Wavelet-based multiresolution analysis for data cleaning and its application to water quality management systems

Data cleaning techniques are useful for extracting desirable knowledge or interesting patterns from existing databases in engineering applications. The major problems of conventional techniques (e.g., Fourier Transformation Technique) are that they are (1) more appropriate in linear systems than nonlinear systems, and (2) stringently depend on state space functions. In this study a wavelet-based multiresolution analysis technique (WMAT) is proposed for reducing noises induced by complex uncertainty. The approach is applied to a river water quality simulation system for showing its practicability in data cleaning and parameter estimation. Clean data are prepared through running a Thomas’ river water quality model and polluted data are synthesized by mixing clean data with white Gaussian noises. The results show that WMAT will not distort the clean data, and can effectively reduce the noise in the polluted data. The data denoised by WMAT are furthermore used for estimating the modeling parameters. It is also indicated that the parameters estimated with the denoised data through WMAT are much closer to real values than those (1) with polluted data through WMAT and (2) with data through Fourier analysis technique. It is thus recommended that the prepared data be used for estimating the modeling parameters until being cleaned with WMAT.