Analysis Of Fire Risk For Forest And Land In West Kalimantan Using Logistic Regression Method With Generalized Extreme Value Approach

Radit Candra Nugroho; Pratnya Paramitha Oktaviana

doi:10.20956/j.v20i1.27474

Authors

Radit Candra Nugroho Institut Teknologi Sepuluh Nopember
Pratnya Paramitha Oktaviana Institut Teknologi Sepuluh Nopember

DOI:

https://doi.org/10.20956/j.v20i1.27474

Keywords:

Generalized Extreme Value, Forest and Land Fires, Opportunity, Binary Logistic Regression, Spatial

Abstract

Forests in Indonesia have been reduced by half due to fires. Forest and land fires often occur during the long dry season in places such as the island of Borneo. West Kalimantan is an area passed by the equator which is directly above the Pontianak area. The main effect is to make West Kalimantan a tropical area with high air temperatures so that forest and land fires often occur. This study aims to obtain the results of the probability of land and forest fires in each district in West Kalimantan. The method used is binary logistic regression analysis with response variables in the form of data categories based on spatial data and analysis of extreme values with Generalized Extreme Value (GEV). Spatial analysis uses the help of ARCGIS software in processing raster data (grid cells). The data used is data on maximum temperature and maximum wind speed taken from October 7, 2021 to October 31, 2022 from the official NASA website. The spatial data used in this study is forest and land fire vulnerability data taken from the BNPB website in the form of raster data. The results of logistic regression analysis found that the maximum temperature variable has a negative relationship with the response variable, while the maximum speed of wind variable has a positive relationship with the response variable. The temporal probability of the resulting GEV is getting higher with a longer period of years ahead. The probability of forest and land fires is obtained by multiplying the log probability by the GEV temporal probability. In this study, it was found that the highest chance of forest and land fires occurring in Sanggau Regency was suspected to occur due to an increase in temperature every year.

References

Adam, S. S., Rindarjono, M. G., & Karyanto, P., 2019. Sistem Informasi Geografi Untuk Zonasi Kerentanan Kebakaran Lahan Dan Hutan Di Kecamatan Malifut, Halmahera Utara. Teknologi Informasi dan Ilmu Komputer, Vol. 6, No. 5, 559-566.

Angger, & Jaya. Direktorat PKHL Lakukan Perhitungan Luas Karhutla Kolaboratif. Direktorat Jenderal Pengendalian Perubahan Iklim: http://ditjenppi.menlhk.go.id/berita-ppi/4296-direktorat-pkhl-lakukan-perhitungan-luas-karhutla-kolaboratif.html. [10 Februari 2023]

Bayususetyo, D., Santoso, R., & Tarno., 2017. Klasifikasi Calon Pendonor Darah Menggunakan Metode Naïve Bayes Classifier. Jurnal Gaussian,Vol. 6, No. 2, 193-200.

BNPB. 2012. Peraturan Kepala Badan Nasional Penanggulangan Bencana Nomor 02 Tahun 2012 Tentang Pedoman Umum Pengkajian Risiko Bencana.

BNPB. 2016. inaRISK: inarisk.bnpb.go.id. [10 Februari 2023]

Burhan, S., & Jaya, A. K., 2018. Penaksiran Parameter Regresi Linier Logistik Dengan Metode Maksimum Likelihood Lokal Pada Resiko Kanker Payudara Di Makassar. Jurnal Matematika Statistika & Komputasi, Vol. 14, No. 2, 159-165.

Dharmawan, K., 2012. Estimasi Nilai VaR Dinamis Indeks Saham Menggunakan Peak-Over Threshold dan Block Maxima. Jurnal Matematika,Vol. 2, No. 2.

Gao, X., Duan, G., & Lan, C., 2021. Bayesian Updates for an Extreme Value Distribution Model of Bridge Traffic Load Effect Based on SHM Data. Sustainability, Vol. 13, No. 15.

Hartono, I. F., & Sutikno., 2020. Analisis Curah Hujan Ekstrem pada Kasus Elevasi Tinggi Air Muka Bendungan Bilibili Sulawesi Selatan dengan Pendekatan Peaks Over Threshold. Sains Dan Seni ITS, Vol. 9, No. 2.

Hong, J., Agustin, W., Yoon, S., & Park, J. S., 2022. Changes of extreme precipitation in the Philippines, projected from the CMIP6 multi-model ensemble. Weather and Climate Extremes, Vol. 37, 100480.

