A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition
| Autor: | Zhiping Nie, Zifan Gui, Qiuwen Zhang, Huafei Que, Gui Zhang, Xike Zhang |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Neural Network (NN)
Dongting Lake basin China 010504 meteorology & atmospheric sciences Mean squared error Health Toxicology and Mutagenesis 0208 environmental biotechnology daily land surface temperature forecasting data-driven hybrid model Ensemble Empirical Mode Decomposition (EEMD) Long Short-Term Memory (LSTM) lcsh:Medicine 02 engineering and technology 01 natural sciences Hilbert–Huang transform Article symbols.namesake 0105 earth and related environmental sciences Mathematics Artificial neural network lcsh:R Public Health Environmental and Occupational Health Temperature Models Theoretical Partial autocorrelation function Pearson product-moment correlation coefficient 020801 environmental engineering Mean absolute percentage error Recurrent neural network symbols Neural Networks Computer Nash–Sutcliffe model efficiency coefficient Algorithm Algorithms |
| Zdroj: | International Journal of Environmental Research and Public Health International Journal of Environmental Research and Public Health; Volume 15; Issue 5; Pages: 1032 International Journal of Environmental Research and Public Health, Vol 15, Iss 5, p 1032 (2018) |
| ISSN: | 1660-4601 |
| Popis: | Daily land surface temperature (LST) forecasting is of great significance for application in climate-related, agricultural, eco-environmental, or industrial studies. Hybrid data-driven prediction models using Ensemble Empirical Mode Composition (EEMD) coupled with Machine Learning (ML) algorithms are useful for achieving these purposes because they can reduce the difficulty of modeling, require less history data, are easy to develop, and are less complex than physical models. In this article, a computationally simple, less data-intensive, fast and efficient novel hybrid data-driven model called the EEMD Long Short-Term Memory (LSTM) neural network, namely EEMD-LSTM, is proposed to reduce the difficulty of modeling and to improve prediction accuracy. The daily LST data series from the Mapoling and Zhijaing stations in the Dongting Lake basin, central south China, from 1 January 2014 to 31 December 2016 is used as a case study. The EEMD is firstly employed to decompose the original daily LST data series into many Intrinsic Mode Functions (IMFs) and a single residue item. Then, the Partial Autocorrelation Function (PACF) is used to obtain the number of input data sample points for LSTM models. Next, the LSTM models are constructed to predict the decompositions. All the predicted results of the decompositions are aggregated as the final daily LST. Finally, the prediction performance of the hybrid EEMD-LSTM model is assessed in terms of the Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Pearson Correlation Coefficient (CC) and Nash-Sutcliffe Coefficient of Efficiency (NSCE). To validate the hybrid data-driven model, the hybrid EEMD-LSTM model is compared with the Recurrent Neural Network (RNN), LSTM and Empirical Mode Decomposition (EMD) coupled with RNN, EMD-LSTM and EEMD-RNN models, and their comparison results demonstrate that the hybrid EEMD-LSTM model performs better than the other five models. The scatterplots of the predicted results of the six models versus the original daily LST data series show that the hybrid EEMD-LSTM model is superior to the other five models. It is concluded that the proposed hybrid EEMD-LSTM model in this study is a suitable tool for temperature forecasting. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|