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Decoding Pakistan's rainfall: optimizing predictions from ARIMA to SARIMA with seasonal adjustments
- 1 Dalian University of Technology, Leicester International Institute, Panjin, 124000, China
* Author to whom correspondence should be addressed.
Abstract
This study aims to improve the accuracy of rainfall forecasting in Pakistan by comparatively analyzing the performance of two models, ARIMA and SARIMA, to optimize the forecasting methodology. The study points out that although the ARIMA model performs well in time series analysis, it has shortcomings in handling data with significant seasonal variations. Therefore, the SARIMA model was introduced and it performed better in forecasting seasonal variations. Future research should consider combining the SARIMA model with models that can explain global climate phenomena such as El Niño and La Niña to enhance the accuracy of forecasts. In addition, ways to automate and improve the selection of model parameters should be explored to make the SARIMA model more efficient and accurate. The introduction of the SARIMA model has significantly improved prediction accuracy and contributed to more efficient planning and management of water resources. Areas where improvements can be made include reserving water resources in advance during the dry season or allocating water resources appropriately during the rainy season to support irrigation agriculture, urban water supply, flood control measures, etc. These enhanced forecasting methods help Pakistan cope with climate change challenges.
Keywords
Rainfall prediction, ARIMA model, SARIMA model, seasonal adjustments, Pakistan.
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Cite this article
Luo,Q. (2024). Decoding Pakistan's rainfall: optimizing predictions from ARIMA to SARIMA with seasonal adjustments. Theoretical and Natural Science,42,73-83.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2nd International Conference on Mathematical Physics and Computational Simulation
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