Improving Accuracy of Daily Weather Forecast Model at Soekarno-Hatta Airport Using BILSTM with SMOTE and ADASYN

Authors

Finkan Danitasari , Muhammad Ryan , Djati Handoko , Ida Pramuwardani

DOI:

10.29303/jppipa.v10i1.5906

Published:

2024-01-25

Issue:

Vol. 10 No. 1 (2024): January

Keywords:

ADASYN, BiLSTM, SMOTE, Weather forecast

Research Articles

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How to Cite

Danitasari, F., Ryan, M. ., Handoko, D. ., & Pramuwardani, I. . (2024). Improving Accuracy of Daily Weather Forecast Model at Soekarno-Hatta Airport Using BILSTM with SMOTE and ADASYN . Jurnal Penelitian Pendidikan IPA, 10(1), 179–193. https://doi.org/10.29303/jppipa.v10i1.5906

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Abstract

Bidirectional LSTM (BiLSTM) is an extension of LSTM which can improve model efficiency and accuracy in classification scenarios based on time series data or longer time series data repeatedly. This research uses the BiLSTM algorithm to build a daily weather forecast model at Soekarno-Hatta Airport. The model built will assist forecasters in making weather forecasts on a local scale. This research is expected to be implemented and able to increase the verification value of Soekarno-Hatta Airport weather forecasts to support flight safety in Indonesia. The dataset used is hourly surface air weather parameter data (synoptic data) of Soekarno-Hatta Meteorological Station for the period January 2018 - December 2022. There is an imbalance in the data set, so the SMOTE and ADASYN techniques are used to handle the problem. The output of this research is weather conditions categorised into sunny, sunny cloudy, cloudy, light rain, moderate rain, heavy rain, and thunder rain. The results obtained will go through model verification and evaluation by finding the accuracy value by comparing the weather forecast model output with actual weather data using a multi-category contingency table. The BiLSTM - ADASYN model obtained the highest average accuracy value compared to other models, which was 83.2%.

References

Akhila, P., Anjana, R. L. S., & Kavitha, M. (2022). Climate Forecasting:Long short Term Memory Model using Global Temperature Data. 2022 6th International Conference on Computing Methodologies and Communication (ICCMC), 469–473. https://doi.org/10.1109/ICCMC53470.2022.9753779

Akram, M., & El, C. (2016). Sequence to Sequence Weather Forecasting with Long Short-Term Memory Recurrent Neural Networks. International Journal of Computer Applications, 143(11), 7–11. https://doi.org/10.5120/ijca2016910497

Anaxos Inc. (2008). U.X.L Encyclopedia of Weather and Natural Disaster. USA: The gale Group.

Bengio, Y. (2009). Learning Deep Architectures for AI. Machine Learning, 2(1), 1-127. https://doi.org/10.1561/2200000006

Brooks, H. E., & Doswell, C. A. (1996). A Comparison of Measures-Oriented and Distributions-Oriented Approaches to Forecast Verification. Weather and Forecasting, 11(3), 288–303. https://doi.org/10.1175/1520-0434(1996)011<0288:ACOMOA>2.0.CO;2

Canar, R. L., Fontaine, A., Morillo, P. L., & El Yacoubi, S. (2020). Deep Learning to implement a Statistical Weather Forecast for the Andean City of Quito. 2020 IEEE ANDESCON, 1–6. https://doi.org/10.1109/ANDESCON50619.2020.9272106

Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research, 16, 321–357. https://doi.org/10.1613/jair.953

Chen, L., Dong, P., Su, W., & Zhang, Y. (2019). Improving Classification of Imbalanced Datasets Based on KM++ SMOTE Algorithm. 2nd International Conference on Safety Produce Informatization, 300-306. Retrieved from https://www.semanticscholar.org/paper/Improving-Classification-of-Imbalanced-Datasets-on-Chen-Dong/a25a151284fdf867e1f74ba825085e87dded7ec7

Fente, D. N., & Kumar Singh, D. (2018). Weather Forecasting Using Artificial Neural Network. 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), 1757–1761. https://doi.org/10.1109/ICICCT.2018.8473167

Goldberg, D. E., & Holland, J. H. (1988). Genetic Algorithms and Machine Learning. Kluwer Academic Publishers, 3(2), 95–99.

Gosain, A., & Sardana, S. (2017). Handling Class Imbalance Problem Using Oversampling Techniques: A Review. 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 79–85. https://doi.org/10.1109/ICACCI.2017.8125820

He, H., Bai., Y. Garcia, E. A., & Li, S. (2008). ADASYN: Adaptive synthetic sampling approach for imbalanced learning. 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 1322–1328. https://doi.org/10.1109/IJCNN.2008.4633969

Hennayake, K. M. S. A., Dinalankara, R., & Mudunkotuwa, D. Y. (2021). Machine Learning Based Weather Prediction Model for Short Term Weather Prediction in Sri Lanka. 2021 10th International Conference on Information and Automation for Sustainability (ICIAfS), 299–304. https://doi.org/10.1109/ICIAfS52090.2021.9606077

Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computing, 9(8), 1735–1780. Retrieved from https://direct.mit.edu/neco/article/9/8/1735/6109/Long-Short-Term-Memory

Hsu, S.-C., Lai, Y.-J., & Lai, S. (2021). Rainfall Forecasting Using Recurrent Neural Network and LSTM in Central Taiwan. 2021 International Conference on Engineering and Emerging Technologies (ICEET), 1–5. https://doi.org/10.1109/ICEET53442.2021.9659726

