Hybrid denoisingjittering data processing approac

Authors: Afshin Partovian Vahid Nourani Mohammad Taghi Alami

Publish Date: 2016/12/30

Volume: 13, Issue: 12, Pages: 2135-2146

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Abstract

Successful modeling of hydroenvironmental processes widely relies on quantity and quality of accessible data and noisy data can affect the modeling performance On the other hand in training phase of any Artificial Intelligence AI based model each training data set is usually a limited sample of possible patterns of the process and hence might not show the behavior of whole population Accordingly in the present paper waveletbased denoising method was used to smooth hydrological time series Thereafter small normally distributed noises with the mean of zero and various standard deviations were generated and added to the smooth time series to form different denoisedjittered data sets Finally the obtained preprocessed data were imposed into Artificial Neural Network ANN and Adaptive NeuroFuzzy Inference System ANFIS models for daily runoffsediment modeling of the Minnesota River To evaluate the modeling performance the outcomes were compared with results of multi linear regression MLR and Auto Regressive Integrated Moving Average ARIMA models The comparison showed that the proposed data processing approach which serves both denoising and jittering techniques could enhance the performance of ANN and ANFIS based runoffsediment modeling of the case study up to 34 and 25 in the verification phase respectivelyGreat thanks due to attention of editor and reviewers which with valuable comments added to the quality of the present research The research was financially supported by a grant from Research Affairs of Najafabad Branch Islamic Azad University Iran

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