Climate Change Analysis in Malaysia Using Machine Learning
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Abstract
Climate change presents significant challenges to ecosystems, economies, and societies globally. In Malaysia, a tropical country highly dependent on its natural resources, the impacts are evident in altered rainfall patterns, rising temperatures, and extreme weather events. Despite these challenges, many studies still predominantly rely on traditional statistical methods, which limit their capacity for making accurate climate predictions and developing effective policy solutions.This study effectively addresses the existing gap in research by analyzing extensive historical climate data using advanced machine learning (ML) techniques. The primary focus is on accurately forecasting trends in both precipitation patterns and surface air temperature fluctuations. Performance measures like Mean Absolute Error (MAE), Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) are used to assess three ML models: Support Vector Regression (SVR), Random Forest Regression (RFR) and Linear Regression (LR). The findings demonstrate that LR performs better than the other models in forecasting patterns of precipitation and temperature. The results suggest a significant increase in temperature and unpredictable patterns of precipitation, and that poses major implications for agriculture, infrastructure resilience, and water management. Malaysia's climate resilience is improved by this research, which promotes data-driven policymaking by assessing current climate adaptation methods and offering practical ideas.
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References
M. F. Abdullah, M. Z. Mat Amin, M. F. Mohamad, M. Mohamad Ideris, Z. Zainol, and N. Y. Yussof, “N-HyDAA - Big Data Analytics for Malaysia Climate Change Knowledge Management,” pp. 10–1. doi: 10.29007/5b3v.
A. H. Ali and R. Thakkar, “Climate Changes through Data Science: Understanding and Mitigating Environmental Crisis,” Mesopotamian Journal of Big Data, vol. 2023, pp. 125–137, Dec. 2023, doi: 10.58496/MJBD/2023/017.
L. Y. Leng, O. H. Ahmed, and M. B. Jalloh, “Brief review on climate change and tropical peatlands,” Geoscience Frontiers, vol. 10, no. 2, pp. 373–380, Mar. 2019, doi: 10.1016/j.gsf.2017.12.018.
S. Das, C. Chatterjee, and S. Chakraborty, “A Machine Learning Approach to Re-Classification of Climate Zones Based on Multiple Rain Features Over India,” in IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, IEEE, Jul. 2019, pp. 7752–7754. doi: 10.1109/IGARSS.2019.8898422.
S. Vázquez-Ramírez, M. Torres-Ruiz, R. Quintero, K. T. Chui, and C. Guzmán Sánchez-Mejorada, “An Analysis of Climate Change Based on Machine Learning and an Endoreversible Model,” Mathematics, vol. 11, no. 14, p. 3060, Jul. 2023, doi: 10.3390/math11143060.
S. Zainudin, D. S. Jasim, and A. Abu Bakar, “Comparative Analysis of Data Mining Techniques for Malaysian Rainfall Prediction,” Int J Adv Sci Eng Inf Technol, vol. 6, no. 6, p. 1148, Dec. 2016, doi: 10.18517/ijaseit.6.6.1487.
B. S. Sidhu, Z. Mehrabi, N. Ramankutty, and M. Kandlikar, “How can machine learning help in understanding the impact of climate change on crop yields?,” Environmental Research Letters, vol. 18, no. 2, p. 024008, Feb. 2023, doi: 10.1088/1748-9326/acb164.
T. Ferdousi et al., “A Machine Learning Framework to Explain Complex Geospatial Simulations: A Climate Change Case Study,” in 2023 Winter Simulation Conference (WSC), IEEE, Dec. 2023, pp. 2990–3001. doi: 10.1109/WSC60868.2023.10408406.
R. Wamanse and T. Patil, “Analysis of various climate change parameters in India using machine learning,” Jan. 2022.
J.-L. Goh, S.-B. Ho, and C.-H. Tan, “Weather-Based Arthritis Tracking: A Mobile Mechanism for Preventive Strategies,” Journal of Informatics and Web Engineering, vol. 3, no. 1, pp. 210–225, Feb. 2024, doi: 10.33093/jiwe.2024.3.1.14.
W.-X. Ong, S.-B. Ho, and C.-H. Tan, “Enhancing Migraine Management System through Weather Forecasting for a Better Daily Life,” Journal of Informatics and Web Engineering, vol. 2, no. 2, pp. 201–217, Sep. 2023, doi: 10.33093/jiwe.2023.2.2.15.
H. L., “Tackling Climate Change with Machine Learning,” 2021.
R. A. A. Rashid, P. N. E. Nohuddin, and Z. Zainol, “Analyzing Climate Variability in Malaysia Using Association Rule Mining,” International Journal of Engineering & Technology, vol. 7, no. 4.34, p. 394, Dec. 2018, doi: 10.14419/ijet.v7i4.34.26881.
H. A. Rahman, “CLIMATE CHANGE SCENARIOS IN MALAYSIA: ENGAGING THE PUBLIC,” International Journal of Malay-Nusantara Studies, vol. 1, no. 2, 2018.
N. A. Ismail and W. Z. Wan Zain, “Statistical Analysis of Rainfall Data in Peninsular Malaysia using Strong Transformation of Cubic Spline Analysis,” Jurnal Kejuruteraan, vol. si5, no. 2, pp. 237–240, Nov. 2022, doi: 10.17576/jkukm-2022-si5(2)-26.
M. N. I. Kassim et al., “Climate Change Research in Malaysia: A Scientometric Analysis,” Journal of Scientometric Research, vol. 12, no. 3, pp. 534–543, Nov. 2023, doi: 10.5530/jscires.12.3.051.
N. A. F. Sulaiman, S. M. Shaharudin, S. Ismail, N. H. Zainuddin, M. L. Tan, and Y. Abd Jalil, “Predictive Modelling of Statistical Downscaling Based on Hybrid Machine Learning Model for Daily Rainfall in East-Coast Peninsular Malaysia,” Symmetry (Basel), vol. 14, no. 5, p. 927, May 2022, doi: 10.3390/sym14050927.
V. H., “Climate Change Analysis Using Machine Learning,” International Journal of Science and Research (IJSR), vol. 9, no. 8, pp. 973–977, 2020.
K. Chattrairat, W. Wongseree, and A. Leelasantitham, “Comparisons of Machine Learning Methods of Statistical Downscaling Method: Case Studies of Daily Climate Anomalies in Thailand,” Journal of Web Engineering, Jul. 2021, doi: 10.13052/jwe1540-9589.2057.
V. Shevchenko et al., “Climate Change Impact on Agricultural Land Suitability: An Interpretable Machine Learning-Based Eurasia Case Study,” IEEE Access, vol. 12, pp. 15748–15763, 2024, doi: 10.1109/ACCESS.2024.3358865.
P. Mangal, A. Rajesh, and R. Misra, “Big Data in Climate Change Research: opportunities and Challenges,” in 2020 International Conference on Intelligent Engineering and Management (ICIEM), IEEE, Jun. 2020, pp. 321–326. doi: 10.1109/ICIEM48762.2020.9160174.
T. Ladi, S. Jabalameli, and A. Sharifi, “Applications of machine learning and deep learning methods for climate change mitigation and adaptation,” Environ Plan B Urban Anal City Sci, vol. 49, no. 4, pp. 1314–1330, May 2022, doi: 10.1177/23998083221085281.
Visual Crossing, “Weather Data Documentation.” Accessed: Sep. 23, 2024. [Online]. Available: https://www.visualcrossing.com/resources/documentation/weather-data/weather-data-documentation/