and control measures of forest fires. Traditional models were developed on the basis of random selection of absence data (i.e., nonfire data from unburned areas), which could bring uncertainties to modeling results. Here, a new model with the genetic algorithm for Rule-set Production (GARP) algorithm and 10 environmental layers was proposed to process presence-only data in the susceptibility modeling of forest fires in Chongqing city. To do this, 70% of 684 fire occurrence data (479) during the period of 2000–2018 were applied to train the proposed model. And, 30% of these fire occurrence data (205) and the same amount of no-fire data (205) were emerged as validation dataset. The results showed that, for some environmental layers (i.e., distance to the nearest road, land cover, precipitation, distance to the nearest settlement, aspect, relative humidity, elevation, wind speed, and temperature), theirP values were less than 0.05, indicating that these 9 environmental layers have significant influence on the spatial distribution of fire susceptibility in Chongqing city. On the contrary, with a higher P value (i.e., 0.126), the slope layer has an insignificant effect on fire susceptibility in the study area. Furthermore, the results of receiver operating characteristic analysis (ROC) showed that the proposed model has a good performance with an AUC value of 0.869, an accuracy value of 0.732, a sensitivity value of 0.59, a specificity value of 0.873, a positive predictive value of 0.823, and a negative predictive value of 0.681. This study revealed the validity of the proposed model in modeling the susceptibility of forest fires.


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## Body

## 1. Introduction

With intensive global warming and increasing urbanization, forest fires around the world occurred frequently in recent years, resulting in severe impacts on the safety of forest ecosystems [1, 2]. The geographical distribution of fire susceptibility was an urgent need to implement adequate management actions [3–6]. It was of importance to perform studies on modeling the susceptibility of forest fires [7, 8].Meteorological factors (e.g., relative humidity, wind speed, air temperature, and precipitation) were thought to be important contributors to fire occurrence, which have already been adopted to forecast the fire risk in operational systems [9–12]. However, the occurrence of forest fires was also result of other environmental factors (e.g., land cover, topography, and human activity) [13, 14]. Thus, comprehensive models involving multiple factors were emerging in the development of modeling fire susceptibility [15, 16].The similar development is also seen in the field of landslide susceptibility mapping [17]. Some studies reported that machine learning algorithms (e.g., random forest [18, 19], support vector machine [18], boosted regression tree [18], naïve Bayes classifier [20], and extreme gradient boosting [19, 20]) could be used for building comprehensive models.Therefore, recent advances have mainly focused on utilizing machine learning algorithms to develop comprehensive models in fire susceptibility modeling. For instance, to map the fire susceptibility in Iran, Pourtaghi, literature [21] proposed Shannon’s entropy and frequency ratio models for combining several environmental factors (i.e., topography data, soil texture, proximity data, normalized difference vegetation index, land use map, and meteorological data). Following that, literature [22] applied boosted regression tree, generalized additive model, and random forest to investigate the importance of 15 condition factors on forest fire and model fire susceptibility. Taking 10 ignition factors into consideration, literature [23] developed a fire susceptibility model using the neural fuzzy inference system at a tropical area. Thereafter, literature [15] established an evolutionary optimized gradient boosted decision tree model by incorporating 18 ignition factors of forest fires. In 2019, literature [24] utilized 10 related conditioning factors and occurrences data of previous fires to train the LogitBoost ensemble-based decision tree machine learning method for mapping the fire susceptibility at the Lao Cai region of Vietnam. Based on historical fire data and 8 risk factors, literature [16] proposed a new operational model with the ant-miner algorithm to predict the risk of forest fires in cloud-rich areas of southwestern China. More recently, involving 12 influencing factors, literature [25] employed a weighted approach to integrate the forest fire susceptibility outputs of different machine learning techniques in a forest-agriculture mosaic landscape of southern India.Since the above models could not handle the presence-only data directly, a certain number of absence data (i.e., no-fire data from unburned areas) were generally required. However, areas lacking records of fire occurrence might not truly represent areas of fire absence [26, 27]. Therefore, the selection of no-fire data would bring uncertainties to modeling results. Recently, it was reported that genetic algorithm for rule-set production (GARP) could be a robust model, which could effectively deal with presence-only data [28–30]. Here, we made the attempt to apply GARP to propose a new model for modeling fire susceptibility.Thus, the main objective is to propose a genetic algorithm-based model, which could process the presence-only data for mapping the susceptibility of forest fire. Furthermore, the susceptibility of forest fire in a mountain area of southwestern China was mapped and then was evaluated using a receiver operating characteristic analysis. Additionally, we try to investigate which environmental layers have significant influence on the susceptibility of forest fires in the study area. As a new attempt to process the presence-only fire occurrence data, the proposed model has the potential of being applied in other southwest regions of China and could provide guidance for the implementation of forest fire prevention and control measures.


