Wavelet denoising and cubic spline interpolation for observation data in groundwater pollution source identification problems

As the identified results of groundwater pollution source identification (GPSI) can influence the cost for the polluter in paying for remediating groundwater resources, it is important that the accuracy of the estimated result should be as high as possible. However, many factors can influence the result, such as noisy concentration data and incomplete concentration data. Thus, this paper is aimed at studying the difference between using the observation data before and after denoising and interpolating for solving GPSI problems. Four kinds of noise level and 20 groups of missing data were designed to test the performance of wavelet denoising and cubic spline interpolation, respectively. The results show that the denoising process can improve the estimated result for the GPSI problem, and the higher the noise level, the stronger this effect. In terms of interpolation, more accurate results can be made after interpolating if the missing data belong to the period after the source releases the pollutant. If the missing data are from when the pollution source is active, interpolation cannot help increase the estimated performance.