Study of Precipitation Forecast Based on Deep Belief Networks
In one sentence. Applies deep belief networks to precipitation forecasting, claiming better results than conventional forecasting methods; quantitative results are not given in the abstract.
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.
Labels
- Topic
- Local & station forecasting
- Type
- New model or method
- Method
- Other
- Time scale
- 6 h–3 days
- Domain
- Local / station
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.3390/a11090132 OpenAlex:W2889283791
- Added
- 2026-10-22
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