Evaluation of forest fire risk using the Apriori algorithm and fuzzy c-means clustering

Jafarzadeh, Ali Akbar; Mahdavi, Ali; Jafarzadeh, Heydar

doi:10.17221/7/2017-jfs

Cited by 22 publications

(7 citation statements)

References 31 publications

(30 reference statements)

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“…In our study, the overall accuracy was 95%, the kappa coefficient was 0.93 and the AUC value was 0.81, suggesting the map is accurate and fit for purpose. The AUC value above 0.80 is considered highly accurate and well-accepted (Jafarzadeh et al, 2017). Tiwari et al, (2021) report similar results from India when comparing the AUC values under fuzzy-AHP, AHP and frequency methods of forest fire risk map where the highest AUC value was under fuzzy-AHP method (0.83).…”

Section: Discussionmentioning

confidence: 55%

Areas of the Terai Arc Landscape in Nepal at Risk of Forest Fire Identified by Fuzzy Analytic Hierarchy Process

Parajuli¹,

Manzoor

Lukáč³

2022

SSRN Journal

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Section: Discussionmentioning

confidence: 55%

Areas of the Terai Arc Landscape in Nepal at Risk of Forest Fire Identified by Fuzzy Analytic Hierarchy Process

Parajuli¹,

Manzoor

Lukáč³

2022

SSRN Journal

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“…FCM was used to map all of the variables into 3 clusters, namely low, medium and high. Their study concluded that there was a strong relationship between the forest fire clusters and the cluster of 8 input variables, including: distance from the settlements, population density, distance from a road, slope, standing dead oak trees, temperature, land cover and distance from farmland (Jafarzadeh, Mahdavi, & Jafarzadeh, 2017). Differing from the aforementioned studies, in this paper we apply global satellite data that contains dense spatial information about climate and burned areas to cluster areas over Borneo.…”

Section: Introductionmentioning

confidence: 98%

“…Mustofa (2014) predicted the size of forest fires with climate variables and the forest fire weather index using the FCM clustering method that can successfully classify fire levels into three categories namely low, light and heavily burned area (Shidik & Mustofa, 2014). Jafarzadeh (2017) studied the relationship between climate and geographical variables and the fire events in Ilam Province, West of Iran. FCM was used to map all of the variables into 3 clusters, namely low, medium and high.…”

Section: Introductionmentioning

confidence: 99%

Correlation of Climate Variability and Burned Area in Borneo using Clustering Methods

Hidayati

Nalaratih

Shabrina

et al. 2020

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The island of Borneo has faced seasonal forest fires for decades. This phenomenon is worsening during dry seasons, especially when droughts are concurrent with the El Niño-Southern Oscillation (ENSO) phenomenon. Climate is therefore one of the drivers of the fire phenomenon. This paper studies the relationship between climate variables, namely temperature, precipitation, relative humidity, and wind speed, and the occurrence of forest fire using two clustering methods, K-means and Fuzzy C-means (FCM) clustering methods. Borneo is clustered into four areas based on burned area data obtained from Global Fire Emission Data (GFED). It is also clustered according to the combinations of climate variables. Both methods reach the highest correlation between the climate variable and the burned area clusters in September. The K-means method gives a correlation of -0.54 while the FCM gives -0.55. In August until October, relative humidity provides the dominant correlation affecting burned area, even though an additional precipitation or wind variable slightly increases the correlation in the FCM method. In November, temperature largely contributed to the burned area by a positive correlation of 0.31 in K-means and 0.33 in FCM. The evaluation performance of the methods is conducted by an internal validation called the Silhouette index. Both methods have positive index values ranging from 0.39 to 0.69 and the maximum value is influenced by the wind cluster. This indicates that the clustering methods applied in this paper can identify one or a combination of climate variables into dense and well-separated clusters.

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“…Mokhtari et al, 2019Ahmadi et al, 2018Jafarzadeh & Mahdavi, 2017Mokhtari et al, 2019Rostami & Kazemi, 2019Ahmadi et al, 2018Ghanavati, 2014Mokhtari et al, 2019Ahmadi et al, 2018 …”

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confidence: 99%

Geodiversity evaluation and geoconservation using grid analysis: case study, north of Ilam Province

et al. 2021

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The north of Ilam Province in Iran has a great variety of geodiversity features, and the presence of geodiversity resources, along with other geological and geomorphological values, has enriched the area. The purpose of the present study is to evaluate the geodiversity in the north of Ilam Province. The main indices for assessing geodiversity in the present study include geology, geomorphology, geomorphosites, paleontology, pedology, hydrology, mineral resources, and fossil species. In addition to assessing geodiversity, the hazard maps of the area were also prepared and overlapped with the geodiversity attraction maps, to identify areas with high geodiversity attraction as well as areas with high vulnerability. The results showed that 71% of the region had moderate and high attraction in terms of geodiversity indices. The most attractive areas are located in the north, center, and southeast of the region, which include the entire protected area of Manesht, Bankul, and Ghalarang; the Gachan and Arghavan area; and to the south the Sivan Arch and the Kabir Kuh area. The hazard map analysis also showed that the areas with the highest score in terms of geodiversity indices had many hazards indicating high sensitivity, unsustainable exploitation, and a growing trend of their destruction. All areas with high geodiversity attraction are exposed to severe human hazards. Most of these hazards are concentrated in the center and southeast of the region, which corresponds to areas with high geodiversity attraction. Due to their specific sensitivities, these areas can maintain their existence if they are exploited in a sustainable manner; otherwise, we will see these resources degraded and destroyed in the future.

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Evaluation of forest fire risk using the Apriori algorithm and fuzzy c-means clustering

Cited by 22 publications

References 31 publications

Areas of the Terai Arc Landscape in Nepal at Risk of Forest Fire Identified by Fuzzy Analytic Hierarchy Process

Areas of the Terai Arc Landscape in Nepal at Risk of Forest Fire Identified by Fuzzy Analytic Hierarchy Process

Correlation of Climate Variability and Burned Area in Borneo using Clustering Methods

Geodiversity evaluation and geoconservation using grid analysis: case study, north of Ilam Province

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