## 2. Study Area

With an area of around 82,402 km2, Chongqing city was selected in this research as the study area (see Figure 1). It was located in southwestern China, which was bounded by 105° 03′ to 110° 02′ E longitudes and 28°02′ to 32° 03′ N latitudes. This area was mainly comprised of high mountains and deep valleys with an altitude ranging from 73 to 2,797 m. It possesses a continental humid subtropical monsoon climate with an average relative humidity of 71–81%, an annual rainfall of 1,000–1,350 mm, and an annual average temperature of 16–18°C. The evergreen broad-leaved forest was the dominant vegetation type in the study area.Figure 1
Locations of the study area and fire data.Due to its topographic, geological, and climate features, Chongqing city has experienced a more frequent occurrence of forest fires. This region was a typical area for carrying out studies on modeling the susceptibility of forest fires in mountain areas of southwestern China.


## 3. Materials and Methods

### 3.1. Input Fire Occurrence Data

With authorization access, fire occurrence data during the period 2000–2018 were applied for further studies. Locations of these fires were sourced from an operational monitoring system that was based on MODIS images and field surveys. Fire occurrence data includes attributes of latitude, longitude, and ignition time, which were accessed from Forest Fire Control Office of Chongqing and Forest Fire Protection Network of China in Chongqing city. In total, the number of fire occurrence data was 684.Out of 684 fire data, 479 (70%) were randomly selected as the training sample and the remaining 205 (30%) were kept for validation purpose. Since GARP belongs to presence-only approach, which does not need the existence of no-fire data in training phase, only the same amount of no-fire data (205) were randomly sampled for model validation [31]. Finally, the total number of data used in this study was 889 (i.e., training sample: 479 and validation sample: 410), which is plotted in Figure 1.


### 3.2. Environmental Layers

Four groups of environmental layers influencing fire occurrence (i.e., annual weather, land cover, topographic, and human activity data) were used in the GARP approach (see in Table1).Table 1
The details of data used in this study.
CategoryDataTypeResolution/scaleSourceFireIgnition——The Forest Fire Control Office of Chongqing and Forest Fire Protection Network of ChinaAnnual weatherRelative humidityContinuous—The National Meteorological Network of China (https://data.cma.cn)Wind speedContinuous—TemperatureContinuous—PrecipitationContinuous—Land coverLand cover typeDiscrete30 mThe Resource and Environment Data Cloud Platform (https://www.resdc.cn/)TopographyElevationContinuous30 mGeospatial data cloud website (https://www.gscloud.cn/)AspectDiscrete30 mSlopeContinuous30 mHuman activityDistance to the nearest roadDiscrete200 mBaidu map (https://www.lbsyun.baidu.com/index.php)Distance to the nearest settlementDiscrete1 kmDaily weather data at 12 national stations across the study area were obtained first from the National Meteorological Network of China (https://data.cma.cn). Annual weather data (i.e., relative humidity, wind speed, temperature, and precipitation) were then calculated using daily weather observations during 2000–2018. Finally, annual weather data were interpolated to a grid of 1 km spatial resolution using the inverse distance weight method. Interpolations of annual relative humidity, annual wind speed, annual temperature, and annual precipitation were regarded as environmental layers in the following GARP approach.Land cover was a crucial factor for fire occurrence, since it could provide specific information on the type and distribution of vegetation. Land cover data used in this study were obtained from the Resource and Environment Data Cloud Platform (https://www.resdc.cn/). These data have 1-km spatial resolution, which was an updated version of 2015 land cover data based on Landsat 8 remote sensing images in 2018. Land cover was regarded as one of the other environmental layers in the following GARP approach, which is illustrated in Figure 2(e).Figure 2
Environmental layers influenced the occurrence of forest fires: (a) relative humidity, (b) wind speed, (c) temperature, (d) precipitation, (e) land cover, (f) elevation, (g) aspect, (h) slope, (i) distance to the nearest settlement, and (j) distance to the nearest road.Topography layers were considered other causes of forest fires. In this research, 20 digital elevation model images (ASTGTM2) with a spatial resolution of 30 m were downloaded from the Geospatial Data Cloud website (https://www.gscloud.cn/). To extract the elevation layer, ASTGTM2 data was were preprocessed using image mosaic and clipping operations in spatial analyst tools of ArcGIS software (version 10.2). Moreover, for matching the spatial resolution of other environmental layers, the bilinear interpolation was applied to resample the spatial resolution of elevation data (30 m) to 1 km. Based on resampled data, “Surface” tool of ArcGIS software was then employed to extract the other two topography layers (i.e., aspect and slope).In addition, two other environmental layers (i.e., distance to the nearest settlement and distance to the nearest road) were used to assess the influence of human activity on fire occurrence. Based on settlement distribution and road network data in Chongqing city, we calculated the distance to the nearest settlement and the distance to the nearest road using a buffer analysis tool of ArcGIS software. These calculated layers were then discretized into equal intervals (i.e., distance to the nearest settlement: 0-1 km, 1-2 km, 2-3 km, 3-4 km, and >4 km and distance to the nearest road: 0–200 m, 200–400 m, 400–600 m, 600–800 m, and >800 m), according to our previous experience in this area [16].


