Application of precipitation prediction model based on multi-model coupling in agricultural irrigation
Keywords:
Precipitation Prediction, LSTM, SVM, CEEMDAN, Agricultural IrrigationAbstract
Agricultural irrigation faces escalating challenges in water management due to the ongoing impacts of climate change. In this study, our goal is to enhance the precision of precipitation prediction to offer more dependable support for agricultural irrigation decision-making. We adopted a multi-model fusion algorithm based on long and short-term memory networks and integrated various data sources, including meteorological station observations and satellite remote sensing gridded data, to construct a comprehensive precipitation prediction model.
The objective of this research is to develop an efficient and accurate precipitation prediction model that can provide scientific decision support for agricultural irrigation. Through rigorous comparison of different models, we identified the optimal combination to improve the model's robustness and accuracy. Our experimental results reveal that multi-model fusion exhibits higher accuracy and stability in precipitation prediction compared to a single model.
Our study further validates the substantial advantages of multi-model fusion in enhancing prediction accuracy and emphasizes the critical role of integrating data from multiple sources for optimal model performance. By furnishing more reliable predictive information for agricultural irrigation decision-making, this study introduces new methodologies and ideas for enhancing agricultural water use efficiency and addressing the challenges posed by climate change.
In terms of innovations, this study leverages a multi-model fusion algorithm grounded in long and short-term memory networks, and integrates multi-source data to offer a comprehensive and reliable solution for precipitation prediction. This approach provides valuable insights for future similar studies and contributes to the advancement of agricultural water management practices.
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*******************Cite this Article*******************
APA:
Wang, B., Lan, X., Peng, Y., Qiu, Z., & Peng, R. (2024). Application of precipitation prediction model based on multi-model coupling in agricultural irrigation. International Scientific Technical and Economic Research, 2(3), 10–21. http://www.istaer.online/index.php/Home/article/view/No.2461
GB/T 7714-2015:
Wang Bo, Lan Xingchen, Peng Yue, Qiu Zitong, Peng Ran. Application of precipitation prediction model based on multi-model coupling in agricultural irrigation[J]. International Scientific Technical and Economic Research, 2024, 2(3): 10–21. http://www.istaer.online/index.php/Home/article/view/No.2461
MLA:
Wang, Bo, et al. "Application of precipitation prediction model based on multi-model coupling in agricultural irrigation." International Scientific Technical and Economic Research, 2.3 (2024): 10-21. http://www.istaer.online/index.php/Home/article/view/No.2461
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