Itsnaini, N., Sasmito, B., Sukmono, A., & Prasasti, I., 2017. Analisis Hubungan Curah Hujan Dan Parameter Sistem Peringkat Bahaya Kebakaran (SPBK) Dengan Kejadian Kebakaran Hutan Dan Lahan Untuk Menentukan Nilai Ambang Batas Kebakaran. Jurnal Geodesi Undip, Vol. 6, No. 2.

Keyser, A. R., & Westerling, A. L., 2019. Predicting increasing high severity area burned for three forested regions in the western United States using extreme value theory. Forest Ecology and Management, Vol. 432, 694-706.

Keyser, A., & Westerling, A. L., 2017. Climate drives inter-annual variability in probability of high severity fire occurrence in the western United States. Environmental Research, Vol. 12, No. 6.

Kim, H., Lee, J. H., Park, H. J., & Heo, J. H., 2021. Assessment of temporal probability for rainfall-induced landslides based on nonstationary extreme value analysis. Engineering Geology, Vol. 294, 106372.

Mahdi, M. I., 2022. Luas Kebakaran Hutan dan Lahan Indonesia Meningkat pada 2021. dataindonesia: https://dataindonesia.id/ragam/detail/luas-kebakaran-hutan-dan-lahan-indonesia-meningkat-pada-2021. [28 Januari 2023]

Nurdiawan, O., & Putri, H., 2018. Pemetaan Daerah Rawan Banjir Berbasis Sistem Informasi Geografis Dalam Upaya Mengoptimalkan Langkah Antisipasi Bencana. InfoTECH, Vol. 4, No. 2.

Pandapotan, I. B., Suarbawa, K. N., & Widagda, I. G., 2022. Analisis Pola Sebaran Asap Terhadap Kondisi Meteorologi di Pulau Kalimantan Terkait Kebakaran Hutan dan Lahan: Studi Kasus Kebakaran Hutan pada Bulan September 2019. Buletin Fisika, Vol. 23, No. 1, 19-25.

Rahmayani, D., & Sutikno., 2019. Analisis Curah Hujan Ekstrim Non-Stasioner dengan Pendekatan Block Maxima di Surabaya dan Mojokerto. Sains Dan Seni ITS, Vol. 8, No. 2.

Rasyid, F., 2014. Permasalahan dan Dampak Kebakaran Hutan. Lingkar Widyaiswara, Vol. 1, No. 4, 47-59.

Rinaldi, A., 2016. Sebaran Generalized Extreme Value (GEV) dan Generalized Pareto (GP) untuk Pendugaan Curah Hujan Ekstrim di Wilayah DKI Jakarta. Jurnal Pendidikan Matematika, Vol. 7, No. 1, 75 - 84.

Rizaty, M. A., 2022. Sebanyak 30 Provinsi Terdampak Karhutla hingga Oktober 2022. dataindonesia: https://dataindonesia.id/varia/detail/sebanyak-30-provinsi-terdampak-karhutla-hingga-oktober-2022. [10 Februari 2023]

Saputro, J. G., Handayani, I. G., & Najicha, F. U., 2021. Analisis Upaya Penegakan Hukum Dan Pengawasan Mengenai Kebakaran Hutan Di Provinsi Kalimantan Barat. Jurnal Manajemen Bencana, Vol. 7, No. 1, 27-36.

Simanjuntak, M. S., Kusnandar, D., & Debataraja, N. N., 2022. Pemetaan Rawan Kebakaran Hutan Di Kalimantan Barat Tahun 2020. Buletin Ilmiah Math Stat dan Terapannya, Vol. 11, No. 5, 777 – 784.

Townsend, J. P., & Aldstadt, J., 2023. Habitat suitability mapping using logistic regression analysis of long-term bioacoustic bat survey dataset in the Cassadaga Creek watershed (USA). Science of the Total Environment, Vol. 895, 165077.

Yusuf, A., Hapsoh, Siregar, S. H., & Nurrochmat, D. R., 2019. Analisis Kebakaran Hutan Dan Lahan Di Provinsi Riau. Dinamika Lingkungan Indonesia, Vol. 6, No. 2, 67-84.

Zhang, C., & Yang, Y., 2020. Modeling the spatial variations in anthropogenic factors of soil heavy metal accumulation by geographically weighted logistic regression. Science of the Total Environment, Vol. 717, 137096.