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Statistical remote sensing for prediction of inland water quality parameters for Shatt Al-Arab River in Iraq

In this study, the empirical approach besides the methods multilinear regression was followed to generate the predictive mathematical models to estimate the water quality parameters values in the surface water of the Shatt Al-arab River which is located southern part of Iraq. And to show both benefi...

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Main Authors: Abbas, Malik R., Rasib, Abd. Wahid, Ahmad, Baharin, Abbas, Talib R.
Format: Conference or Workshop Item
Language:English
Published: 2021
Subjects:
G70.39-70.6 Remote sensing
Online Access:http://eprints.utm.my/id/eprint/96680/1/BaharinAhmad2021_StatisticalRemoteSensingforPredictionofInland.pdf
http://eprints.utm.my/id/eprint/96680/
http://dx.doi.org/10.1088/1755-1315/722/1/012014
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spelling my.utm.966802022-08-15T08:50:44Z http://eprints.utm.my/id/eprint/96680/ Statistical remote sensing for prediction of inland water quality parameters for Shatt Al-Arab River in Iraq Abbas, Malik R. Rasib, Abd. Wahid Ahmad, Baharin Abbas, Talib R. G70.39-70.6 Remote sensing In this study, the empirical approach besides the methods multilinear regression was followed to generate the predictive mathematical models to estimate the water quality parameters values in the surface water of the Shatt Al-arab River which is located southern part of Iraq. And to show both benefits and the viability of using Landsat 8 optical spectral images to estimate some of the water quality parameters concentration. The daily water quality data archive for the of the total dissolved solids (TDS), conductivity (E.C), Nitrate (NO3), and potential hydrogen ions (pH) of the water in four seasons (winter, spring, summer, and autumn) distributed along three years (2013, 2014, and 2015), was collected from four ground stations along of the Shatt Al-arab River. The objective of the study was to generate seasonal empirical mathematical models for the (open time) that can be used every year, without the need for calibration every time. Optical data were corrected to remove radiometric and atmospheric error sources effects prior to the developing the models. Multiple regression analysis between measured water quality parameters of the ground stations and the reflectance of the pixels corresponding to the sampling stations was used to generate these models. Determination coefficients (R2) of the proposed mathematical models were between 0.83-0.99. The percentage error between predicted and measured values for these models were between 0.03% -12%. The results of this work indicate the novelty of the approach used to generated these mathematical models for the open time for any year but in the each season. These models are reliable to estimate the spatial and temporal variation of TDS, E.C, NO3, and pH. So models generated from Landsat 8 can be used as a tool to facilitate the environmental, economic, and social management of the surface waters bodies like a Shatt Al-arab River. 2021 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/96680/1/BaharinAhmad2021_StatisticalRemoteSensingforPredictionofInland.pdf Abbas, Malik R. and Rasib, Abd. Wahid and Ahmad, Baharin and Abbas, Talib R. (2021) Statistical remote sensing for prediction of inland water quality parameters for Shatt Al-Arab River in Iraq. In: Article number 0120141st International Virtual Conference of Environmental Sciences, IVCES 2020, 15 - 16 December 2020, Virtual, Online. http://dx.doi.org/10.1088/1755-1315/722/1/012014
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic G70.39-70.6 Remote sensing
spellingShingle G70.39-70.6 Remote sensing
Abbas, Malik R.
Rasib, Abd. Wahid
Ahmad, Baharin
Abbas, Talib R.
Statistical remote sensing for prediction of inland water quality parameters for Shatt Al-Arab River in Iraq
description In this study, the empirical approach besides the methods multilinear regression was followed to generate the predictive mathematical models to estimate the water quality parameters values in the surface water of the Shatt Al-arab River which is located southern part of Iraq. And to show both benefits and the viability of using Landsat 8 optical spectral images to estimate some of the water quality parameters concentration. The daily water quality data archive for the of the total dissolved solids (TDS), conductivity (E.C), Nitrate (NO3), and potential hydrogen ions (pH) of the water in four seasons (winter, spring, summer, and autumn) distributed along three years (2013, 2014, and 2015), was collected from four ground stations along of the Shatt Al-arab River. The objective of the study was to generate seasonal empirical mathematical models for the (open time) that can be used every year, without the need for calibration every time. Optical data were corrected to remove radiometric and atmospheric error sources effects prior to the developing the models. Multiple regression analysis between measured water quality parameters of the ground stations and the reflectance of the pixels corresponding to the sampling stations was used to generate these models. Determination coefficients (R2) of the proposed mathematical models were between 0.83-0.99. The percentage error between predicted and measured values for these models were between 0.03% -12%. The results of this work indicate the novelty of the approach used to generated these mathematical models for the open time for any year but in the each season. These models are reliable to estimate the spatial and temporal variation of TDS, E.C, NO3, and pH. So models generated from Landsat 8 can be used as a tool to facilitate the environmental, economic, and social management of the surface waters bodies like a Shatt Al-arab River.
format Conference or Workshop Item
author Abbas, Malik R.
Rasib, Abd. Wahid
Ahmad, Baharin
Abbas, Talib R.
author_facet Abbas, Malik R.
Rasib, Abd. Wahid
Ahmad, Baharin
Abbas, Talib R.
author_sort Abbas, Malik R.
title Statistical remote sensing for prediction of inland water quality parameters for Shatt Al-Arab River in Iraq
title_short Statistical remote sensing for prediction of inland water quality parameters for Shatt Al-Arab River in Iraq
title_full Statistical remote sensing for prediction of inland water quality parameters for Shatt Al-Arab River in Iraq
title_fullStr Statistical remote sensing for prediction of inland water quality parameters for Shatt Al-Arab River in Iraq
title_full_unstemmed Statistical remote sensing for prediction of inland water quality parameters for Shatt Al-Arab River in Iraq
title_sort statistical remote sensing for prediction of inland water quality parameters for shatt al-arab river in iraq
publishDate 2021
url http://eprints.utm.my/id/eprint/96680/1/BaharinAhmad2021_StatisticalRemoteSensingforPredictionofInland.pdf
http://eprints.utm.my/id/eprint/96680/
http://dx.doi.org/10.1088/1755-1315/722/1/012014
_version_ 1743107013094670336
score 13.211869

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