Application of artificial intelligence to estimate phycocyanin pigment concentration using water quality data: a comparative study
Abstract In the present investigation, the usefulness and capabilities of four artificial intelligence (AI) models, namely feedforward neural networks (FFNNs), gene expression programming (GEP), adaptive neuro-fuzzy inference system with grid partition (ANFIS-GP) and adaptive neuro-fuzzy inference s...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2019-09-01
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| Series: | Applied Water Science |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1007/s13201-019-1044-3 |
| _version_ | 1818506541825785856 |
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| author | Salim Heddam Hadi Sanikhani Ozgur Kisi |
| author_facet | Salim Heddam Hadi Sanikhani Ozgur Kisi |
| author_sort | Salim Heddam |
| collection | DOAJ |
| description | Abstract In the present investigation, the usefulness and capabilities of four artificial intelligence (AI) models, namely feedforward neural networks (FFNNs), gene expression programming (GEP), adaptive neuro-fuzzy inference system with grid partition (ANFIS-GP) and adaptive neuro-fuzzy inference system with subtractive clustering (ANFIS-SC), were investigated in an attempt to evaluate their predictive ability of the phycocyanin pigment concentration (PC) using data from two stations operated by the United States Geological Survey (USGS). Four water quality parameters, namely temperature, pH, specific conductance and dissolved oxygen, were utilized for PC concentration estimation. The four models were evaluated using root mean square errors (RMSEs), mean absolute errors (MAEs) and correlation coefficient (R). The results showed that the ANFIS-SC provided more accurate predictions in comparison with ANFIS-GP, GEP and FFNN for both stations. For USGS 06892350 station, the R, RMSE and MAE values in the test phase for ANFIS-SC were 0.955, 0.205 μg/L and 0.148 μg/L, respectively. Similarly, for USGS 14211720 station, the R, RMSE and MAE values in the test phase for ANFIS-SC, respectively, were 0.950, 0.050 μg/L and 0.031 μg/L. Also, using several combinations of the input variables, the results showed that the ANFIS-SC having only temperature and pH as inputs provided good accuracy, with R, RMSE and MAE values in the test phase, respectively, equal to 0.917, 0.275 μg/L and 0.200 μg/L for USGS 06892350 station. This study proved that artificial intelligence models are good and powerful tools for predicting PC concentration using only water quality variables as predictors. |
| first_indexed | 2024-12-10T22:05:58Z |
| format | Article |
| id | doaj.art-87bc05e06c254c768569d89b8c714701 |
| institution | Directory Open Access Journal |
| issn | 2190-5487 2190-5495 |
| language | English |
| last_indexed | 2024-12-10T22:05:58Z |
| publishDate | 2019-09-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Applied Water Science |
| spelling | doaj.art-87bc05e06c254c768569d89b8c7147012022-12-22T01:31:44ZengSpringerOpenApplied Water Science2190-54872190-54952019-09-019711610.1007/s13201-019-1044-3Application of artificial intelligence to estimate phycocyanin pigment concentration using water quality data: a comparative studySalim Heddam0Hadi Sanikhani1Ozgur Kisi2Laboratory of Research in Biodiversity Interaction Ecosystem and Biotechnology, Hydraulics Division, Agronomy Department, Faculty of Science, University 20 Août 1955Water Sciences and Engineering Department, Agriculture Faculty, University of KurdistanSchool of Technology, Ilia State UniversityAbstract In the present investigation, the usefulness and capabilities of four artificial intelligence (AI) models, namely feedforward neural networks (FFNNs), gene expression programming (GEP), adaptive neuro-fuzzy inference system with grid partition (ANFIS-GP) and adaptive neuro-fuzzy inference system with subtractive clustering (ANFIS-SC), were investigated in an attempt to evaluate their predictive ability of the phycocyanin pigment concentration (PC) using data from two stations operated by the United States Geological Survey (USGS). Four water quality parameters, namely temperature, pH, specific conductance and dissolved oxygen, were utilized for PC concentration estimation. The four models were evaluated using root mean square errors (RMSEs), mean absolute errors (MAEs) and correlation coefficient (R). The results showed that the ANFIS-SC provided more accurate predictions in comparison with ANFIS-GP, GEP and FFNN for both stations. For USGS 06892350 station, the R, RMSE and MAE values in the test phase for ANFIS-SC were 0.955, 0.205 μg/L and 0.148 μg/L, respectively. Similarly, for USGS 14211720 station, the R, RMSE and MAE values in the test phase for ANFIS-SC, respectively, were 0.950, 0.050 μg/L and 0.031 μg/L. Also, using several combinations of the input variables, the results showed that the ANFIS-SC having only temperature and pH as inputs provided good accuracy, with R, RMSE and MAE values in the test phase, respectively, equal to 0.917, 0.275 μg/L and 0.200 μg/L for USGS 06892350 station. This study proved that artificial intelligence models are good and powerful tools for predicting PC concentration using only water quality variables as predictors.http://link.springer.com/article/10.1007/s13201-019-1044-3ModelingPhycocyanin concentrationFeedforward neural networksGene expression programmingAdaptive neuro-fuzzy inference systemGrid partition |
| spellingShingle | Salim Heddam Hadi Sanikhani Ozgur Kisi Application of artificial intelligence to estimate phycocyanin pigment concentration using water quality data: a comparative study Applied Water Science Modeling Phycocyanin concentration Feedforward neural networks Gene expression programming Adaptive neuro-fuzzy inference system Grid partition |
| title | Application of artificial intelligence to estimate phycocyanin pigment concentration using water quality data: a comparative study |
| title_full | Application of artificial intelligence to estimate phycocyanin pigment concentration using water quality data: a comparative study |
| title_fullStr | Application of artificial intelligence to estimate phycocyanin pigment concentration using water quality data: a comparative study |
| title_full_unstemmed | Application of artificial intelligence to estimate phycocyanin pigment concentration using water quality data: a comparative study |
| title_short | Application of artificial intelligence to estimate phycocyanin pigment concentration using water quality data: a comparative study |
| title_sort | application of artificial intelligence to estimate phycocyanin pigment concentration using water quality data a comparative study |
| topic | Modeling Phycocyanin concentration Feedforward neural networks Gene expression programming Adaptive neuro-fuzzy inference system Grid partition |
| url | http://link.springer.com/article/10.1007/s13201-019-1044-3 |
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