Analysis of bathymetry data for shape prediction of a reservoir

Abstract

The collection of bathymetry data remotely and its analysis is a challenging task due to the complexity of the data acquisition devices like echo sounders and the large amount of data. The data obtained is also affected by noise and needs to be processed to predict the bottom surface of the water body. This paper presents a novel approach for analysis of bathymetry data of a reservoir. The proposed system involves multipath noise removal and interpolation of the data to find the volume of water in the reservoir. The Wavelet packet decomposition technique is used for removing noise in the data. Further, nearest neighborhood and TIN interpolation techniques are used to obtain a 3D model of the reservoir. This approach is used to calculate the volume of water in the reservoir.