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Article

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

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Agricultural Engineering, Evaluation of Natural Resources Department, Environmental Studies and Research Institute, University of Sadat City, Minufiya 32897, Egypt
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Geology Department, Faculty of Science, Menoufia University, Shiben El Kom, Minufiya 51123, Egypt
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Geology Department, Faculty of Science, Damanhour University, Damanhour 22511, Egypt
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Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura 35516, Egypt
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Department of Agricultural Engineering, Faculty of Agriculture, Tanta University, Tanta 31527, Egypt
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Soil and Water Department, Faculty of Agriculture, Kafrelsheikh University, Kafr El-Sheikh 33516, Egypt
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Agricultural Research Center, Field Crops Research Institute, Giza 12112, Egypt
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Department of Soil Science, Faculty of Agricultural Sciences and Technology, Bahauddin Zakariya University, Multan 60800, Pakistan
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Department of Geology and Pedology, Faculty of Forestry and Wood Technology, Mendel University in Brno, Zemedelska1, 61300 Brno, Czech Republic
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Hydrogeology, Evaluation of Natural Resources Department, Environmental Studies and Research Institute, University of Sadat City, Minufiya 32897, Egypt
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Authors to whom correspondence should be addressed.
Academic Editor: Kun Shi
Water 2021, 13(21), 3094; https://doi.org/10.3390/w13213094
Received: 9 October 2021 / Revised: 29 October 2021 / Accepted: 1 November 2021 / Published: 3 November 2021
(This article belongs to the Section Water Quality and Contamination)
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 (NH4+), orthophosphate (PO43−), 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 R2 with all tested WQPs. The majority of NSRIs-3bs demonstrated strong significant relationships with three WQPs (TN, NH4+, and PO43−) with (R2 = 0.70 to 0.77), and a moderate relationship with COD (R2 = 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 R2 values of 0.92 and 0.84 for calibration and validation, respectively. The ANN-PO43−VI-17 was the highest accuracy model for predicting PO43− with R2 = 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. View Full-Text
Keywords: artificial neural networks models; total nitrogen; non-destructive technique; water quality; lakes artificial neural networks models; total nitrogen; non-destructive technique; water quality; lakes
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MDPI and ACS Style

Elsayed, S.; Ibrahim, H.; Hussein, H.; Elsherbiny, O.; Elmetwalli, A.H.; Moghanm, F.S.; Ghoneim, A.M.; Danish, S.; Datta, R.; Gad, M. Assessment of Water Quality in Lake Qaroun Using Ground-Based Remote Sensing Data and Artificial Neural Networks. Water 2021, 13, 3094. https://doi.org/10.3390/w13213094

AMA Style

Elsayed S, Ibrahim H, Hussein H, Elsherbiny O, Elmetwalli AH, Moghanm FS, Ghoneim AM, Danish S, Datta R, Gad M. Assessment of Water Quality in Lake Qaroun Using Ground-Based Remote Sensing Data and Artificial Neural Networks. Water. 2021; 13(21):3094. https://doi.org/10.3390/w13213094

Chicago/Turabian Style

Elsayed, Salah, Hekmat Ibrahim, Hend Hussein, Osama Elsherbiny, Adel H. Elmetwalli, Farahat S. Moghanm, Adel M. Ghoneim, Subhan Danish, Rahul Datta, and Mohamed Gad. 2021. "Assessment of Water Quality in Lake Qaroun Using Ground-Based Remote Sensing Data and Artificial Neural Networks" Water 13, no. 21: 3094. https://doi.org/10.3390/w13213094

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