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Paper Information

Journal:   JOURNAL OF WATER AND SOIL CONSERVATION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES)   2016 , Volume 22 , Number 6; Page(s) 135 To 152.
 
Paper: 

PREDICTING MONTHLY PRECIPITATION OF KERMANSHAH SYNOPTIC STATION USING THE HYBRID MODEL OF NEURAL NETWORK AND WAVELET

 
 
Author(s):  MOZAFARI GH.A.*, SHAFIEE SH., HEMATI H.R.
 
* DEPT. OF GEOGRAPHY, UNIVERSITY OF YAZD
 
Abstract: 

Background and Objectives: Precipitation is one of the most important meteorology elements and recognition of its amount, its variation and prediction, are necessary to have an actual planning in agricultural, economic and social management. So the hydrologists and climatologists are paying attention to it. Due to precipitation importance in planning and crisis management, the goal of this research leads to the implementation of neural network and wavelet conversion hybrid model to monthly precipitation prediction of Kermanshah synoptic station.
Materials and Methods: In this research to predict monthly precipitation time series of Kermanshah’s synoptic station, five parameters namely: monthly precipitation average, relative humidity average, Maximum temperature average, minimum temperature average and wind speed average, were used within the forty years period (1970-2010). In order to quality control of given statistic and information, sequencing test is used. The results showed that given information is significantly homogeneous and according to nonlinear specifications of multiple time scales, neural network and wavelet model is used to precipitation prediction.
Results: In order to precipitation prediction, four parameters of relative humidity average, maximum temperature average, minimum temperature average and wind speed average, were used and using wavelet alternation decomposed into 8 sub-series and then these series were used to future monthly precipitation prediction as the entrance neural network model data. The correlation coefficient (R=0.874) of next month prediction denoted the relatively low efficiency of the neural network while the wavelet-neural network model correlation coefficient is 0.94.
Also, both model prediction precision decreases by increasing the number of output neurons delays. It should be noted that Meyer wavelet is used to wavelet- neurotic network prediction which has a high precision. Regarding the F statistic, variance analysis of homogeneity and heterogeneity indices of observed and predicted precipitation, is homogeneous in the confidence level of %99 (P<0.008).
Conclusion: The comparison between the driven results from wavelet conversion-neural network and driven results from a neural network application, showed that wavelet-neural network method had higher predicting precision than neural network and also predicting precision in both models decreases through increasing the number of delays of exit neurons. It is noteworthy that Meyer wavelet was used to neural network predicting which its simulated results had high precision.

 
Keyword(s): PRECIPITATION PREDICTION, HYBRID NEURAL NETWORK-WAVELET MODEL, KERMANSHAH SYNOPTIC STATION, MEYER WAVELET
 
References: 
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