Multidimensional precipitation index prediction based on CNN-LSTM hybrid framework

• URL: http://arxiv.org/abs/2504.20442v1
• Date: Tue, 29 Apr 2025 05:32:43 GMT
• Title: Multidimensional precipitation index prediction based on CNN-LSTM hybrid framework
• Authors: Yuchen Wang, Pengfei Jia, Zhitao Shu, Keyan Liu, Abdul Rashid Mohamed Shariff,
• Abstract summary: This study proposes a multidimensional precipitation index prediction model based on a CNN- LSTM hybrid framework.<n>The dataset is sourced from Pune, Maharashtra, India, covering monthly mean precipitation data from 1972 to 2002.<n> Experimental results show that the model achieves a root mean square error (RMSE) of 6.752, which demonstrates a significant advantage over traditional time series prediction methods.
• Score: 5.484147776963721
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the intensification of global climate change, accurate prediction of weather indicators is of great significance in disaster prevention and mitigation, agricultural production, and transportation. Precipitation, as one of the key meteorological indicators, plays a crucial role in water resource management, agricultural production, and urban flood control. This study proposes a multidimensional precipitation index prediction model based on a CNN- LSTM hybrid framework, aiming to improve the accuracy of precipitation forecasts. The dataset is sourced from Pune, Maharashtra, India, covering monthly mean precipitation data from 1972 to 2002. This dataset includes nearly 31 years (1972-2002) of monthly average precipitation, reflecting the long-term fluctuations and seasonal variations of precipitation in the region. By analyzing these time series data, the CNN-LSTM model effectively captures local features and long-term dependencies. Experimental results show that the model achieves a root mean square error (RMSE) of 6.752, which demonstrates a significant advantage over traditional time series prediction methods in terms of prediction accuracy and generalization ability. Furthermore, this study provides new research ideas for precipitation prediction. However, the model requires high computational resources when dealing with large-scale datasets, and its predictive ability for multidimensional precipitation data still needs improvement. Future research could extend the model to support and predict multidimensional precipitation data, thereby promoting the development of more accurate and efficient meteorological prediction technologies.

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