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...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-11-01
|
| Series: | Water |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4441/13/21/3094 |
| _version_ | 1797511635762413568 |
|---|---|
| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T05:48:59Z |
| format | Article |
| id | doaj.art-5585eeb5980b45f088cc3b59e7e6de87 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T05:48:59Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-5585eeb5980b45f088cc3b59e7e6de872023-11-22T21:55:13ZengMDPI AGWater2073-44412021-11-011321309410.3390/w13213094Assessment of Water Quality in Lake Qaroun Using Ground-Based Remote Sensing Data and Artificial Neural NetworksSalah Elsayed0Hekmat Ibrahim1Hend Hussein2Osama Elsherbiny3Adel H. Elmetwalli4Farahat S. Moghanm5Adel M. Ghoneim6Subhan Danish7Rahul Datta8Mohamed Gad9Agricultural Engineering, Evaluation of Natural Resources Department, Environmental Studies and Research Institute, University of Sadat City, Minufiya 32897, EgyptGeology Department, Faculty of Science, Menoufia University, Shiben El Kom, Minufiya 51123, EgyptGeology Department, Faculty of Science, Damanhour University, Damanhour 22511, EgyptAgricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura 35516, EgyptDepartment of Agricultural Engineering, Faculty of Agriculture, Tanta University, Tanta 31527, EgyptSoil and Water Department, Faculty of Agriculture, Kafrelsheikh University, Kafr El-Sheikh 33516, EgyptAgricultural Research Center, Field Crops Research Institute, Giza 12112, EgyptDepartment of Soil Science, Faculty of Agricultural Sciences and Technology, Bahauddin Zakariya University, Multan 60800, PakistanDepartment of Geology and Pedology, Faculty of Forestry and Wood Technology, Mendel University in Brno, Zemedelska1, 61300 Brno, Czech RepublicHydrogeology, Evaluation of Natural Resources Department, Environmental Studies and Research Institute, University of Sadat City, Minufiya 32897, EgyptMonitoring 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.https://www.mdpi.com/2073-4441/13/21/3094artificial neural networks modelstotal nitrogennon-destructive techniquewater qualitylakes |
| spellingShingle | 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 Water artificial neural networks models total nitrogen non-destructive technique water quality lakes |
| title | 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_short | 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 |
| topic | artificial neural networks models total nitrogen non-destructive technique water quality lakes |
| url | https://www.mdpi.com/2073-4441/13/21/3094 |
| work_keys_str_mv | AT salahelsayed assessmentofwaterqualityinlakeqarounusinggroundbasedremotesensingdataandartificialneuralnetworks AT hekmatibrahim assessmentofwaterqualityinlakeqarounusinggroundbasedremotesensingdataandartificialneuralnetworks AT hendhussein assessmentofwaterqualityinlakeqarounusinggroundbasedremotesensingdataandartificialneuralnetworks AT osamaelsherbiny assessmentofwaterqualityinlakeqarounusinggroundbasedremotesensingdataandartificialneuralnetworks AT adelhelmetwalli assessmentofwaterqualityinlakeqarounusinggroundbasedremotesensingdataandartificialneuralnetworks AT farahatsmoghanm assessmentofwaterqualityinlakeqarounusinggroundbasedremotesensingdataandartificialneuralnetworks AT adelmghoneim assessmentofwaterqualityinlakeqarounusinggroundbasedremotesensingdataandartificialneuralnetworks AT subhandanish assessmentofwaterqualityinlakeqarounusinggroundbasedremotesensingdataandartificialneuralnetworks AT rahuldatta assessmentofwaterqualityinlakeqarounusinggroundbasedremotesensingdataandartificialneuralnetworks AT mohamedgad assessmentofwaterqualityinlakeqarounusinggroundbasedremotesensingdataandartificialneuralnetworks |