Long term rainfall forecasting by integrated artificial neural network-fuzzy logic-wavelet model in Karoon basin

Scientific Research and Essays

Sarah Afshin¹ 

关键词:  ;    Intelligent networks;    long-term prediction;    meteorological signals;    artificial neural network;     ;    fuzzy logic;    wavelet function.;   

DOI  :  10.5897/SRE10.448

学科分类：社会科学、人文和艺术（综合）

来源: Academic Journals

【 摘 要 】

Physical, mathematical models and statistical distribution are applied to forecasting, whereas in natural resources, it is difficult to choose models that are closed to reality. Rainfall forecasting as an important dynamic process is ever favored by the researchers. Analyzing the behavior of these phenomena by intelligent systems is completely better than classical methods, because of high non-linear dynamic atmospheric phenomena. In this paper, a long term forecasting method is presented by a combination of intelligent methods with theuse of the past month rainfall in karoon basin and global meteorological signals such as southern oscillation index (SOI), north athletics oscillation (NAO), sea level pressure (SLP), sea surface temperature (SST) and 41 years historical data. This method is obtained by the combination of artificialneural network, fuzzy logic and wavelet functions. In this model, several scenarios have been examined for the karoon basin of Iran, through the signals. SST and NAO signals show the best results, and then, the long-term forecasts are done for periods of six months, one year and two years. The results of the integrated model showed superior results when compared to the two-year forecasts to predict the six-month and annual periods. As a result ofthe root mean squared error, predictingthe two-year and annual periods is 6.22 and 7.11, respectively. However, the predicted six months shows 13.15.

【 授权许可】

CC BY