Comparative Analysis of Linear Regression Models and XGBoost to Assess the Impact of ENSO on Rainfall in Ternate City in 2023

Firman Almaliky Gapri Amra; Anton Widodo; Muchamad Rizqy Nugraha

doi:10.63581/JoCPES.v5i1.02

Authors

Firman Almaliky Gapri Amra College of Meteorology Climatology and Geophysics Author
Anton Widodo College of Meteorology Climatology and Geophysics Author
Muchamad Rizqy Nugraha College of Meteorology Climatology and Geophysics Author

DOI:

https://doi.org/10.63581/JoCPES.v5i1.02

Keywords:

ENSO, ENSO, Rainfall, Rainfall, Linear Regression, Linear Regression, XGBoost, XGBoost, Machine Learning, Machine Learning

Abstract

The purpose of this study is to evaluate how well two prediction models—linear regression and XGBoost—perform in assessing how ENSO (El Niño-Southern Oscillation) affects rainfall in Ternate City in 2023. The Meteorology, Climatology, and Geophysics Agency (BMKG) provided monthly rainfall data, while the Bureau of Meteorology (BOM) in Australia provided ENSO index data. Performance indicators such Pearson correlation analysis, the coefficient of determination (R-squared), and mean squared error (MSE) were used in the evaluation. According to the findings, the two models perform differently when it comes to capturing the pattern of the link between rainfall and ENSO; XGBoost is more adaptable but has a tendency to overfit on small amounts of data, whereas linear regression obtains a better R-squared value.

Author Biographies

Firman Almaliky Gapri Amra, College of Meteorology Climatology and Geophysics

Student

Undergraduate Program in Applied of Instrumentation Meteorology, Climatology Geophysics
Anton Widodo, College of Meteorology Climatology and Geophysics

Lecturer

Undergraduate Program in Applied of Instrumentation Meteorology, Climatology Geophysics
Muchamad Rizqy Nugraha, College of Meteorology Climatology and Geophysics

Lecturer

Undergraduate Program in Applied of Instrumentation Meteorology, Climatology Geophysics

References

H. S. Lee, “General Rainfall Patterns in Indonesia and the Potential Impacts of Local Seas on Rainfall Intensity,” Water (Switzerland), vol. 7, no. 4, pp. 1751–1768, Apr. 2015, doi: 10.3390/w7041751.

L. Agustina, R. H. Virgianto, and A. N. Fitrianto, “Utilization of remote sensing data for mapping the effect of Indian Ocean Dipole (IOD) and El Nino Southern Oscillation (ENSO) in Sumatra Island,” in IOP Conference Series: Earth and Environmental Science, IOP Publishing Ltd, Nov. 2021. doi: 10.1088/1755-1315/893/1/012062.

J. T. Nugroho, D. Nurfitriani, Suwarsono, G. A. Chulafak, R. J. Manalu, and S. Harini, “Rainfall anomalies assessment during drought episodes of 2015 in Indonesia using CHIRPS Data,” in IOP Conference Series: Earth and Environmental Science, IOP Publishing Ltd, Apr. 2021. doi: 10.1088/1755-1315/739/1/012044.

S. Nurdiati, F. Bukhari, A. Sopaheluwakan, P. Septiawan, and V. Hutapea, “ENSO AND IOD IMPACT ANALYSIS OF EXTREME CLIMATE CONDITION IN PAPUA, INDONESIA,” Geographia Technica, vol. 19, no. 1, pp. 1–18, Mar. 2024, doi: 10.21163/GT_2024.191.01.

N. A. I. Baharuddin, M. Zainuddin, and Najamuddin, “The impact of ENSO-IOD on Decapterus spp. in Pangkajene Kepulauan and Barru Waters, Makassar Strait, Indonesia,” Biodiversitas, vol. 23, no. 11, pp. 5613–5622, 2022, doi: 10.13057/biodiv/d231110.

A. Parmar, K. Mistree, and M. Sompura, “Machine Learning Techniques For Rainfall Prediction: A Review,” 2017. [Online]. Available: https://www.researchgate.net/publication/319503839

D. Maulud and A. M. Abdulazeez, “A Review on Linear Regression Comprehensive in Machine Learning,” Journal of Applied Science and Technology Trends, vol. 1, no. 2, pp. 140–147, Dec. 2020, doi: 10.38094/jastt1457.

