Deep Learning Methods as a Detection Tools for Forest Fire Decision Making Process Fire Prevention in Indonesia

被引:0
|
作者
Suri, Dia Meirina [1 ,3 ]
Nurmandi, Achmad [2 ]
机构
[1] Univ Muhammadiyah Yogyakarta, Dept Islamic Polit Polit Sci, Yogyakarta, Indonesia
[2] Univ Muhammdiyah Yogyakarta, JK Sch Govt, Dept Govt Affairs & Adm, Yogyakarta, Indonesia
[3] Univ Islam Riau, Publ Adm, Pekanbaru, Indonesia
关键词
D O I
10.1007/978-3-030-90176-9_24
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This research examines the collaboration between agencies in policy-making based on hotspot monitoring from satellites. Valid data regarding the number of hotspots from the satellite is needed in decision making because it provides information used to control forest and land fires in Indonesia. For instance, the Ministry of Forestry uses data from the NOAA-18 satellite for analysis, while the BMKG utilizes those from the Agua/Terra. However, the data generated by each satellite has differences in the number of hotspots. Therefore, this research aims to determine the collaboration between the Ministry of Forestry and BMKG in the use of satellite data for decision-makers to determine disaster alert status. This research uses a qualitative approach to analyze secondary data from two popular media sources collected using the Nvivo 12 plus application. The result showed that agencies involved in fire prevention lack collaboration due to institutional designs that lead to a lack of communication and unclear roles for each institution during the decision making process.
引用
收藏
页码:177 / 182
页数:6
相关论文
共 50 条
  • [31] Exploiting drone images for forest fire detection using metaheuristics with deep learning model
    Rajalakshmi, S.
    Sellam
    Kannan, N.
    Saranya, S.
    [J]. GLOBAL NEST JOURNAL, 2023, 25 (07): : 147 - 154
  • [32] Detection of forest fire using deep convolutional neural networks with transfer learning approach
    Reis, Hatice Catal
    Turk, Veysel
    [J]. APPLIED SOFT COMPUTING, 2023, 143
  • [33] DRONE IMAGERY FOREST FIRE DETECTION AND CLASSIFICATION USING MODIFIED DEEP LEARNING MODEL
    Mashraqi, Aisha M.
    Asiri, Yousef
    Algarni, Abeer D.
    Abu-Zinadah, Hanaa
    [J]. THERMAL SCIENCE, 2022, 26 : 411 - 423
  • [34] DEEP LEARNING OF QINLING FOREST FIRE ANOMALY DETECTION BASED ON GENETIC ALGORITHM OPTIMIZATION
    Jiang, Yuan
    Wei, Rui
    Chen, Jian
    Wang, Guibao
    [J]. UNIVERSITY POLITEHNICA OF BUCHAREST SCIENTIFIC BULLETIN SERIES C-ELECTRICAL ENGINEERING AND COMPUTER SCIENCE, 2021, 83 (04): : 75 - 84
  • [35] Investigation of Combining Deep Learning Object Recognition with Drones for Forest Fire Detection and Monitoring
    Yandouzi, Mimoun
    Grari, Mounir
    Berrahal, Mohammed
    Idrissi, Idriss
    Moussaoui, Omar
    Azizi, Mostafa
    Ghoumid, Kamal
    Elmiad, Aissa K. E. R. K. O. U. R.
    [J]. INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2023, 14 (03) : 377 - 384
  • [36] Review of Modern Forest Fire Detection Techniques: Innovations in Image Processing and Deep Learning
    Özel, Berk
    Alam, Muhammad Shahab
    Khan, Muhammad Umer
    [J]. Information (Switzerland), 2024, 15 (09)
  • [37] DRONE IMAGERY FOREST FIRE DETECTION AND CLASSIFICATION USING MODIFIED DEEP LEARNING MODEL
    Mashraqi, Aisha M.
    Asiri, Yousef
    Algarni, Abeer D.
    Abu-Zinadah, Hanaa
    [J]. Thermal Science, 2022, 26 (Special Issue 1):
  • [38] Deep learning of qinling forest fire anomaly detection based on genetic algorithm optimization
    Jiang, Yuan
    Wei, Rui
    Chen, Jian
    Wang, Guibao
    [J]. UPB Scientific Bulletin, Series C: Electrical Engineering and Computer Science, 2021, 83 (04): : 75 - 84
  • [39] A real-time forest fire and smoke detection system using deep learning
    Mohammed, Raghad K.
    [J]. INTERNATIONAL JOURNAL OF NONLINEAR ANALYSIS AND APPLICATIONS, 2022, 13 (01): : 2053 - 2063
  • [40] Early Forest Fire Detection System using Wireless Sensor Network and Deep Learning
    Benzekri, Wiame
    El Moussati, Ali
    Moussaoui, Omar
    Berrajaa, Mohammed
    [J]. INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2020, 11 (05) : 496 - 503