Augmented Logarithmic Gaussian Process Regression Methodology for Chlorophyll Prediction

In aquaculture engineering, estimation of chlorophyll concentration is of utmost importance for water quality monitoring. For a particular area, its concentration is a direct manifestation of the region suitability for fish farming. In literature different parametric and non parametric methods have been studied for chlorophyll concentration prediction. In this paper we have pre-processed the remote sensing data by logarithmic transformation which enhances the data correlation and followed by Gaussian Process Regression (GPR) based forecasting. The proposed methodology is validated on Sea-viewing Wide Field-of-View Sensor (SeaWIFS) and the NASA operational Moderate Resolution Imaging Spectro-radiometer onboard AQUA (MODIS-Aqua) data-sets. Experimental result shows the proposed method's efficacy in enhanced accuracy using the projected data.