“Simple Linear Regression Multiple Linear Regression Polynomial Regression Underfitting and Overfitting Implementing Linear Regression in Python Python Packages for Linear Regression Simple Linear Regression With scikit-learn Multiple Linear Regression With scikit-learn Polynomial Regression With scikit-learn Advanced Linear Regression With statsmodels Beyond Linear Regression Conclusion.” [Online]. Available: https://realpython.com/linear-regression-in-python/1/21

T. Soares Dos Santos, D. Mendes, and R. Rodrigues Torres, “Artificial neural networks and multiple linear regression model using principal components to estimate rainfall over South America,” Nonlinear Process Geophys, vol. 23, no. 1, pp. 13–20, Jan. 2016, doi: 10.5194/npg-23-13-2016.

I. Papailiou, F. Spyropoulos, I. Trichakis, and G. P. Karatzas, “Artificial Neural Networks and Multiple Linear Regression for Filling in Missing Daily Rainfall Data,” Water (Switzerland), vol. 14, no. 18, Sep. 2022, doi: 10.3390/w14182892.

N. Uzir, S. Raman, S. Banerjee, and R. S. Nishant Uzir Sunil R, “Experimenting XGBoost Algorithm for Prediction and Classification of Different Datasets Experimenting XGBoost Algorithm for Prediction and Classifi cation of Different Datasets,” International Journal of Control Theory and Applications, vol. 9, 2016, [Online]. Available: https://www.researchgate.net/publication/318132203

A. Yasper, D. Handoko, M. Putra, H. K. Aliwarga, and M. S. R. Rosid, “Hyperparameters Optimization in XGBoost Model for Rainfall Estimation: A Case Study in Pontianak City,” Jurnal Penelitian Pendidikan IPA, vol. 9, no. 9, pp. 7113–7121, Sep. 2023, doi: 10.29303/jppipa.v9i9.3890.

T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, Aug. 2016, pp. 785–794. doi: 10.1145/2939672.2939785.

L. Ferreira, A. Pilastri, C. M. Martins, P. M. Pires, and P. Cortez, “A Comparison of AutoML Tools for Machine Learning, Deep Learning and XGBoost,” in Proceedings of the International Joint Conference on Neural Networks, Institute of Electrical and Electronics Engineers Inc., Jul. 2021. doi: 10.1109/IJCNN52387.2021.9534091.

Z. Kuang et al., “A Hybrid ENSO Prediction System Based on the FIO−CPS and XGBoost Algorithm,” Remote Sens (Basel), vol. 15, no. 7, Apr. 2023, doi: 10.3390/rs15071728.

A. Kurniadi, E. Weller, S. K. Min, and M. G. Seong, “Independent ENSO and IOD impacts on rainfall extremes over Indonesia,” International Journal of Climatology, vol. 41, no. 6, pp. 3640–3656, May 2021, doi: 10.1002/joc.7040.

A. Santoso, M. J. Mcphaden, and W. Cai, “The Defining Characteristics of ENSO Extremes and the Strong 2015/2016 El Niño,” Dec. 01, 2017, Blackwell Publishing Ltd. doi: 10.1002/2017RG000560.

C. Wang, C. Deser, J. Y. Yu, P. DiNezio, and A. Clement, “El Niño and Southern Oscillation (ENSO): A Review,” in Coral Reefs of the World, vol. 8, Springer Nature, 2017, pp. 85–106. doi: 10.1007/978-94-017-7499-4_4.

J. Pesantez-Narvaez, M. Guillen, and M. Alcañiz, “Predicting motor insurance claims using telematics data—XGboost versus logistic regression,” Risks, vol. 7, no. 2, Jun. 2019, doi: 10.3390/risks7020070.

J. Wu et al., “Prediction and Screening Model for Products Based on Fusion Regression and XGBoost Classification,” Comput Intell Neurosci, vol. 2022, 2022, doi: 10.1155/2022/4987639.