Estimation of zooplankton density with artificial neural networks (a new statistical approach) method, Elazığ-Türkiye
This study was carried out to predict the zooplankton density in the Cip reservoir (Elazığ) with an artificial neural network, using some water quality parameters. The plankton samples were collected monthly from Cip Reservoir in 2021- 2022, using a standard plankton net from three stations. Water t...
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| Language: | English |
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Sciendo
2023-12-01
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| Series: | Oceanological and Hydrobiological Studies |
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| Online Access: | https://doi.org/10.26881/oahs-2023.4.11 |
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| author | Bulut Hilal |
| author_facet | Bulut Hilal |
| author_sort | Bulut Hilal |
| collection | DOAJ |
| description | This study was carried out to predict the zooplankton density in the Cip reservoir (Elazığ) with an artificial neural network, using some water quality parameters. The plankton samples were collected monthly from Cip Reservoir in 2021- 2022, using a standard plankton net from three stations. Water temperature, dissolved oxygen, pH, electrical conductivity, secchi disk, alkalinity, total nitrogen and total phosphorus were measured. The actual values of zooplankton density and results obtained from the artificial neural networks were compared. Mean absolute percent error (MAPE) values were calculated with actual values and ANNs values. ANNs values were determined to be close to the real data. MAPE percentage value at the first station was determined as 1.143 for Rotifer, 0.118 for Cladocera, and 0.141 for Copepoda. The MAPE percentage value at the second station was determined as 0.941 for Rotifer, 0.377 for Cladocera, and 0.185 for Copepoda. The MAPE percentage value at the third station was determined as 0.342 for Rotifer, 0.557 for Cladocera, and 0.301 for Copepoda. In the present study, it has been seen that artificial neural networks with a learning feature are successful in predicting zooplankton densities in an aquatic environment. It can be concluded from the study that ANNs are a powerful tool for understanding their relationships with the environment |
| format | Article |
| id | doaj-art-32c2e1dd71ec4923b7d8db348292d5eb |
| institution | Kabale University |
| issn | 1897-3191 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Oceanological and Hydrobiological Studies |
| spelling | doaj-art-32c2e1dd71ec4923b7d8db348292d5eb2025-01-20T11:10:24ZengSciendoOceanological and Hydrobiological Studies1897-31912023-12-0152450251510.26881/oahs-2023.4.11Estimation of zooplankton density with artificial neural networks (a new statistical approach) method, Elazığ-TürkiyeBulut Hilal0Faculty of Fisheries, Firat University, 23000Elazığ, TürkiyeThis study was carried out to predict the zooplankton density in the Cip reservoir (Elazığ) with an artificial neural network, using some water quality parameters. The plankton samples were collected monthly from Cip Reservoir in 2021- 2022, using a standard plankton net from three stations. Water temperature, dissolved oxygen, pH, electrical conductivity, secchi disk, alkalinity, total nitrogen and total phosphorus were measured. The actual values of zooplankton density and results obtained from the artificial neural networks were compared. Mean absolute percent error (MAPE) values were calculated with actual values and ANNs values. ANNs values were determined to be close to the real data. MAPE percentage value at the first station was determined as 1.143 for Rotifer, 0.118 for Cladocera, and 0.141 for Copepoda. The MAPE percentage value at the second station was determined as 0.941 for Rotifer, 0.377 for Cladocera, and 0.185 for Copepoda. The MAPE percentage value at the third station was determined as 0.342 for Rotifer, 0.557 for Cladocera, and 0.301 for Copepoda. In the present study, it has been seen that artificial neural networks with a learning feature are successful in predicting zooplankton densities in an aquatic environment. It can be concluded from the study that ANNs are a powerful tool for understanding their relationships with the environmenthttps://doi.org/10.26881/oahs-2023.4.11artificial neural networkzooplankton dynamicsreal time predictivewater qualityturkey |
| spellingShingle | Bulut Hilal Estimation of zooplankton density with artificial neural networks (a new statistical approach) method, Elazığ-Türkiye Oceanological and Hydrobiological Studies artificial neural network zooplankton dynamics real time predictive water quality turkey |
| title | Estimation of zooplankton density with artificial neural networks (a new statistical approach) method, Elazığ-Türkiye |
| title_full | Estimation of zooplankton density with artificial neural networks (a new statistical approach) method, Elazığ-Türkiye |
| title_fullStr | Estimation of zooplankton density with artificial neural networks (a new statistical approach) method, Elazığ-Türkiye |
| title_full_unstemmed | Estimation of zooplankton density with artificial neural networks (a new statistical approach) method, Elazığ-Türkiye |
| title_short | Estimation of zooplankton density with artificial neural networks (a new statistical approach) method, Elazığ-Türkiye |
| title_sort | estimation of zooplankton density with artificial neural networks a new statistical approach method elazig turkiye |
| topic | artificial neural network zooplankton dynamics real time predictive water quality turkey |
| url | https://doi.org/10.26881/oahs-2023.4.11 |
| work_keys_str_mv | AT buluthilal estimationofzooplanktondensitywithartificialneuralnetworksanewstatisticalapproachmethodelazıgturkiye |