Assessment of Water Quality in Lake Qaroun Using Ground-Based Remote Sensing Data and Artificial Neural Networks

Monitoring and managing water quality parameters (WQPs) in water bodies (e.g., lakes) on a large scale using sampling-point techniques is tedious, laborious, and not highly representative. Hyperspectral and data-driven technology have provided a potentially valuable tool for the precise measurement...

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Autores principales:	Salah Elsayed, Hekmat Ibrahim, Hend Hussein, Osama Elsherbiny, Adel H. Elmetwalli, Farahat S. Moghanm, Adel M. Ghoneim, Subhan Danish, Rahul Datta, Mohamed Gad
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	artificial neural networks models total nitrogen non-destructive technique water quality lakes Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500
Acceso en línea:	https://doaj.org/article/5585eeb5980b45f088cc3b59e7e6de87
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spelling	oai:doaj.org-article:5585eeb5980b45f088cc3b59e7e6de872021-11-11T19:57:04ZAssessment of Water Quality in Lake Qaroun Using Ground-Based Remote Sensing Data and Artificial Neural Networks10.3390/w132130942073-4441https://doaj.org/article/5585eeb5980b45f088cc3b59e7e6de872021-11-01T00:00:00Zhttps://www.mdpi.com/2073-4441/13/21/3094https://doaj.org/toc/2073-4441Monitoring and managing water quality parameters (WQPs) in water bodies (e.g., lakes) on a large scale using sampling-point techniques is tedious, laborious, and not highly representative. Hyperspectral and data-driven technology have provided a potentially valuable tool for the precise measurement of WQPs. Therefore, the objective of this work was to integrate WQPs, derived spectral reflectance indices (published spectral reflectance indices (PSRIs)), newly two-band spectral reflectance indices (NSRIs-2b) and newly three-band spectral indices (NSRIs-3b), and artificial neural networks (ANNs) for estimating WQPs in Lake Qaroun. Shipboard cruises were conducted to collect surface water samples at 16 different sites throughout Lake Qaroun throughout a two-year study (2018 and 2019). Different WQPs, such as total nitrogen (TN), ammonium (NH<sub>4</sub><sup>+</sup>), orthophosphate (PO<sub>4</sub><sup>3−</sup>), and chemical oxygen demand (COD), were evaluated for aquatic use. The results showed that the highest determination coefficients were recorded with the NSRIs-3b, followed by the NSRIs-2b, and then followed by the PSRIs, which produced lower R<sup>2</sup> with all tested WQPs. The majority of NSRIs-3bs demonstrated strong significant relationships with three WQPs (TN, NH<sub>4</sub><sup>+</sup>, and PO<sub>4</sub><sup>3−</sup>) with (R<sup>2</sup> = 0.70 to 0.77), and a moderate relationship with COD (R<sup>2</sup> = 0.52 to 0.64). The SRIs integrated with ANNs would be an efficient tool for estimating the investigated four WQPs in both calibration and validation datasets with acceptable accuracy. For examples, the five features of the SRIs involved in this model are of great significance for predicting TN. Its outputs showed high R<sup>2</sup> values of 0.92 and 0.84 for calibration and validation, respectively. The ANN-PO<sub>4</sub><sup>3−</sup>VI-17 was the highest accuracy model for predicting PO<sub>4</sub><sup>3−</sup> with R<sup>2</sup> = 0.98 and 0.89 for calibration and validation, respectively. In conclusion, this research study demonstrated that NSRIs-3b, alongside a combined approach of ANNs models and SRIs, would be an effective tool for assessing WQPs of Lake Qaroun.Salah ElsayedHekmat IbrahimHend HusseinOsama ElsherbinyAdel H. ElmetwalliFarahat S. MoghanmAdel M. GhoneimSubhan DanishRahul DattaMohamed GadMDPI AGarticleartificial neural networks modelstotal nitrogennon-destructive techniquewater qualitylakesHydraulic engineeringTC1-978Water supply for domestic and industrial purposesTD201-500ENWater, Vol 13, Iss 3094, p 3094 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial neural networks models total nitrogen non-destructive technique water quality lakes Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500
