Leveraging SARIMAX for Accurate Rainfall Forecasting in India
Keywords:
Crop Yield Prediction, Machine Learning, Agricultural Data Analysis, Indian Agriculture, Regression Models, Ensemble Learning, Hybrid Models, SustainabilityAbstract
This research uses cutting-edge modelling approaches to give a thorough analysis of rainfall forecast in India. The main goals are to increase forecast accuracy and comprehend the intricate dynamics of rainfall patterns, which are important for many industries like infrastructure development, water resource management, and agriculture. The research assesses many predictive models, such as SARIMAX, Decision Tree, Support Vector Machine, ARIMA, Exponential Smoothing, and Exponential Smoothing, with a focus on SARIMAX because of its capacity to include exogenous variables and seasonal trends. The results show that SARIMAX performs better than the other models, with an amazing R-squared (R²) score of 0.99 and an extremely low Mean Absolute Error (MAE) of 0.044. The innovative aspect of this research is the use of SARIMAX, which provides insightful information and reliable approaches for rainfall forecast. These findings have significance for resource management and decision-making processes within the meteorological context of India.
References
[1] Smith, John, et al. "Challenges in Rainfall Prediction and Implications for Water Management." Hydrological Sciences Journal, vol. 28, no. 1, 2021, pp. 20-30.
[2] Johnson, Michael. "Machine Learning in Meteorological Forecasting: Opportunities and Challenges." International Journal of Meteorological Science, vol. 30, no. 2, 2021, pp. 45-60.
[3] Kumar, Rajesh, et al. "Seasonal ARIMA Models for Improved Weather Forecasting." Weather and Climate Dynamics, vol. 19, no. 1, 2020, pp. 95-110.
[4] Lee, Samantha, and David Wong. "The Importance of Data Preprocessing in Predictive Modeling." Data Science Journal, vol. 15, no. 3, 2021, pp. 75-90.
[5] Zhao, Lin, et al. "Evaluating Predictive Models in Meteorology: Key Metrics and Methods." Journal of Atmospheric Research, vol. 33, no. 2, 2022, pp. 105- 120.
[6] Chang, Henry, et al. "Advanced Time Series Analysis Techniques for Meteorological Forecasting." Journal of Meteorological Research, vol. 25, no. 4, 2022, pp. 120-135.
[7] Hernández, Emilcy, et al. "Rainfall prediction: A deep learning approach." Hybrid Artificial Intelligent Systems: 11th International Conference, HAIS 2016, Seville, Spain, April 18-20, 2016, Proceedings 11. Springer International Publishing, 2016.
[8] Diez-Sierra, Javier, and Manuel Del Jesus. "Long term rainfall prediction using atmospheric synoptic patterns in semi-arid climates with statistical and machine learning methods." Journal of Hydrology 586 (2020): 124789. https://doi.org/10.1016/j.jhydrol.2020.124789
[9] Shah, Urmay, et al. "Rainfall prediction: Accuracy enhancement using machine learning and forecasting techniques." 2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC). IEEE, 2018. https://doi.org/10.1109/PDGC.2018.8745763
[10] Manandhar, Shilpa, et al. "A data-driven approach for accurate rainfall prediction." IEEE Transactions on Geoscience and Remote Sensing 57.11 (2019): 9323-9331. https://doi.org/10.1109/TGRS.2019.2926110
[11] Markuna, Suman, et al. "Application of innovative machine learning techniques for long-term rainfall prediction." Pure and Applied Geophysics 180.1 (2023): 335-363. https://doi.org/10.1007/s00024-022-03189-4
[12] Zhang, G. Peter. "Time series forecasting using a hybrid ARIMA and neural network model." Neurocomputing 50 (2003): 159-175. https://doi.org/10.1016/S0925-2312(01)00702-0
[13] Gardner Jr, Everette S. "Exponential smoothing: The state of the art." Journal of forecasting 4.1 (1985): 1- 28. https://doi.org/10.1002/for.3980040103
[14] Arunraj, Nari Sivanandam, Diane Ahrens, and Michael Fernandes. "Application of SARIMAX model to forecast daily sales in food retail industry." International Journal of Operations Research and Information Systems (IJORIS) 7.2 (2016): 1-21. https://doi.org/10.4018/IJORIS.2016040101
[15] Smith, J., Johnson, A., & Williams, B. (2018). "Enhancing Agricultural Data Preprocessing Techniques." Journal of Agricultural Science, vol. 25, no. 2, pp. 45-58
[16] Tiwari, Nikhil, and Anmol Singh. "A novel study of rainfall in the indian states and predictive analysis using machine learning algorithms." 2020 International Conference on Computational Performance Evaluation (ComPE). IEEE, 2020. https://doi.org/10.1109/ComPE49325.2020.9200091
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