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Atmosphere 2014, 5(4), 788-805; doi:10.3390/atmos5040788

Neural Fuzzy Inference System-Based Weather Prediction Model and Its Precipitation Predicting Experiment

1
School of Computer and Software, Nanjing University of Information Science and Technology, 219 Ningliu Road, Nanjing 210044, China
2
Shanxi Provincial Meteorological Bureau, 80 Pingyang Road, Taiyuan 030002, China
3
Earth System Science Interdisciplinary Center, University of Maryland, 5825 University Research Court, College Park, MD 20740, USA
4
School of Atmospheric Science, Nanjing University of Information Science and Technology, 219 Ningliu Road, Nanjing 210044, China
5
Key Laboratory of Arid Climatic Change and Reducing Disaster of Gansu Province, Lanzhou Institute of Arid Meteorology, China Meteorological Administration, 2070 Donggang East Road, Lanzhou 730020, China
*
Author to whom correspondence should be addressed.
Received: 23 September 2014 / Accepted: 8 October 2014 / Published: 3 November 2014
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Abstract

We propose a weather prediction model in this article based on neural network and fuzzy inference system (NFIS-WPM), and then apply it to predict daily fuzzy precipitation given meteorological premises for testing. The model consists of two parts: the first part is the “fuzzy rule-based neural network”, which simulates sequential relations among fuzzy sets using artificial neural network; and the second part is the “neural fuzzy inference system”, which is based on the first part, but could learn new fuzzy rules from the previous ones according to the algorithm we proposed. NFIS-WPM (High Pro) and NFIS-WPM (Ave) are improved versions of this model. It is well known that the need for accurate weather prediction is apparent when considering the benefits. However, the excessive pursuit of accuracy in weather prediction makes some of the “accurate” prediction results meaningless and the numerical prediction model is often complex and time-consuming. By adapting this novel model to a precipitation prediction problem, we make the predicted outcomes of precipitation more accurate and the prediction methods simpler than by using the complex numerical forecasting model that would occupy large computation resources, be time-consuming and which has a low predictive accuracy rate. Accordingly, we achieve more accurate predictive precipitation results than by using traditional artificial neural networks that have low predictive accuracy. View Full-Text
Keywords: fuzzy logic; neural network; weather prediction model; sequential relation among fuzzy sets; precipitation prediction fuzzy logic; neural network; weather prediction model; sequential relation among fuzzy sets; precipitation prediction
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Lu, J.; Xue, S.; Zhang, X.; Zhang, S.; Lu, W. Neural Fuzzy Inference System-Based Weather Prediction Model and Its Precipitation Predicting Experiment. Atmosphere 2014, 5, 788-805.

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