### 3.3. Developing GARP Model

Through DesktopGARP program (version 1.1.6), a genetic algorithm for rule-set prediction (GARP) was performed in this study to model fire susceptibility (see in Figure3). As an expert system and machine learning algorithm, this algorithm could develop a set of rules to relate fire occurrence data to environmental layers. These rules then could be projected onto a landscape and applied to model the fire susceptibility [32].Figure 3
The flowchart of the proposed model: (a) data and processing, (b) model developing, and (c) model evaluating.Before developing the GARP model, a jackknifing procedure was carried out to determine which environmental layers were more significant. This process was accomplished using an “environmental layers panel” in DesktopGARP, which could generate a set of tasks with various combinations of environmental layers. After running these tasks, a multiple linear regression was run to understand which environmental layers have a significant impact on resulting errors (https://www.nhm.ku.edu/desktopgarp/PrinterUM.html). Based on the analysis results, significant environmental layers were designated for subsequent experiments.To optimize the parameters of the GARP approach, 200 models with max 1,000 iterations and a convergence limit of 0.01 were established in DesktopGARP (see Table2). During the process of model building, the training sample (i.e., 479 data mentioned above) was internally divided into a 70%/30%. Then, 20 best models under 10% hard omission threshold and 50% commission threshold were selected using a subset approach in DesktopGARP. Finally, based on commission errors of these 20 subset models, output maps of 10 optimal models were imported into ArcMap software and summated using a raster calculator tool of spatial analyst extension. In the summated map, a higher pixel value means a greater potential of forest fires.Table 2
Parameters setting in DesktopGARP.
Optimization parametersBest subset selection parametersRuns200Omission measure typeExtrinsic-hardConvergence limit0.01Omission threshold10Maximum iterations1000Commission threshold50Rule typesAtomicTotal models under hard omission threshold20RangeNegated range logistic regression


### 3.4. Evaluating Model’s Performance

Based on the validation sample (i.e., 410 data mentioned above), the performance of the proposed model was evaluated using a receiver operating characteristic analysis (ROC). Area under curve (AUC) was measured and additional metrics could be calculated using the following formulas[24, 33]:(1)accuracy=TP+TNTP+FP+FN+TN,sensitivity=TPTP+FN,specificity=TNTN+FP,positive predictive value=TPTP+FP,negative predictive value=TNTN+FN,where TP is the true positive, FP is the false positive, FN is the false negative, and TN is the true negative


## 3.1. Input Fire Occurrence Data

With authorization access, fire occurrence data during the period 2000–2018 were applied for further studies. Locations of these fires were sourced from an operational monitoring system that was based on MODIS images and field surveys. Fire occurrence data includes attributes of latitude, longitude, and ignition time, which were accessed from Forest Fire Control Office of Chongqing and Forest Fire Protection Network of China in Chongqing city. In total, the number of fire occurrence data was 684.Out of 684 fire data, 479 (70%) were randomly selected as the training sample and the remaining 205 (30%) were kept for validation purpose. Since GARP belongs to presence-only approach, which does not need the existence of no-fire data in training phase, only the same amount of no-fire data (205) were randomly sampled for model validation [31]. Finally, the total number of data used in this study was 889 (i.e., training sample: 479 and validation sample: 410), which is plotted in Figure 1.


