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Algorithms 2018, 11(9), 132; https://doi.org/10.3390/a11090132

Study of Precipitation Forecast Based on Deep Belief Networks

1,2,* , 1,2
and
3
1
School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
2
Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of Information Science and Technology, Nanjing 210044, China
3
Jiangsu Longchuan Water Conservancy Construction Co., Ltd., Yangzhou 225200, China
*
Author to whom correspondence should be addressed.
Received: 21 July 2018 / Revised: 20 August 2018 / Accepted: 30 August 2018 / Published: 4 September 2018
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Abstract

Due to the impact of weather forecasting on global human life, and to better reflect the current trend of weather changes, it is necessary to conduct research about the prediction of precipitation and provide timely and complete precipitation information for climate prediction and early warning decisions to avoid serious meteorological disasters. For the precipitation prediction problem in the era of climate big data, we propose a new method based on deep learning. In this paper, we will apply deep belief networks in weather precipitation forecasting. Deep belief networks transform the feature representation of data in the original space into a new feature space, with semantic features to improve the predictive performance. The experimental results show, compared with other forecasting methods, the feasibility of deep belief networks in the field of weather forecasting. View Full-Text
Keywords: deep belief networks; precipitation prediction deep belief networks; precipitation prediction
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Du, J.; Liu, Y.; Liu, Z. Study of Precipitation Forecast Based on Deep Belief Networks. Algorithms 2018, 11, 132.

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