Prediksi Kabut Bandar Udara di Indonesia Menggunakan Neural Network dan Radom Forest
Abstract
Fog at airports greatly disrupts flight operations, limiting visibility and thus having a significant impact on flight operations such as taxiing, takeoff and landing. The biggest challenge in fog prediction is the inconsistent and chaotic complexity of atmospheric processes. This research uses the Neural Network algorithm and random forest algorithm to predict fog at Radin Inten II Airport in Lampung. The data used in this study include 14 weather attributes collected hourly from 2020 to 2024. Meteorological variables analyzed include dry bulb temperature, wet bulb temperature, dew point, relative humidity, barometric pressure QFE and QFF, and fog-related weather conditions . The predictive model was optimized by hyperparameter tuning including optimizer selection (SGD, Adam), learning rate ( 0.001), and number of epochs ( 300). The research results show that the random forest model with optimal configuration provides the highest accuracy of 69.44% in fog prediction. The Backpropagasi Neural Network also shows good performance well with an accuracy of 67.23%. By using this model, fog predictions can be made more accurate and faster, providing significant benefits to aviation safety. This research highlights the importance of using diverse data and rigorous evaluation methods to create reliable and effective weather prediction models.
Downloads
References
WMO, “No Title.” [Online]. Available: https://cloudatlas.wmo.int/en/fog.html
P. Pithani et al., “WRF Model Prediction of a Dense Fog Event Occurred During the Winter Fog Experiment (WIFEX),” Pure Appl. Geophys., vol. 176, no. 4, pp. 1827–1846, 2019, doi: 10.1007/s00024-018-2053-0.
P. Yadav et al., “Understanding the genesis of a dense fog event over Delhi using observations and high-resolution model experiments,” Model. Earth Syst. Environ., vol. 8, no. 4, pp. 5011–5022, 2022, doi: 10.1007/s40808-022-01463-x.
H. J. S. Fernando et al., “C-FOG life of coastal fog,” Bull. Am. Meteorol. Soc., vol. 102, no. 2, pp. E244–E272, 2021, doi: 10.1175/BAMS-D-19-0070.1.
D. Bari, T. Bergot, and R. Tardif, “Fog Decision Support Systems: A Review of the Current Perspectives,” Atmosphere (Basel)., vol. 14, no. 8, pp. 1–13, 2023, doi: 10.3390/atmos14081314.
K. YuvaPrasath and I. Sudha, “Accurate Weather Prediction on Sunny Days Using Back Propagation Algorithm Compared with Artificial Neural Networks,” in 2023 Intelligent Computing and Control for Engineering and Business Systems (ICCEBS), Dec. 2023, pp. 1–4. doi: 10.1109/ICCEBS58601.2023.10448882.
A. Shankar and B. C. Sahana, “Early warning of low visibility using the ensembling of machine learning approaches for aviation services at Jay Prakash Narayan International (JPNI) Airport Patna,” SN Appl. Sci., vol. 5, no. 5, 2023, doi: 10.1007/s42452-023-05350-7.
S. H. Arun et al., “A study to improve the fog/visibility forecast at IGI Airport, New Delhi during the winter season 2020–2021,” J. Earth Syst. Sci., vol. 131, no. 2, p. 124, 2022, doi: 10.1007/s12040-022-01874-5.
N. Penov and G. Guerova, “Sofia Airport Visibility Estimation with Two Machine-Learning Techniques,” Remote Sens., vol. 15, no. 19, pp. 1–17, 2023, doi: 10.3390/rs15194799.
A. Noeman, D. Handayani, and A. Hiswara, “Decision Tree-Based Weather Prediction,” PIKSEL Penelit. Ilmu Komput. Sist. Embed. Log., vol. 10, pp. 67–78, Jul. 2022, doi: 10.33558/piksel.v10i1.4418.
H. Qin and H. Qin, “An End-to-End Traffic Visibility Regression Algorithm,” IEEE Access, vol. 10, pp. 25448–25454, 2022, doi: 10.1109/ACCESS.2021.3101323.
S. Saha and D. Valles, “Forecast Analysis of Visibility for Airport Operations with Deep Learning Techniques,” in 2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC), 2023, pp. 553–558. doi: 10.1109/CCWC57344.2023.10099100.
