PASIG RIVER WATER QUALITY ESTIMATION USING AN EMPIRICAL ORDINARY LEAST SQUARES REGRESSION MODEL OF SENTINEL-2 SATELLITE IMAGES
- 1Training Center for Applied Geodesy and Photogrammetry, University of the Philippines, Diliman, Quezon City 1101, Philippines
- 2Dept. of Geodetic Engineering, University of the Philippines, Diliman, Quezon City 1101, Philippines
Keywords: Water Pollution, Remote Sensing, DAO-2016-08, Statistical Models
Abstract. This study entails generation of empirical ordinary least squares regression models to estimate water parameters. It uses remote sensing for environmental monitoring of Pasig River located in the Philippines. This uses measurements of primary water quality (WQ) parameters defined on Department of Environment and Natural Resources Administrative Order 2016-08 recorded on the Pasig River Unified Monitoring Stations (PRUMS) report from January to June of 2019. Sentinel-2 images are utilized to estimate biological oxygen demand (BOD), Chloride, Color, Dissolved Oxygen (DO), Fecal Coliform, Nitrate, pH, Phosphate, Temperature, and Total suspended solids (TSS). Feature generation involved calculation of different band reflectances from the satellite image. Exhaustive feature selection through application of a Pearson Correlation threshold was applied to limit number of independent variables. The box-cox transformations of water quality parameters (except for Temperature) were used as dependent variables and the selected features are used as dependent variables for the ordinary least squares regression model. The root mean square error (RMSE) values for the models which are computed using the k-fold cross validation technique showed outliers, especially for the TSS model (>547000 mg/L), which made its average negative RMSE so large. Tests for multicollinearity, autocorrelation, and homoscedasticity indicated problems in models created. However, normality of residuals indicates that models allow us to roughly estimate water quality for the river as a whole with the advantages of remote sensing, enabling a better perspective for its spatial distribution.