Research on Precipitation Forecast Based on LSTM–CP Combined Model

Guo, Yan; Tang, Wei; Hou, Guirong; Pan, Fei; Wang, Yübo; Wang, Wei

doi:10.3390/su132111596

Cited by 5 publications

(2 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results show that the combined ARIMA-SVR model has a better forecasting effect and higher forecasting accuracy than the single ARIMA or SVR model. Guo et al [26] proposed the LSTM-CP combination model, which is composed of the LSTM and Chebyshev polynomial (CP), for precipitation forecasting. Through theoretical analysis and experimental comparison, the LSTM-CP combination model requires fewer parameters and a shorter running time than the LSTM network.…”

Section: Predictive Model Based On a Combination Of Different Methodsmentioning

confidence: 99%

Temperature Prediction Based on STOA-SVR Rolling Adaptive Optimization Model

Shen

et al. 2023

Sustainability

View full text Add to dashboard Cite

In this paper, a support vector regression (SVR) adaptive optimization rolling composite model with a sooty tern optimization algorithm (STOA) has been proposed for temperature prediction. Firstly, aiming at the problem that the algorithm tends to fall into the local optimum, the model introduces an adaptive Gauss–Cauchy mutation operator to effectively increase the population diversity and search space and uses the improved algorithm to optimize the key parameters of the SVR model, so that the SVR model can mine the linear and nonlinear information in the data well. Secondly, the rolling prediction is integrated into the SVR prediction model, and the real-time update and self-regulation principles are used to continuously update the prediction, which greatly improves the prediction accuracy. Finally, the optimized STOA-SVR rolling forecast model is used to predict the final temperature. In this study, the global mean temperature data set from 1880 to 2022 is used for empirical analysis, and a comparative experiment is set up to verify the accuracy of the model. The results show that compared with the seasonal autoregressive integrated moving average (SARIMA), feedforward neural network (FNN) and unoptimized STOA-SVR-LSTM, the prediction performance of the proposed model is better, and the root mean square error is reduced by 6.33–29.62%. The mean relative error is reduced by 2.74–47.27%; the goodness of fit increases by 4.67–19.94%. Finally, the global mean temperature is predicted to increase by about 0.4976 °C in the next 20 years, with an increase rate of 3.43%. The model proposed in this paper not only has a good prediction accuracy, but also can provide an effective reference for the development and formulation of meteorological policies in the future.

show abstract

Section: Predictive Model Based On a Combination Of Different Methodsmentioning

confidence: 99%

Temperature Prediction Based on STOA-SVR Rolling Adaptive Optimization Model

Shen

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…They showed that all models performed similarly, except for straightforward time-lagged ANNs, which were slightly worse. Combining Chebyshev polynomials (CP) with ANNs, Guo et al (2021) used weather data to train LSTMs followed by ANNs with CP as the activation function. Soundararajan (2021) forecasted rainfall for the next day using weather parameters employing a CNN for Australia.…”

Section: D Rainfall Forecastingmentioning

confidence: 99%

A Systematic Review of Deep Learning Applications in Interpolation and Extrapolation of Precipitation Data

Sit¹,

Demiray²,

Demir³

2022

Preprint

View full text Add to dashboard Cite

With technological enhancements, the volume, velocity, and variety (3Vs) of the raw digital Earth data have increased in recent years. Due to the increased availability of computer resources and the growing popularity of deep learning applications, this data has been a crucial source for data-driven studies that have transformed the fields of climate and earth science. One of the critical data sources is precipitation supporting climate and earth science studies on modeling, forecasting, and preparedness for extreme events (i.e., floods, droughts, pollution transport). In this study, we worked on an extensive review of manuscripts focusing on use of deep learning methods to tackle challenges either to improve the quality or extrapolate (forecast) rainfall datasets. The purpose of this study is to summarize the most recent developments in deep learning approaches for forecasting rainfall or improving rainfall datasets, as well as highlighting issues, shortcomings, and open questions with insightful recommendations for future directions.

show abstract