## 3.2. Environmental Layers

Four groups of environmental layers influencing fire occurrence (i.e., annual weather, land cover, topographic, and human activity data) were used in the GARP approach (see in Table1).Table 1
The details of data used in this study.
CategoryDataTypeResolution/scaleSourceFireIgnition——The Forest Fire Control Office of Chongqing and Forest Fire Protection Network of ChinaAnnual weatherRelative humidityContinuous—The National Meteorological Network of China (https://data.cma.cn)Wind speedContinuous—TemperatureContinuous—PrecipitationContinuous—Land coverLand cover typeDiscrete30 mThe Resource and Environment Data Cloud Platform (https://www.resdc.cn/)TopographyElevationContinuous30 mGeospatial data cloud website (https://www.gscloud.cn/)AspectDiscrete30 mSlopeContinuous30 mHuman activityDistance to the nearest roadDiscrete200 mBaidu map (https://www.lbsyun.baidu.com/index.php)Distance to the nearest settlementDiscrete1 kmDaily weather data at 12 national stations across the study area were obtained first from the National Meteorological Network of China (https://data.cma.cn). Annual weather data (i.e., relative humidity, wind speed, temperature, and precipitation) were then calculated using daily weather observations during 2000–2018. Finally, annual weather data were interpolated to a grid of 1 km spatial resolution using the inverse distance weight method. Interpolations of annual relative humidity, annual wind speed, annual temperature, and annual precipitation were regarded as environmental layers in the following GARP approach.Land cover was a crucial factor for fire occurrence, since it could provide specific information on the type and distribution of vegetation. Land cover data used in this study were obtained from the Resource and Environment Data Cloud Platform (https://www.resdc.cn/). These data have 1-km spatial resolution, which was an updated version of 2015 land cover data based on Landsat 8 remote sensing images in 2018. Land cover was regarded as one of the other environmental layers in the following GARP approach, which is illustrated in Figure 2(e).Figure 2
Environmental layers influenced the occurrence of forest fires: (a) relative humidity, (b) wind speed, (c) temperature, (d) precipitation, (e) land cover, (f) elevation, (g) aspect, (h) slope, (i) distance to the nearest settlement, and (j) distance to the nearest road.Topography layers were considered other causes of forest fires. In this research, 20 digital elevation model images (ASTGTM2) with a spatial resolution of 30 m were downloaded from the Geospatial Data Cloud website (https://www.gscloud.cn/). To extract the elevation layer, ASTGTM2 data was were preprocessed using image mosaic and clipping operations in spatial analyst tools of ArcGIS software (version 10.2). Moreover, for matching the spatial resolution of other environmental layers, the bilinear interpolation was applied to resample the spatial resolution of elevation data (30 m) to 1 km. Based on resampled data, “Surface” tool of ArcGIS software was then employed to extract the other two topography layers (i.e., aspect and slope).In addition, two other environmental layers (i.e., distance to the nearest settlement and distance to the nearest road) were used to assess the influence of human activity on fire occurrence. Based on settlement distribution and road network data in Chongqing city, we calculated the distance to the nearest settlement and the distance to the nearest road using a buffer analysis tool of ArcGIS software. These calculated layers were then discretized into equal intervals (i.e., distance to the nearest settlement: 0-1 km, 1-2 km, 2-3 km, 3-4 km, and >4 km and distance to the nearest road: 0–200 m, 200–400 m, 400–600 m, 600–800 m, and >800 m), according to our previous experience in this area [16].


## 3.3. Developing GARP Model

Through DesktopGARP program (version 1.1.6), a genetic algorithm for rule-set prediction (GARP) was performed in this study to model fire susceptibility (see in Figure3). As an expert system and machine learning algorithm, this algorithm could develop a set of rules to relate fire occurrence data to environmental layers. These rules then could be projected onto a landscape and applied to model the fire susceptibility [32].Figure 3
The flowchart of the proposed model: (a) data and processing, (b) model developing, and (c) model evaluating.Before developing the GARP model, a jackknifing procedure was carried out to determine which environmental layers were more significant. This process was accomplished using an “environmental layers panel” in DesktopGARP, which could generate a set of tasks with various combinations of environmental layers. After running these tasks, a multiple linear regression was run to understand which environmental layers have a significant impact on resulting errors (https://www.nhm.ku.edu/desktopgarp/PrinterUM.html). Based on the analysis results, significant environmental layers were designated for subsequent experiments.To optimize the parameters of the GARP approach, 200 models with max 1,000 iterations and a convergence limit of 0.01 were established in DesktopGARP (see Table2). During the process of model building, the training sample (i.e., 479 data mentioned above) was internally divided into a 70%/30%. Then, 20 best models under 10% hard omission threshold and 50% commission threshold were selected using a subset approach in DesktopGARP. Finally, based on commission errors of these 20 subset models, output maps of 10 optimal models were imported into ArcMap software and summated using a raster calculator tool of spatial analyst extension. In the summated map, a higher pixel value means a greater potential of forest fires.Table 2
Parameters setting in DesktopGARP.
Optimization parametersBest subset selection parametersRuns200Omission measure typeExtrinsic-hardConvergence limit0.01Omission threshold10Maximum iterations1000Commission threshold50Rule typesAtomicTotal models under hard omission threshold20RangeNegated range logistic regression


## 3.4. Evaluating Model’s Performance

Based on the validation sample (i.e., 410 data mentioned above), the performance of the proposed model was evaluated using a receiver operating characteristic analysis (ROC). Area under curve (AUC) was measured and additional metrics could be calculated using the following formulas[24, 33]:(1)accuracy=TP+TNTP+FP+FN+TN,sensitivity=TPTP+FN,specificity=TNTN+FP,positive predictive value=TPTP+FP,negative predictive value=TNTN+FN,where TP is the true positive, FP is the false positive, FN is the false negative, and TN is the true negative


