Abstract
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 Zhijiang 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.
Highlights
Accurate forecasting of daily land surface temperature (LST) is highly important for various fields, including weather maintenance services, agriculture, eco-environment, and industry [1]
Hochreiter and Schmidhuber [53] proposed a special type of Recurrent Neural Network (RNN), namely the Long-Term Short where in Equations (6)–(11), xt is the input at time t; ht−1 and ht t are the outputs of the hidden layer at Memory (LSTM) recurrent neural network
Ensemble Empirical Mode Decomposition (EMD) (EEMD)-Long Short-Term Memory (LSTM) model outperforms the other models with the smallest Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE), results close to the training data set have less residuals and normalized residuals, while far from the as well as the largest CC and Nash-Sutcliffe Coefficient of Efficiency (NSCE) for daily LST forecasting
Summary
Accurate forecasting of daily land surface temperature (LST) is highly important for various fields, including weather maintenance services, agriculture, eco-environment, and industry [1]. Many data-driven models have been proposed for time series forecasting. Hybrid data-driven models, in the last few years, have received much attention and have been widely adopted and applied in hydro-climate analysis to improve prediction accuracy as powerful alternative modeling tools. Different basis functions can produce different results [41] To solve this problem, a self-adaptive decomposition method has been introduced by Wu and Huang [42] for time series processing: The Ensemble Empirical Mode. Wang et al [30] proposed a hybrid model that utilized the EEMD coupled with ANN for long-term runoff forecasting. A hybrid data-driven model, EEMD coupled with Long Short-Term Memory (LSTM), namely the EEMD-LSTM, is proposed for daily LST data series forecasting.
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