P. Hewage, M. Trovati, E. Pereira, and A. Behera, “Deep learning-based effective fine-grained weather forecasting model,” Pattern Anal. Appl., vol. 24, no. 1, pp. 343–366, 2021, doi: 10.1007/s10044-020-00898-1.
G. Montavon, “Introduction to Neural Networks BT - Machine Learning Meets Quantum Physics,” K. T. Schütt, S. Chmiela, O. A. von Lilienfeld, A. Tkatchenko, K. Tsuda, and K.-R. Müller, Eds., Cham: Springer International Publishing, 2020, pp. 37–62. doi: 10.1007/978-3-030-40245-7_4.
V. Y. Zulfian Azmi, Pengantar Jaringan Saraf Tiruan: (Pengantar Jaringan Syaraf Tiruan). Mitra Wacana Media, 2021. [Online]. Available: https://www.mitrawacanamedia.com/pengantar-jaringan-saraf-tiruan
E. Izquierdo-Verdiguier and R. Zurita-Milla, “An evaluation of Guided Regularized Random Forest for classification and regression tasks in remote sensing,” Int. J. Appl. Earth Obs. Geoinf., vol. 88, p. 102051, 2020, doi: https://doi.org/10.1016/j.jag.2020.102051.
Sriyanto and A. Ria Supriyatna, “Prediksi Penyakit Diabetes Menggunakan Algoritma Random Forest,” Ijccs, vol. 17 No. 1, no. x, pp. 1–5, 2023.
A. Raup, W. Ridwan, Y. Khoeriyah, S. Supiana, and Q. Y. Zaqiah, “Deep Learning dan Penerapannya dalam Pembelajaran,” JIIP - J. Ilm. Ilmu Pendidik., vol. 5, no. 9, pp. 3258–3267, 2022, doi: 10.54371/jiip.v5i9.805.
A. Zulfiani and C. Fauzi, “Penerapan Algorimta Backpropagation Untuk Prakiraan Cuaca Harian Dibandingkan Dengan Support Vector Machine dan Logistic Regression,” J. Media Inform. Budidarma, vol. 7, no. 3, pp. 1229–1237, 2023, doi: 10.30865/mib.v7i3.6173.
A. Zaras, N. Passalis, and A. Tefas, “Chapter 2 - Neural networks and backpropagation,” in Deep Learning for Robot Perception and Cognition, A. Iosifidis and A. Tefas, Eds., Academic Press, 2022, pp. 17–34. doi: https://doi.org/10.1016/B978-0-32-385787-1.00007-5.
B. Sullivan, “Charniak, E. An Introduction to Deep Learning,” Perception, vol. 48, no. 8, pp. 759–761, 2019, doi: 10.1177/0301006619857273.
R. Nurpambudi, E. S. P. Wulandari, and R. A. Aziz, “Prediction of flood events in the city of Bandar Lampung using the artificial neural network,” J. Infotel, vol. 15, no. 1, pp. 34–45, 2023, doi: 10.20895/infotel.v15i1.878.
T. O. Hodson, “Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not,” Geosci. Model Dev., vol. 15, no. 14, pp. 5481–5487, 2022, doi: 10.5194/gmd-15-5481-2022.
A. A. Suryanto, “Penerapan Metode Mean Absolute Error (Mea) Dalam Algoritma Regresi Linear Untuk Prediksi Produksi Padi,” Saintekbu, vol. 11, no. 1, pp. 78–83, 2019, doi: 10.32764/saintekbu.v11i1.298.
F. Demir, “14 - Deep autoencoder-based automated brain tumor detection from MRI data,” V. Bajaj and G. R. B. T.-A. I.-B. B.-C. I. Sinha, Eds., Academic Press, 2022, pp. 317–351. doi: https://doi.org/10.1016/B978-0-323-91197-9.00013-8.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Prediksi Kabut Bandar Udara di Indonesia Menggunakan Neural Network dan Radom Forest
Pages: 746-757
Copyright (c) 2024 agustinus kurniawan agustinus, RZ Abdul Aziz
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).