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All Journal Megasains Jurnal Widya Climago
Prawiro Adiredjo, Indra
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Humanities Earth & Planetary Sciences Education Social Sciences Other

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PREDIKSI KEJADIAN PETIR DENGAN ARTIFICIAL NEURAL NETWORK DI WILAYAH KABUPATEN KEPULAUAN TANIMBAR Prawiro Adiredjo, Indra
Megasains Vol 14 No 2 (2023): Megasains Vol. 14 No. 2 Tahun 2023
Publisher : Stasiun Pemantau Atmosfer Global Bukit Kototabang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46824/megasains.v14i2.140

Abstract

Various research efforts have been made to determine thunderstorm prediction methods, one of which involves using upper air data. However, the use of atmospheric stability threshold values as a reference does not always apply uniformly to all locations due to differences in the characteristics of each region. Therefore, a more objective and precise approach is needed in predicting thunderstorm events, including the application of artificial neural network (ANN) techniques. In this study, the Artificial Neural Network (ANN) method, which is an implementation of artificial intelligence, is used to predict thunderstorm events in the Saumlaki region. The ANN input not only relies on raw data in the form of atmospheric instability index values but also uses feature selection processing to reduce the dimensionality of multivariate input data, minimizing the loss of input data. This process focuses only on essential information and eliminates linear dependencies between features, a technique known as Principal Component Analysis (PCA). The research results indicate that ANN with PCA technique has a higher level of accuracy in predicting thunderstorm events in the Saumlaki region.
UJI KORELASI PARAMETER INDEKS RASON TERHADAP PEMBENTUKAN PETIR DI KEPULAUAN TANIMBAR Prawiro Adiredjo, Indra
Jurnal Widya Climago Vol 6 No 2 (2024): Developing Human Resource Competencies in Climate Analysis and Learning Innovatio
Publisher : Pusdiklat BMKG

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Abstract

Several meteorological practitioners have utilized machine learning techniques to forecast adverse weather conditions, particularly lightning occurrences. Upper air data, obtained through radiosonde measurements, is frequently employed to train machine learning models due to its ability to capture atmospheric instability. Despite its common usage, radiosonde-based lightning predictions typically have a validity window of 6-12 hours. However, cumulonimbus cloud formation in tropical regions, the primary source of lightning, typically lasts between 30 minutes to 1-2 hours per phase, casting doubt on the efficacy of radiosonde data for longer-term predictions. Furthermore, variations in local atmospheric patterns result in non-uniform utilization of radiosonde index parameters across different regions. Understanding the relationship between these parameters and lightning events is crucial for atmospheric thermodynamic analysis and region-specific prediction model development. This study examines the correlation between radiosonde index parameters in the Tanimbar Islands and lightning events from cumulonimbus clouds, utilizing indices such as KI, LI, SI, TT, CAPE, and CIN. Results suggest that index sustainability does not consistently correlate with lightning formation, with differing validity periods observed for 3 and 6 hours ahead. The reason index parameters in the form of SI, KI, and TT are only valid for predicting 3 hours ahead during the months of March-April-May, while only KI maintains validity for both 3 and 6 hours ahead at certain times.
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