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Volume 61, 2024The 9th International Symposium on Current Progress in Mathematics and Sciences 2023 (The 9th ISCPMS 2023) in conjunction with AUA Academic Conference on the Application of Artificial Intelligences and Data Sciences in a Modern Science for a Better Life
|Number of page(s)
|10 January 2024
Space-time permutation statistics application to detect cloud-to-ground lightning prone area (Case study: Pasuruan, Indonesia)
Mathematics Department, Faculty of Science and Technology, UIN Maulana Malik Ibrahim Malang, Jalan Gayana 50, Malang, Jawa Timur 65144, Indonesia
* Corresponding author: email@example.com
Lightning is a natural phenomenon caused by the release of positive and negative charges occur in cumulonimbus (CB) clouds. CG lightning is a lightning that strikes from the clouds to the ground. This lightning is dangerous for human activities which can cause burns, blindness, and even temporary deafness. This research will determine the areas prone to CG lightning strikes and lightning characteristics in the city of Pasuruan. Space-time permutation scan statistics is a method used to detect prone areas by considering spatial and temporal aspects. This method merely requires case data, such as location and time without using population data. The detected prone areas will be tested for significance using Monte Carlo. It is used to determine the distribution of the sample. In this study, the Monte Carlo test for scanning window is 0.048 (p-value < 0.05), Sekargadung. Thus, making Sekargadung a hotspot for lightning-prone areas. Furthermore, the value is taken based on the highest ratio test (LRT), 11.46, which is the most likely cluster. Based on space-time permutation statistics Sekargadung is the main hotspot prone area in this case study. It has 226 strikes with the intensity of their occurrence with characteristics area dominant paddy field.
Key words: Space-time permutatio scan / likelihood ratio test / hazardous areas / cloud-to-fround / lightning prone area
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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