## 4. Results

### 4.1. Significant Layers Influencing Fire Susceptibility

Initially, 10 environmental layers influencing fire occurrence were selected to develop the GARP algorithm. However, to improve the effectiveness of proposed model, multiple linear regression analysis results in the removal of one environmental layer (i.e., slope). Specifically, the impact of environmental layers could be identified byP values in the multiple linear regression analysis. A smaller P value means a more significance of the environmental layer in modeling the fire susceptibility [34].The results in Table3 showed that P values of these layers respectively are as follows: distance to the nearest road (P<0.001), land cover (P<0.001), precipitation (P<0.001), distance to the nearest settlement (P<0.001), aspect (P<0.001), relative humidity (P<0.001), elevation (P=0.004), wind speed (P=0.005), and temperature (P=0.014). All P values of these environmental layers were less than 0.05, indicating that the susceptibility of forest fires in Chongqing city was significantly influenced by human activities, land cover, and weather factors.Table 3
Environmental layers influencing the susceptibility of forest fires.
Environmental layersCoefficientsStandard deviationt StatP valueEffectIncluded or excluded(Intercept)44.1650.45297.634P<0.001SignificantIncludedDistance to the nearest road−12.1760.271−44.856P<0.001SignificantIncludedLand cover−9.5740.271−35.270P<0.001SignificantIncludedPrecipitation−3.1040.271−11.434P<0.001SignificantIncludedDistance to the nearest settlement−2.9870.271−11.004P<0.001SignificantIncludedAspect−2.1000.271−7.738P<0.001SignificantIncludedRelative humidity−1.4970.271−5.513P<0.001SignificantIncludedElevation0.7810.2712.8770.004SignificantIncludedWind speed0.7600.2712.8010.005SignificantIncludedTemperature0.6640.2712.4460.014SignificantIncludedSlope−0.4150.271−1.5300.126InsignificantExcludedNote: environmental layers are ranked byP value.However, Table3 further shows that the slope layer has a higher P value (i.e., 0.126 > 0.05). This means that slope has an insignificant impact on the susceptibility of forest fires in Chongqing city. As a result, the susceptibility of forest fires was mapped using 9 environmental layers (i.e., distance to the nearest road, land cover, precipitation, distance to the nearest settlement, aspect, relative humidity, elevation, wind speed, and temperature), followed by performance evaluation.


### 4.2. Susceptibility Map of Forest Fires

After running the subset approach in DesktopGARP, 10 optimal models were developed and summated using ArcMap software. After that, a composite map was generated to illustrate the spatial distribution of fire susceptibility, in which a higher pixel value means greater model agreements and a higher fire risk level. Finally, the fire susceptibility map was categorized into 6 classes: very low, low, moderate, high, very high, and extremely high risks.From Figure4, it can be seen that high risk of forest fires (i.e., model agreements were higher than 8) primarily distributed in southwestern, central, and northeastern parts of the study area. This distribution was similar to that of human activity factors (i.e., distance to the nearest road in Figure 2(i) and distance to the nearest settlement in Figure 2(j)), which verifies a fact in Table 2 that distance to the nearest road and distance to the nearest settlement were more significant layers influencing the fire susceptibility.Figure 4
Susceptibility map of forest fires using the proposed model.On the contrary, Figure4 displays that the risk of forest fires was low in the southeastern region of Chongqing city, where human activity was high and vegetation was rich. It is evident that a higher precipitation in this region (see Figure 2(d)) resulted in a changing distribution of forest fires. Also, the results in Table 2 verified that the precipitation layer was an important layer influencing the fire occurrence.For the susceptibility map of forest fires (Figure4), the area and percentage associated with each risk level can be found in Table 4. Specifically, 11.44% and 8.44% out of the whole study area were located in extremely high and very high fire susceptible areas. Moreover, 9.36% and 6.74% were located in high and moderate fire susceptible areas, respectively. Furthermore, 4.39% and 59.64% were located in very low and low fire susceptible areas, respectively.Table 4
Area and percentage of each risk level in the susceptibility map of forest fires.
ClassArea (km2)PercentageVery low risk4651159.64Low risk34204.39Moderate risk52556.74High risk73009.36Very high risk65798.44Extremely high risk892011.44


### 4.3. Model Performance

As an effective technique to measure model’s performance [18], a ROC analysis was applied in this study to evaluate the performance of the proposed model. In particular, based on the validation dataset mentioned above, the ROC curve was illustrated by plotting the false-positive fraction of the proposed model on x-axis and true-positive fraction of the proposed model on the y-axis. If the proposed model has no effect on fire susceptibility modeling, the ROC curve is close to the upper left corner (i.e., 1 : 1 line) and its AUC value will be 0.5 [11]. A value between 0.85 and 0.95 means a good performance of the proposed model [31].The results in Figure5 showed that the AUC value was 0.869, which indicates that the GARP model has a good performance. Moreover, Figure 5 demonstrates other 5 metrics to measure model’s performance, that is, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value, separately. Figure 5 shows that accuracy was 0.732, sensitivity was 0.59, specificity was 0.873, PPV was 0.823, and NPV was 0.681. These additional quantitative metrics further suggested the validity of the proposed model.Figure 5
Performance evaluation of the proposed model using ROC analysis.


