EMD-Based Predictive Deep Belief Network for Time Series Prediction: An Application to Drought Forecasting

Drought is a stochastic natural feature that arises due to intense and persistent shortage of precipitation. Its impact is mostly manifested as agricultural and hydrological droughts following an initial meteorological phenomenon. Drought prediction is essential because it can aid in the preparednes...

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Bibliographic Details
Published in:Hydrology 2018-03, Vol.5 (1), p.18
Main Authors: Agana, Norbert A., Homaifar, Abdollah
Format: Article
Language:English
Subjects:
Accuracy
Agricultural management
Artificial intelligence
Belief networks
Climate change
Data processing
Decomposition
deep belief network
detrended fluctuation analysis
Drought
drought forecasting
Drought index
Emergency preparedness
Empirical analysis
empirical mode function
Forecasting
Hydrology
International conferences
intrinsic mode functions
Mathematical models
Model accuracy
Multilayer perceptrons
Neural networks
Noise reduction
Precipitation
Prediction models
Regression analysis
River basins
Rivers
Scaling
Stochasticity
Stream discharge
Stream flow
Support vector machines
Time series
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Summary:Drought is a stochastic natural feature that arises due to intense and persistent shortage of precipitation. Its impact is mostly manifested as agricultural and hydrological droughts following an initial meteorological phenomenon. Drought prediction is essential because it can aid in the preparedness and impact-related management of its effects. This study considers the drought forecasting problem by developing a hybrid predictive model using a denoised empirical mode decomposition (EMD) and a deep belief network (DBN). The proposed method first decomposes the data into several intrinsic mode functions (IMFs) using EMD, and a reconstruction of the original data is obtained by considering only relevant IMFs. Detrended fluctuation analysis (DFA) was applied to each IMF to determine the threshold for robust denoising performance. Based on their scaling exponents, irrelevant intrinsic mode functions are identified and suppressed. The proposed method was applied to predict different time scale drought indices across the Colorado River basin using a standardized streamflow index (SSI) as the drought index. The results obtained using the proposed method was compared with standard methods such as multilayer perceptron (MLP) and support vector regression (SVR). The proposed hybrid model showed improvement in prediction accuracy, especially for multi-step ahead predictions.
ISSN:2306-5338
2306-5338
DOI:10.3390/hydrology5010018