ICAO (International Civil Aviation Organization). (2010). Meteorological service for International Air Navigation. Annex 3 to the convention on International Civil Aviation Seventeenth Edition, ICAO, 3–2. Retrieved from https://www.icao.int/airnavigation/IMP/Documents/Annex%203%20-%2075.pdf

Jishan, S. T., Rashu, R. I., Haque, N., & Rahman, R. M. (2015). Improving Accuracy Of Students’ Final Grade Prediction Model Using Optimal Equal Width Binning And Synthetic Minority Over-Sampling Technique. Decision Analytics, 2(1), 1. https://doi.org/10.1186/s40165-014-0010-2

Mimboro, P., Lumban Gaol, F., Lesie Hendric Spits Warnars, H., & Soewito, B. (2021). Weather Monitoring System AIoT Based for Oil Palm Plantation Using Recurrent Neural Network Algorithm. 2021 IEEE 5th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), 283–287. https://doi.org/10.1109/ICITISEE53823.2021.9655818

Nizar, I. M., Adytia, D., & Ramadhan, A. W. (2021). Forecasting of Temperature by using LSTM and Bidirectional LSTM approach: Case Study in Semarang, Indonesia. 2021 International Conference on Data Science and Its Applications (ICoDSA), 146–150. https://doi.org/10.1109/ICoDSA53588.2021.9617495

Rahayu, S., Bharata Adji, T., & Akhmad Setiawan, N. (2017). Penghitungan k-NN pada Adaptive Synthetic-Nominal (ADASYN-N) dan Adaptive Synthetic-kNN (ADASYN-kNN) untuk Data Nominal-Multi Kategori. Jurnal Otomasi Kontrol dan Instrumentasi, 9(2), 119. https://doi.org/10.5614/joki.2017.9.2.5

Ravi, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., & Yang, G.-Z. (2017). Deep Learning for Health Informatics. IEEE Journal of Biomedical and Health Informatics, 21(1), 4–21. https://doi.org/10.1109/JBHI.2016.2636665

Salehin, I., Talha, I. M., Mehedi Hasan, Md., Dip, S. T., Saifuzzaman, Mohd., & Moon, N. N. (2020). An Artificial Intelligence Based Rainfall Prediction Using LSTM and Neural Network. 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE), 5–8. https://doi.org/10.1109/WIECON-ECE52138.2020.9398022

Sharma, U., & Sharma, C. (2022). Deep Learning Based Prediction Of Weather Using Hybrid_stacked Bi-Long Short Term Memory. 2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence), 422–427. https://doi.org/10.1109/Confluence52989.2022.9734133

Singh, N., Chaturvedi, S., & Akhter, S. (2019). Weather Forecasting Using Machine Learning Algorithm. 2019 International Conference on Signal Processing and Communication (ICSC), 171–174. https://doi.org/10.1109/ICSC45622.2019.8938211

Sutoyo, E., & Fadlurrahman, M. A. (2020). Penerapan SMOTE untuk Mengatasi Imbalance Class dalam Klasifikasi Television Advertisement Performance Rating Menggunakan Artificial Neural Network. Jurnal Edukasi dan Penelitian Informatika (JEPIN), 6(3), 379. https://doi.org/10.26418/jp.v6i3.42896

Vaidya, S., Virani, K., Nambiar, G., & Devadkar, K. (2021). Real-Time Detection of Weather-based Disasters. 2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC), 1566–1573. https://doi.org/10.1109/ICESC51422.2021.9532615

Verma, S. K., Gupta, A., & Jyoti, A. (2021). Stack layer & Bidirectional Layer Long Short—Term Memory (LSTM) Time Series Model with Intermediate Variable for weather Prediction. 2021 International Conference on Computational Performance Evaluation (ComPE), 065–070. https://doi.org/10.1109/ComPE53109.2021.9752357

Wardani, A., Akbar, A. J., Handayani, L., & Lubis, A. M. (2023). Correlation Among Rainfall, Humidity, and The El Niño-Southern Oscillation (ENSO) Phenomena in Bengkulu City During the Period from 1985-2020. Jurnal Penelitian Pendidikan IPA, 9(4), 1664–1671. https://doi.org/10.29303/jppipa.v9i4.2971

Wardani, D., Sulistyo, S., & Mustika, I. W. (2018). The Blueprint of AWOS Implementation for Aviation Services at BMKG. Conference SENATIK STT Adisutjipto Yogyakarta, 4. https://doi.org/10.28989/senatik.v4i0.243

Wica, M., Witkowski, M., Szumiec, A., & Ziebura, T. (2019). Weather Forecasting System with the use of Neural Network and Backpropagation Algorithm.

Yakshit., Kaur, G., Kaur, V., Sharma, Y., & Bansal, V. (2022). Analyzing various Machine Learning Algorithms with SMOTE and ADASYN for Image Classification having Imbalanced Data. 2022 IEEE International Conference on Current Development in Engineering and Technology (CCET), 1–7. https://doi.org/10.1109/CCET56606.2022.10080783

Author Biographies

Finkan Danitasari, Indonesia Agency for Meteorology Climatology and Geophysics, Jakarta, Indonesia.

Muhammad Ryan, Agency for Meteorology Climatology and Geophysics, Jakarta

Djati Handoko, Department of Physics, Universitas Indonesia, Depok

Ida Pramuwardani, Agency for Meteorology Climatology and Geophysics, Jakarta

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Copyright (c) 2024 Finkan Danitasari, Muhammad Ryan, Djati Handoko, Ida Pramuwardani

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