## 4.1. Significant Layers Influencing Fire Susceptibility

Initially, 10 environmental layers influencing fire occurrence were selected to develop the GARP algorithm. However, to improve the effectiveness of proposed model, multiple linear regression analysis results in the removal of one environmental layer (i.e., slope). Specifically, the impact of environmental layers could be identified byP values in the multiple linear regression analysis. A smaller P value means a more significance of the environmental layer in modeling the fire susceptibility [34].The results in Table3 showed that P values of these layers respectively are as follows: distance to the nearest road (P<0.001), land cover (P<0.001), precipitation (P<0.001), distance to the nearest settlement (P<0.001), aspect (P<0.001), relative humidity (P<0.001), elevation (P=0.004), wind speed (P=0.005), and temperature (P=0.014). All P values of these environmental layers were less than 0.05, indicating that the susceptibility of forest fires in Chongqing city was significantly influenced by human activities, land cover, and weather factors.Table 3
Environmental layers influencing the susceptibility of forest fires.
Environmental layersCoefficientsStandard deviationt StatP valueEffectIncluded or excluded(Intercept)44.1650.45297.634P<0.001SignificantIncludedDistance to the nearest road−12.1760.271−44.856P<0.001SignificantIncludedLand cover−9.5740.271−35.270P<0.001SignificantIncludedPrecipitation−3.1040.271−11.434P<0.001SignificantIncludedDistance to the nearest settlement−2.9870.271−11.004P<0.001SignificantIncludedAspect−2.1000.271−7.738P<0.001SignificantIncludedRelative humidity−1.4970.271−5.513P<0.001SignificantIncludedElevation0.7810.2712.8770.004SignificantIncludedWind speed0.7600.2712.8010.005SignificantIncludedTemperature0.6640.2712.4460.014SignificantIncludedSlope−0.4150.271−1.5300.126InsignificantExcludedNote: environmental layers are ranked byP value.However, Table3 further shows that the slope layer has a higher P value (i.e., 0.126 > 0.05). This means that slope has an insignificant impact on the susceptibility of forest fires in Chongqing city. As a result, the susceptibility of forest fires was mapped using 9 environmental layers (i.e., distance to the nearest road, land cover, precipitation, distance to the nearest settlement, aspect, relative humidity, elevation, wind speed, and temperature), followed by performance evaluation.


## 4.2. Susceptibility Map of Forest Fires

After running the subset approach in DesktopGARP, 10 optimal models were developed and summated using ArcMap software. After that, a composite map was generated to illustrate the spatial distribution of fire susceptibility, in which a higher pixel value means greater model agreements and a higher fire risk level. Finally, the fire susceptibility map was categorized into 6 classes: very low, low, moderate, high, very high, and extremely high risks.From Figure4, it can be seen that high risk of forest fires (i.e., model agreements were higher than 8) primarily distributed in southwestern, central, and northeastern parts of the study area. This distribution was similar to that of human activity factors (i.e., distance to the nearest road in Figure 2(i) and distance to the nearest settlement in Figure 2(j)), which verifies a fact in Table 2 that distance to the nearest road and distance to the nearest settlement were more significant layers influencing the fire susceptibility.Figure 4
Susceptibility map of forest fires using the proposed model.On the contrary, Figure4 displays that the risk of forest fires was low in the southeastern region of Chongqing city, where human activity was high and vegetation was rich. It is evident that a higher precipitation in this region (see Figure 2(d)) resulted in a changing distribution of forest fires. Also, the results in Table 2 verified that the precipitation layer was an important layer influencing the fire occurrence.For the susceptibility map of forest fires (Figure4), the area and percentage associated with each risk level can be found in Table 4. Specifically, 11.44% and 8.44% out of the whole study area were located in extremely high and very high fire susceptible areas. Moreover, 9.36% and 6.74% were located in high and moderate fire susceptible areas, respectively. Furthermore, 4.39% and 59.64% were located in very low and low fire susceptible areas, respectively.Table 4
Area and percentage of each risk level in the susceptibility map of forest fires.
ClassArea (km2)PercentageVery low risk4651159.64Low risk34204.39Moderate risk52556.74High risk73009.36Very high risk65798.44Extremely high risk892011.44


## 4.3. Model Performance

As an effective technique to measure model’s performance [18], a ROC analysis was applied in this study to evaluate the performance of the proposed model. In particular, based on the validation dataset mentioned above, the ROC curve was illustrated by plotting the false-positive fraction of the proposed model on x-axis and true-positive fraction of the proposed model on the y-axis. If the proposed model has no effect on fire susceptibility modeling, the ROC curve is close to the upper left corner (i.e., 1 : 1 line) and its AUC value will be 0.5 [11]. A value between 0.85 and 0.95 means a good performance of the proposed model [31].The results in Figure5 showed that the AUC value was 0.869, which indicates that the GARP model has a good performance. Moreover, Figure 5 demonstrates other 5 metrics to measure model’s performance, that is, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value, separately. Figure 5 shows that accuracy was 0.732, sensitivity was 0.59, specificity was 0.873, PPV was 0.823, and NPV was 0.681. These additional quantitative metrics further suggested the validity of the proposed model.Figure 5
Performance evaluation of the proposed model using ROC analysis.


