Prediction of Forest Fire Occurrence in Peatlands using Machine Learning Approaches

Rosadi, Dedi; Andriyani, Widyastuti; Arisanty, Deasy; Agustina, Dina

doi:10.1109/isriti51436.2020.9315359

Cited by 12 publications

(6 citation statements)

References 6 publications

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“…Konselor perlu bertransformasi menjadi seorang yang mampu memanfaatkan era digital, untuk pengembangan profesi BK. Konselor dituntut untuk menjadi lifelong learner, kreatif, inovatif, reflektif, dan kolaboratif (Rosadi & Andriyani, 2020). Akan tetapi belum semua guru BK memanfaatkan teknologi dalam pemberian layanan.…”

Section: Pendahuluanunclassified

Inovasi Penggunaan Media Digital pada Layanan Bimbingan Karir dalam Kerangka Kurikulum Merdeka Belajar di Era Society 5.0

Amalia,

Nufi,

Maydana

2024

Anterior J

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Layanan bimbingan karir dengan media digital digunakan guru BK dalam upaya untuk memberikan informasi terkait karir. Guru BK/ Konselor sekolah harus mampu memanfaatkan teknologi untuk menunjang pemberian layanan BK, khususnya dalam pemberian layanan bimbingan karir dalam kurikulum merdeka belajar. Tujuan penelitian ini adalah untuk mengetahui inovasi penggunaan media digital pada layanan bimbingan karir dalam kerangka kurikulum merdeka belajar di era society 5.0. metode penelitian yang dilakukan dalam penelitian ini adalah metode kuantitatif dengan desain penelitian survey. Desain penelitian survey dipilih untuk melihat tingkat penggunaan media digital pada layanan bimbingan karir dalam kerangka merdeka belajar dan inovasi yang digunakan guru BK dalam layanan bimbingan karir. Hasil penelitian ini menyatakan bahwa penggunaan media digital yang dilakukan guru BK se Kotawaringin Timur dalam pemberian layanan karir masih dalam kategori sedang dan perlu ditingkatkan serta disesuaikan dengan kurikulum merdeka pada era society 5.0. kesimpulan dalam penelitian ini adalah inovasi dalam penggunaan media digital yang dilakukan oleh guru BK beraneka ragam diantaranya seperti Whatsaap Google Classroom, Canva, Instagram, dll. Akan tetapi dalam proses nya perlu adanya sosialisasi terhadap guru BK terkait penggunaan media digital yang berfokus pada karir siswa.

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Section: Pendahuluanunclassified

Inovasi Penggunaan Media Digital pada Layanan Bimbingan Karir dalam Kerangka Kurikulum Merdeka Belajar di Era Society 5.0

Amalia,

Nufi,

Maydana

2024

Anterior J

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“…In this study, the BN model with an AUC value of 0.96 showed to be more accurate than the other models in predicting future fires. A similar study [36] examined the application of various machine learning approaches to predict forest fire occurrence in peatlands. The variables collected from topographical and meteorological data from South Kalimantan Province were the time (of data collected), the district area, land surface temperature (LST), wind speed, humidity, height, and NDVI (normalized vegetation index).…”

Section: Systematic Reviewmentioning

confidence: 99%

Optimizing Forest Fire Prediction: A Comparative Analysis of Machine Learning Models through Feature Selection and Time-Stage Evaluation

Khosravi,

Shafie,

Raihan

et al. 2023

Preprint

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Despite being considered as natural components of many ecosystems, forest fires pose significant threats to the environment and human health. In order to ensure public safety and effective fire suppression planning, it is necessary to develop reliable prediction models to mitigate forest fire danger. These models should account for specific environmental conditions. The advent of big data in recent years has opened new avenues for improving forest fire predictions. Machine learning techniques, surpassing traditional forecasting methods, have shown significant promise in this area. By applying the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology to a real-world dataset, this paper explores the application of machine learning approaches to understand forest fire patterns and predict fire danger. We consider six distinct time stages and incorporate feature selection to refine our predictions. It is important to note that forest fire behavior models are not universally effective due to geographical variations in data. Nevertheless, advanced decision-making techniques are vital in forest fire management. Our research presents a systematic exploration of the topic, comparing various machine learning models, thereby providing a comprehensive baseline for future investigations in this crucial environmental arena.

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“…e pink color represents a low fire rate, while the dark red represents a high fire rate. Peatlands are dark red areas with high severity peatlands; peatlands always experience fires every dry season [29,33,37].…”

Section: Dnbr and Rbr Spectral Transformation Capability Formentioning

confidence: 99%

Utilizing Sentinel-2 Data for Mapping Burned Areas in Banjarbaru Wetlands, South Kalimantan Province

Arisanty

Ramadhan

Angriani

et al. 2022

International Journal of Forestry Research

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Sentinel-2 imagery can identify forest and land fires in underground parts, surface fires, and crown fires. The dNBR and RBR spectral indices on Sentinel-2 images proved accurate in identifying. This study analyzed the index value for burned area mapping in wetland areas using Sentinel-2 imagery data in 2019 and hotspot data from the MODIS data. The indices used to identify the burned area and the severity of the fire was the differenced normalized burn ratio (dNBR) and relativized burn ratio (RBR). Visual validation tests were performed by comparing RGB composite images to check the appearance before and after combustion with dNBR and RBR results. The dNBR value accuracy was 91.5%, and for a kappa, the accuracy was 89.58%. The RBR accuracy was 92.9%, and the kappa accuracy was 0.91. The results confirmed that in the Banjarbaru area, RBR was more accurate in identifying burned areas than dNBR; both indices can be used for burned area mapping in wetland areas.

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Prediction of Forest Fire Occurrence in Peatlands using Machine Learning Approaches

Cited by 12 publications

References 6 publications

Inovasi Penggunaan Media Digital pada Layanan Bimbingan Karir dalam Kerangka Kurikulum Merdeka Belajar di Era Society 5.0

Inovasi Penggunaan Media Digital pada Layanan Bimbingan Karir dalam Kerangka Kurikulum Merdeka Belajar di Era Society 5.0

Optimizing Forest Fire Prediction: A Comparative Analysis of Machine Learning Models through Feature Selection and Time-Stage Evaluation

Utilizing Sentinel-2 Data for Mapping Burned Areas in Banjarbaru Wetlands, South Kalimantan Province

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