Forecasting Hourly Intermittent Rainfall by Deep Belief Networks with Simple Exponential Smoothing

Accurate rainfall forecasting is essential in planning and managing water resource systems efficiently. However, intermittent rainfall patterns increase the difficulty of accurately forecasting rainfall values. Deep learning techniques have recently been popular and powerful in forecasting. Thus, th...

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Bibliographic Details
Published in:Water resources management 2022-10, Vol.36 (13), p.5207-5223
Main Authors: Huang, Guo-Yu, Lai, Chi-Ju, Pai, Ping-Feng
Format: Article
Language:English
Subjects:
Artificial neural networks
Atmospheric Sciences
Back propagation
Back propagation networks
Belief networks
Civil Engineering
Data smoothing
Deep learning
Earth and Environmental Science
Earth Sciences
Environment
Forecasting
Genetic algorithms
Geotechnical Engineering & Applied Earth Sciences
Hydrogeology
Hydrology/Water Resources
Independent variables
Machine learning
Mathematical models
Neural networks
Precipitation
Procedures
Rain
Rainfall
Smoothing
Support vector machines
Water management
Water resources
Water resources management
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Summary:Accurate rainfall forecasting is essential in planning and managing water resource systems efficiently. However, intermittent rainfall patterns increase the difficulty of accurately forecasting rainfall values. Deep learning techniques have recently been popular and powerful in forecasting. Thus, this study employed deep belief networks with a simple exponential smoothing procedure (DBNSES) to forecast hourly intermittent rainfall values in Taiwan. Weather factors were used as independent variables to forecast rainfall volume. The simple exponential smoothing data preprocessing procedure was used to deal with the intermittent data patterns. The other three forecasting models, namely the least squares support vector regression (LSSVR), the generalized regression neural network (GRNN), and the backpropagation neural network (BPNN), were employed to forecast rainfall using the same data sets. In addition, genetic algorithms were utilized to determine the parameters of four forecasting models. The empirical results indicate that the developed DBNSES models are superior to the other forecasting models in terms of forecasting accuracy. In addition, the DBNSES can obtain smaller values of RMSE than those in the previous studies. Therefore, the DBNSES model is a suitable and effective way of forecasting rainfall with intermittent data patterns.
ISSN:0920-4741
1573-1650
DOI:10.1007/s11269-022-03300-3