## 5. Discussion

### 5.1. Result Analysis

Unlike traditional models [16, 21, 22, 24], the proposed model does not require the input of no-fire data. This means that additional uncertainties caused by the selection of no-fire data would not affect the modeling results, since forest areas lacking records of fire occurrence might not truly represent areas of fire absence [26, 27]. Our obtained results also further verified that the proposed model has a good performance with an AUC value of 0.869 and accuracy value of 0.732, which were close to results of our previous studies in this area (i.e., accuracy values were from 0.58 to 0.79) [16].However, for the proposed model, modeling tasks in this research might be more challenging. Specifically, due to input limitations of DesktopGARP, annual weather data were applied as model inputs. Compared to daily weather data used in [16], the annual weather data might not be accurate indicators of weather conditions on the day of fire occurrence. Therefore, if daily weather data were applied in future studies, there was a further space for improving the accuracy of proposed model.Compared to maximum entropy models (i.e., a different presence-only model [35]), the proposed model in this study has a higher accuracy. In particular, the maximum entropy models (Maxent) have been utilized to develop a new daily fire danger index [36], to predict the possibility of lightning-caused fire occurrence [37] and to examine the spatial distribution and temporal variation in forest fire occurrence [38]. Accuracies of these studies in terms of AUC value were 0.752 to 0.852 [36], 0.866 [37], and 0.816 to 0.847 [38]. These reported studies further verified the validity of the GARP model in modeling the susceptibility of forest fires.Moreover, the results in Table2 showed that the slope layer has an insignificant influence on fire occurrence in this research, even though slope could affect the fire spread rate [21, 24]. This is consistent with findings by Dlamini [26]. The main reason might be that the topography of Chongqing city (i.e., a famous mountain city in China) was dominated by high mountains and deep valleys (see Figure 2(h)). Figure 1 exhibits that fire occurrence data were primarily distributed in mountainous areas with steeper slopes. With low variations at these fire data locations [26], slope factor would have an insignificant effect on the modeling results.Findings in Figure2, Figure 4, and Table 2 indicated that human activities significantly influence the susceptibility of forest fires. The spatial distributions of high risks were similar to spatial distributions of distance to the nearest road in Figure 2(i) and distance to the nearest settlement in Figure 2(j). In a mountain city like Chongqing city, road network and settlement distribution mean an easy accessibility of facilities and services [39]. Therefore, these two factors could effectively reflect the intensity of human activities, which could significantly impact on fire occurrence.Similar to the results in some reported studies [5, 16, 23], findings in Figure 4 and Table 2 indicated that land cover and weather factors greatly affect the fire susceptibility. The primary cause of this was that land cover and weather factors have great influences on fuel moisture and loading [26].


### 5.2. Improvement Strategy

Based on DesktopGARP program, a new model using the genetic algorithm was proposed in this study to model the susceptibility of forest fires. As a new attempt to process presence-only fire occurrence data, the proposed model would not bring uncertainties to the modeling results. And, our findings suggested that the proposed model has a good performance in modeling the susceptibility of forest fires. However, these findings should be interpreted guardedly.The proposed method achieved better results in a mountain area of southwestern China. To further verify its validity, more studies need to be carried out in a wider range of forest ecosystems. Meanwhile, beyond environmental layers used in this study (i.e., annual weather, land cover, topographic, and human activity factors), more factors influencing the occurrence of forest fires should be considered in the building of the susceptibility model in future research.Moreover, accurate weather data were critical to improve the model’s performance [16]. In this study, due to input limitations of the DesktopGARP algorithm, the proposed model was only able to handle annual weather data. This would hinder the performance of the proposed model. Therefore, more efforts need to be devoted to future research to improve the performance of the proposed model in handling daily weather data.Furthermore, the presence-only model showed a great advantage when absence data of species were unavailable [31, 32]. It has been successfully applied in spatial modeling of disease [28, 40, 41], habitat predicting of species distribution [42–44], and hazard mapping of natural disasters [45–47]. Since wildfires have similar conceptual and mathematical rules with plants or animals, ecological niche modeling would be an inductive approach to compute wildfires ignition and spreading likelihood [48]. Therefore, in future studies, more ecological niche modeling could be further considered for modeling the fire susceptibility.


