PASIG RIVER WATER QUALITY ESTIMATION USING AN EMPIRICAL ORDINARY LEAST SQUARES REGRESSION MODEL OF SENTINEL-2 SATELLITE IMAGES

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 En...

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Autores principales:	J. E. Escoto, A. C. Blanco, R. J. Argamosa, J. M. Medina
Formato:	article
Lenguaje:	EN
Publicado:	Copernicus Publications 2021
Materias:	Technology T Engineering (General). Civil engineering (General) TA1-2040 Applied optics. Photonics TA1501-1820
Acceso en línea:	https://doaj.org/article/b36a966e9b9544fa923cc8e9efd89d84
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spelling	oai:doaj.org-article:b36a966e9b9544fa923cc8e9efd89d842021-11-19T01:32:23ZPASIG RIVER WATER QUALITY ESTIMATION USING AN EMPIRICAL ORDINARY LEAST SQUARES REGRESSION MODEL OF SENTINEL-2 SATELLITE IMAGES10.5194/isprs-archives-XLVI-4-W6-2021-161-20211682-17502194-9034https://doaj.org/article/b36a966e9b9544fa923cc8e9efd89d842021-11-01T00:00:00Zhttps://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLVI-4-W6-2021/161/2021/isprs-archives-XLVI-4-W6-2021-161-2021.pdfhttps://doaj.org/toc/1682-1750https://doaj.org/toc/2194-9034This 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.J. E. EscotoA. C. BlancoA. C. BlancoR. J. ArgamosaJ. M. MedinaCopernicus PublicationsarticleTechnologyTEngineering (General). Civil engineering (General)TA1-2040Applied optics. PhotonicsTA1501-1820ENThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLVI-4-W6-2021, Pp 161-168 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Technology T Engineering (General). Civil engineering (General) TA1-2040 Applied optics. Photonics TA1501-1820
spellingShingle	Technology T Engineering (General). Civil engineering (General) TA1-2040 Applied optics. Photonics TA1501-1820 J. E. Escoto A. C. Blanco A. C. Blanco R. J. Argamosa J. M. Medina PASIG RIVER WATER QUALITY ESTIMATION USING AN EMPIRICAL ORDINARY LEAST SQUARES REGRESSION MODEL OF SENTINEL-2 SATELLITE IMAGES
description	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.
format	article
author	J. E. Escoto A. C. Blanco A. C. Blanco R. J. Argamosa J. M. Medina
author_facet	J. E. Escoto A. C. Blanco A. C. Blanco R. J. Argamosa J. M. Medina
author_sort	J. E. Escoto
title	PASIG RIVER WATER QUALITY ESTIMATION USING AN EMPIRICAL ORDINARY LEAST SQUARES REGRESSION MODEL OF SENTINEL-2 SATELLITE IMAGES
title_short	PASIG RIVER WATER QUALITY ESTIMATION USING AN EMPIRICAL ORDINARY LEAST SQUARES REGRESSION MODEL OF SENTINEL-2 SATELLITE IMAGES
title_full	PASIG RIVER WATER QUALITY ESTIMATION USING AN EMPIRICAL ORDINARY LEAST SQUARES REGRESSION MODEL OF SENTINEL-2 SATELLITE IMAGES
title_fullStr	PASIG RIVER WATER QUALITY ESTIMATION USING AN EMPIRICAL ORDINARY LEAST SQUARES REGRESSION MODEL OF SENTINEL-2 SATELLITE IMAGES
title_full_unstemmed	PASIG RIVER WATER QUALITY ESTIMATION USING AN EMPIRICAL ORDINARY LEAST SQUARES REGRESSION MODEL OF SENTINEL-2 SATELLITE IMAGES
title_sort	pasig river water quality estimation using an empirical ordinary least squares regression model of sentinel-2 satellite images
publisher	Copernicus Publications
publishDate	2021
url	https://doaj.org/article/b36a966e9b9544fa923cc8e9efd89d84
work_keys_str_mv	AT jeescoto pasigriverwaterqualityestimationusinganempiricalordinaryleastsquaresregressionmodelofsentinel2satelliteimages AT acblanco pasigriverwaterqualityestimationusinganempiricalordinaryleastsquaresregressionmodelofsentinel2satelliteimages AT acblanco pasigriverwaterqualityestimationusinganempiricalordinaryleastsquaresregressionmodelofsentinel2satelliteimages AT rjargamosa pasigriverwaterqualityestimationusinganempiricalordinaryleastsquaresregressionmodelofsentinel2satelliteimages AT jmmedina pasigriverwaterqualityestimationusinganempiricalordinaryleastsquaresregressionmodelofsentinel2satelliteimages
_version_	1718420629104885760

PASIG RIVER WATER QUALITY ESTIMATION USING AN EMPIRICAL ORDINARY LEAST SQUARES REGRESSION MODEL OF SENTINEL-2 SATELLITE IMAGES

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