Evaluation of the XGBoost Model for Rainfall Prediction and Classification Using BMKG Data and OpenWeather API
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Abstract
Indonesia, situated between two continents and two oceans, experiences significant climate variability, with rainfall patterns shaped by geographical and topographical factors, as well as phenomena like the El Niño Southern Oscillation (ENSO). Accurate rainfall forecasting plays a critical role in disaster mitigation, agricultural planning, and water resource management. This study focuses on developing a rainfall prediction and classification model using the Extreme Gradient Boosting (XGBoost) algorithm. The model leverages historical rainfall data from the Indonesian Meteorological, Climatological, and Geophysical Agency (BMKG) and real-time data from the OpenWeather API. The output includes rainfall trend graphs and classification of rainfall intensity into categories such as light, moderate, or heavy. Model performance is assessed through metrics like accuracy, precision, RMS (Root Mean Square), and RMSE (Root Mean Square Error). This research highlights the integration of historical and real-time data for weather forecasting and demonstrates the application of advanced machine learning techniques like XGBoost to build robust and precise prediction models. The findings are expected to offer practical insights for disaster risk reduction, agricultural strategy planning, and effective water resource management.
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M. A. Priatna and E. C. Djamal, “Precipitation prediction using recurrent neural networks and long short-term memory,” Telkomnika (Telecommunication Computing Electronics and Control), vol. 18, no. 5, pp. 2525–2532, Oct. 2020, doi: 10.12928/TELKOMNIKA.V18I5.14887.
S. Poornima and M. Pushpalatha, “Prediction of rainfall using intensified LSTM based recurrent Neural Network with Weighted Linear Units,” Atmosphere (Basel), vol. 10, no. 11, Nov. 2019, doi: 10.3390/atmos10110668.
N. K. A. Appiah-Badu, Y. M. Missah, L. K. Amekudzi, N. Ussiph, T. Frimpong, and E. Ahene, “Rainfall Prediction Using Machine Learning Algorithms for the Various Ecological Zones of Ghana,” IEEE Access, vol. 10, pp. 5069–5082, 2022, doi: 10.1109/ACCESS.2021.3139312.
M. Putra, M. S. Rosid, and D. Handoko, “High-Resolution Rainfall Estimation Using Ensemble Learning Techniques and Multisensor Data Integration,” Sensors, vol. 24, no. 15, Aug. 2024, doi: 10.3390/s24155030.
E. G. Dada, H. J. Yakubu, and D. O. Oyewola, “Artificial Neural Network Models for Rainfall Prediction,” European Journal of Electrical Engineering and Computer Science, vol. 5, no. 2, pp. 30–35, Apr. 2021, doi: 10.24018/ejece.2021.5.2.313.
M. Mohammed, R. Kolapalli, N. Golla, and S. S. Maturi, “Prediction Of Rainfall Using Machine Learning Techniques,” INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH, vol. 9, p. 1, 2020, [Online]. Available: www.ijstr.org
A. Sharma and G. Nijhawan, “Rainfall Prediction Using Neural Network,” International Journal of Computer Science Trends and Technology, vol. 3, 2015, [Online]. Available: www.ijcstjournal.org
Q. Zou, Y. Liu, and X. Linge, “A survey on rainfall forecasting using artificial neural network’,” 2019.
I. S. Harahap, I. Z. Matondang, Suryanto, E. K. Indah, and I. Fitri, “Mapping Climate Classification of Oldeman in Agricultural Resources Management in South Tapanuli District,” IOP Conf Ser Mater Sci Eng, vol. 1156, no. 1, p. 012002, Jun. 2021, doi: 10.1088/1757-899x/1156/1/012002.
B. Ahmada et al., “Climate-based crop pattern determination using Standard Precipitation Index (SPI) and the Oldeman classification in Sangiran Site,” in IOP Conference Series: Earth and Environmental Science, Institute of Physics Publishing, Mar. 2020. doi: 10.1088/1755-1315/451/1/012019.
C. Dewi and R.-C. Chen, “Integrating Real-Time Weather Forecasts Data Using OpenWeatherMap and Twitter,” 2019. [Online]. Available: http://ejournal.uksw.edu/ijiteb
A. Musah et al., “An Evaluation of the OpenWeatherMap API versus INMET Using Weather Data from Two Brazilian Cities: Recife and Campina Grande,” Data (Basel), vol. 7, no. 8, Aug. 2022, doi: 10.3390/data7080106.
C. Sugiarto, F. Debora Abigael, Y. Faiz Athallah, A. Hari Saputra, and S. Tinggi Meteorologi Klimatologi dan Geofisika, “UTILIZATION OF HUBERREGRESSOR MACHINE LEARNING MODEL TO PREDICT CARBON MONOXIDE CONCENTRATION IN SURABAYA CITY,” JCEBT, vol. 8, no. 1, 2024, [Online]. Available: http://ojs.uma.ac.id/index.php/jcebt
M. T. Anwar, H. D. Purnomo, S. Y. J. Prasetyo, and D. K. Hartomo, “Decision Tree Learning Approach To Wildfire Modeling on Peat and Non-Peat Land in Riau Province,” 2018.
B. Mahesh, “Machine Learning Algorithms - A Review,” International Journal of Science and Research (IJSR), vol. 9, no. 1, pp. 381–386, Jan. 2020, doi: 10.21275/art20203995.
J. Kang, H. Wang, F. Yuan, Z. Wang, J. Huang, and T. Qiu, “Prediction of precipitation based on recurrent neural networks in jingdezhen, jiangxi province, China,” Atmosphere (Basel), vol. 11, no. 3, Mar. 2020, doi: 10.3390/atmos11030246.
N. Uzir, S. Raman, S. Banerjee, and R. S. Nishant Uzir Sunil R, “Experimenting XGBoost Algorithm for Prediction and Classification of Different Datasets,” International Journal of Control Theory and Applications, vol. 9, 2016, [Online]. Available: https://www.researchgate.net/publication/318132203
Aditya Gumilar, Sri Suryani Prasetiyowati, and Yuliant Sibaroni, “Performance Analysis of Hybrid Machine Learning Methods on Imbalanced Data (Rainfall Classification),” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 6, no. 3, pp. 481–490, Jul. 2022, doi: 10.29207/resti.v6i3.4142.
R. Adityo, “PENGARUH JUMLAH KATEGORI, RENTANG THRESHOLD DAN METODE ESTIMASI TERHADAP AKURASI SKOR TES PADA BEBERAPA MODEL POLITOMI,” 2014.
H. Zheng, J. Yuan, and L. Chen, “Short-Term Load Forecasting Using EMD-LSTM neural networks with a xgboost algorithm for feature importance evaluation,” Energies (Basel), vol. 10, no. 8, Aug. 2017, doi: 10.3390/en10081168.
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