Machine learning model combined with CEEMDAN algorithm for monthly precipitation prediction

Shen, Zi-yi; Ban, Wenchao

doi:10.1007/s12145-023-01011-w

Cited by 9 publications

(2 citation statements)

References 24 publications

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“…The research results showed that this method significantly improves the consistency between observation and estimation. Additionally, this method effectively reduced the impact of noise on the model [46]. Zhang et al used the monthly rainfall data of Zhongwei City for 18 years (1999-2016) as training samples and constructed a rainfall prediction model using CEEMDAN-PSO-ELM.…”

Section: Precipitation Predictionmentioning

confidence: 99%

Research on Water Resource Modeling Based on Machine Learning Technologies

Liu,

Zhou,

Yang

et al. 2024

Water

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Water resource modeling is an important means of studying the distribution, change, utilization, and management of water resources. By establishing various models, water resources can be quantitatively described and predicted, providing a scientific basis for water resource management, protection, and planning. Traditional hydrological observation methods, often reliant on experience and statistical methods, are time-consuming and labor-intensive, frequently resulting in predictions of limited accuracy. However, machine learning technologies enhance the efficiency and sustainability of water resource modeling by analyzing extensive hydrogeological data, thereby improving predictions and optimizing water resource utilization and allocation. This review investigates the application of machine learning for predicting various aspects, including precipitation, flood, runoff, soil moisture, evapotranspiration, groundwater level, and water quality. It provides a detailed summary of various algorithms, examines their technical strengths and weaknesses, and discusses their potential applications in water resource modeling. Finally, this paper anticipates future development trends in the application of machine learning to water resource modeling.

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Section: Precipitation Predictionmentioning

confidence: 99%

Research on Water Resource Modeling Based on Machine Learning Technologies

Liu,

Zhou,

Yang

et al. 2024

Water

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“…Although the improved EEMD method effectively reduces modal aliasing, there are too many low-frequency components in the subsequence [12]. Shen et al used the CEEMDAN decomposition to overcome the above problems and improve the efficiency and accuracy of decomposition [13].…”

Section: Introductionmentioning

confidence: 99%

A short-term prediction model for photovoltaic power forecasting based on CEEMDAN-CS-LSTM

mao,

2024

Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023)

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Accurate prediction of PV power is essential to maximize solar energy utilization, enhance the performance of PV power generation systems, and ensure the dependability of power infrastructures. In this paper, an approach based on CS-LSTM neural network is proposed for short term power forecasting. The proposed approach involves processing the data using CEEMDAN, followed by the application of the CS algorithm to determine the optimal LSTM hyperparameters. To evaluate the accuracy and dependability of the model, CEEMDAN-CS-LSTM is compared to other models. Experiments show that CEEMDAN-CS-LSTM outperforms the other models in terms of prediction accuracy and stability.

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