## 5.1. Result Analysis

Unlike traditional models [16, 21, 22, 24], the proposed model does not require the input of no-fire data. This means that additional uncertainties caused by the selection of no-fire data would not affect the modeling results, since forest areas lacking records of fire occurrence might not truly represent areas of fire absence [26, 27]. Our obtained results also further verified that the proposed model has a good performance with an AUC value of 0.869 and accuracy value of 0.732, which were close to results of our previous studies in this area (i.e., accuracy values were from 0.58 to 0.79) [16].However, for the proposed model, modeling tasks in this research might be more challenging. Specifically, due to input limitations of DesktopGARP, annual weather data were applied as model inputs. Compared to daily weather data used in [16], the annual weather data might not be accurate indicators of weather conditions on the day of fire occurrence. Therefore, if daily weather data were applied in future studies, there was a further space for improving the accuracy of proposed model.Compared to maximum entropy models (i.e., a different presence-only model [35]), the proposed model in this study has a higher accuracy. In particular, the maximum entropy models (Maxent) have been utilized to develop a new daily fire danger index [36], to predict the possibility of lightning-caused fire occurrence [37] and to examine the spatial distribution and temporal variation in forest fire occurrence [38]. Accuracies of these studies in terms of AUC value were 0.752 to 0.852 [36], 0.866 [37], and 0.816 to 0.847 [38]. These reported studies further verified the validity of the GARP model in modeling the susceptibility of forest fires.Moreover, the results in Table2 showed that the slope layer has an insignificant influence on fire occurrence in this research, even though slope could affect the fire spread rate [21, 24]. This is consistent with findings by Dlamini [26]. The main reason might be that the topography of Chongqing city (i.e., a famous mountain city in China) was dominated by high mountains and deep valleys (see Figure 2(h)). Figure 1 exhibits that fire occurrence data were primarily distributed in mountainous areas with steeper slopes. With low variations at these fire data locations [26], slope factor would have an insignificant effect on the modeling results.Findings in Figure2, Figure 4, and Table 2 indicated that human activities significantly influence the susceptibility of forest fires. The spatial distributions of high risks were similar to spatial distributions of distance to the nearest road in Figure 2(i) and distance to the nearest settlement in Figure 2(j). In a mountain city like Chongqing city, road network and settlement distribution mean an easy accessibility of facilities and services [39]. Therefore, these two factors could effectively reflect the intensity of human activities, which could significantly impact on fire occurrence.Similar to the results in some reported studies [5, 16, 23], findings in Figure 4 and Table 2 indicated that land cover and weather factors greatly affect the fire susceptibility. The primary cause of this was that land cover and weather factors have great influences on fuel moisture and loading [26].


## 5.2. Improvement Strategy

Based on DesktopGARP program, a new model using the genetic algorithm was proposed in this study to model the susceptibility of forest fires. As a new attempt to process presence-only fire occurrence data, the proposed model would not bring uncertainties to the modeling results. And, our findings suggested that the proposed model has a good performance in modeling the susceptibility of forest fires. However, these findings should be interpreted guardedly.The proposed method achieved better results in a mountain area of southwestern China. To further verify its validity, more studies need to be carried out in a wider range of forest ecosystems. Meanwhile, beyond environmental layers used in this study (i.e., annual weather, land cover, topographic, and human activity factors), more factors influencing the occurrence of forest fires should be considered in the building of the susceptibility model in future research.Moreover, accurate weather data were critical to improve the model’s performance [16]. In this study, due to input limitations of the DesktopGARP algorithm, the proposed model was only able to handle annual weather data. This would hinder the performance of the proposed model. Therefore, more efforts need to be devoted to future research to improve the performance of the proposed model in handling daily weather data.Furthermore, the presence-only model showed a great advantage when absence data of species were unavailable [31, 32]. It has been successfully applied in spatial modeling of disease [28, 40, 41], habitat predicting of species distribution [42–44], and hazard mapping of natural disasters [45–47]. Since wildfires have similar conceptual and mathematical rules with plants or animals, ecological niche modeling would be an inductive approach to compute wildfires ignition and spreading likelihood [48]. Therefore, in future studies, more ecological niche modeling could be further considered for modeling the fire susceptibility.


## 6. Conclusions

As a very destructive natural hazard, forest fire frequently occurred in mountain areas of southwestern China. Numerous models were proposed to model the fire susceptibility, which could facilitate the implementation of effective prevention and control measures. However, since a certain amount of absence data are required to train traditional models, uncertainties could be introduced into modeling results. Thus, a new model using GARP approach was utilized to process presence-only data in modeling the susceptibility of forest fires in Chongqing city. Initially, 479 fire occurrence data were applied to train the proposed model with 10 environmental layers. In addition, 205 fire occurrence data and 205 no-fire data were used as the validation dataset. Based on them, the proposed model was evaluated using a receiver operating characteristic analysis with metrics of AUC, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. The results showed that distance to the nearest road, land cover, precipitation, distance to the nearest settlement, aspect, relative humidity, elevation, wind speed, and temperature layers have significantly influence on the spatial distribution of fire susceptibility in Chongqing city. Conversely, the slope layer has an insignificant effect on modeling results, since itsP value was more than 0.05. Furthermore, the ROC results showed that AUC was 0.869, accuracy was 0.732, sensitivity was 0.59, specificity was 0.873, positive predictive value was 0.823, and negative predictive value was 0.681. These metrics revealed that the proposed model has good performance. To further verify model’s validity, more studies need to be carried out in a wider range of forest ecosystems. Moreover, the performance of the proposed model could be improved by using accurate weather data in future research.



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*Source: 5502209-2022-04-29.xml*