spellingShingle	artificial neural networks models total nitrogen non-destructive technique water quality lakes Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500 Salah Elsayed Hekmat Ibrahim Hend Hussein Osama Elsherbiny Adel H. Elmetwalli Farahat S. Moghanm Adel M. Ghoneim Subhan Danish Rahul Datta Mohamed Gad Assessment of Water Quality in Lake Qaroun Using Ground-Based Remote Sensing Data and Artificial Neural Networks
description	Monitoring and managing water quality parameters (WQPs) in water bodies (e.g., lakes) on a large scale using sampling-point techniques is tedious, laborious, and not highly representative. Hyperspectral and data-driven technology have provided a potentially valuable tool for the precise measurement of WQPs. Therefore, the objective of this work was to integrate WQPs, derived spectral reflectance indices (published spectral reflectance indices (PSRIs)), newly two-band spectral reflectance indices (NSRIs-2b) and newly three-band spectral indices (NSRIs-3b), and artificial neural networks (ANNs) for estimating WQPs in Lake Qaroun. Shipboard cruises were conducted to collect surface water samples at 16 different sites throughout Lake Qaroun throughout a two-year study (2018 and 2019). Different WQPs, such as total nitrogen (TN), ammonium (NH<sub>4</sub><sup>+</sup>), orthophosphate (PO<sub>4</sub><sup>3−</sup>), and chemical oxygen demand (COD), were evaluated for aquatic use. The results showed that the highest determination coefficients were recorded with the NSRIs-3b, followed by the NSRIs-2b, and then followed by the PSRIs, which produced lower R<sup>2</sup> with all tested WQPs. The majority of NSRIs-3bs demonstrated strong significant relationships with three WQPs (TN, NH<sub>4</sub><sup>+</sup>, and PO<sub>4</sub><sup>3−</sup>) with (R<sup>2</sup> = 0.70 to 0.77), and a moderate relationship with COD (R<sup>2</sup> = 0.52 to 0.64). The SRIs integrated with ANNs would be an efficient tool for estimating the investigated four WQPs in both calibration and validation datasets with acceptable accuracy. For examples, the five features of the SRIs involved in this model are of great significance for predicting TN. Its outputs showed high R<sup>2</sup> values of 0.92 and 0.84 for calibration and validation, respectively. The ANN-PO<sub>4</sub><sup>3−</sup>VI-17 was the highest accuracy model for predicting PO<sub>4</sub><sup>3−</sup> with R<sup>2</sup> = 0.98 and 0.89 for calibration and validation, respectively. In conclusion, this research study demonstrated that NSRIs-3b, alongside a combined approach of ANNs models and SRIs, would be an effective tool for assessing WQPs of Lake Qaroun.
format	article
author	Salah Elsayed Hekmat Ibrahim Hend Hussein Osama Elsherbiny Adel H. Elmetwalli Farahat S. Moghanm Adel M. Ghoneim Subhan Danish Rahul Datta Mohamed Gad
author_facet	Salah Elsayed Hekmat Ibrahim Hend Hussein Osama Elsherbiny Adel H. Elmetwalli Farahat S. Moghanm Adel M. Ghoneim Subhan Danish Rahul Datta Mohamed Gad
author_sort	Salah Elsayed
title	Assessment of Water Quality in Lake Qaroun Using Ground-Based Remote Sensing Data and Artificial Neural Networks
title_short	Assessment of Water Quality in Lake Qaroun Using Ground-Based Remote Sensing Data and Artificial Neural Networks
title_full	Assessment of Water Quality in Lake Qaroun Using Ground-Based Remote Sensing Data and Artificial Neural Networks
title_fullStr	Assessment of Water Quality in Lake Qaroun Using Ground-Based Remote Sensing Data and Artificial Neural Networks
title_full_unstemmed	Assessment of Water Quality in Lake Qaroun Using Ground-Based Remote Sensing Data and Artificial Neural Networks
title_sort	assessment of water quality in lake qaroun using ground-based remote sensing data and artificial neural networks
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/5585eeb5980b45f088cc3b59e7e6de87
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Assessment of Water Quality in Lake Qaroun Using Ground-Based Remote Sensing Data and Artificial Neural Networks

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