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

Atmosphere

Jing Lu¹  Shengjun Xue¹  Xiakun Zhang⁵  Shuyu Zhang²  Wanshun Lu³  Robinson I. Negron-Juarez⁴ 

[1] School of Computer and Software, Nanjing University of Information Science and Technology, 219 Ningliu Road, Nanjing 210044, China; E-Mails:;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;Shanxi Provincial Meteorological Bureau, 80 Pingyang Road, Taiyuan 030002, China; E-Mail:School of Computer and Software, Nanjing University of Information Science and Technology, 219 Ningliu Road, Nanjing 210044, China;;School of Atmospheric Science, Nanjing University of Information Science and Technology, 219 Ningliu Road, Nanjing 210044, China; E-Mail:

关键词: fuzzy logic;    neural network;    weather prediction model;    sequential relation among fuzzy sets;    precipitation prediction;   

DOI  :  10.3390/atmos5040788

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.