Data-Driven Model for the Prediction of Total Dissolved Gas: Robust Artificial Intelligence Approach
Saturated total dissolved gas (TDG) is recently considered as a serious issue in the environmental engineering field since it stands behind the reasons for increasing the mortality rates of fish and aquatic organisms. The accurate and more reliable prediction of TDG has a very significant role in pr...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/6618842 |
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| author | Mohamed Khalid AlOmar Mohammed Majeed Hameed Nadhir Al-Ansari Mohammed Abdulhakim AlSaadi |
| author_facet | Mohamed Khalid AlOmar Mohammed Majeed Hameed Nadhir Al-Ansari Mohammed Abdulhakim AlSaadi |
| author_sort | Mohamed Khalid AlOmar |
| collection | DOAJ |
| description | Saturated total dissolved gas (TDG) is recently considered as a serious issue in the environmental engineering field since it stands behind the reasons for increasing the mortality rates of fish and aquatic organisms. The accurate and more reliable prediction of TDG has a very significant role in preserving the diversity of aquatic organisms and reducing the phenomenon of fish deaths. Herein, two machine learning approaches called support vector regression (SVR) and extreme learning machine (ELM) have been applied to predict the saturated TDG% at USGS 14150000 and USGS 14181500 stations which are located in the USA. For the USGS 14150000 station, the recorded samples from 13 October 2016 to 14 March 2019 (75%) were used for training set, and the rest from 15 March 2019 to 13 October 2019 (25%) were used for testing requirements. Similarly, for USGS 14181500 station, the hourly data samples which covered the period from 9 June 2017 till 11 March 2019 were used for calibrating the models and from 12 March 2019 until 9 October 2019 were used for testing the predictive models. Eight input combinations based on different parameters have been established as well as nine statistical performance measures have been used for evaluating the accuracy of adopted models, for instance, not limited, correlation of determination (R2), mean absolute relative error (MAE), and uncertainty at 95% (U95). The obtained results of the study for both stations revealed that the ELM managed efficiently to estimate the TDG in comparison to SVR technique. For USGS 14181500 station, the statistical measures for ELM (SVR) were, respectively, reported as R2 of 0.986 (0.986), MAE of 0.316 (0.441), and U95 of 3.592 (3.869). Lastly, for USGS 14181500 station, the statistical measures for ELM (SVR) were, respectively, reported as R2 of 0.991 (0.991), MAE of 0.338 (0.396), and U95 of 0.832 (0.837). In addition, ELM’s training process computational time is stated to be much shorter than that of SVM. The results also showed that the temperature parameter was the most significant variable that influenced TDG relative to the other parameters. Overall, the proposed model (ELM) proved to be an appropriate and efficient computer-assisted technology for saturated TDG modeling that will contribute to the basic knowledge of environmental considerations. |
| format | Article |
| id | doaj-art-c297bbc1fdd54be09b8a2b643f897006 |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-c297bbc1fdd54be09b8a2b643f8970062025-02-03T06:46:57ZengWileyAdvances in Civil Engineering1687-80861687-80942020-01-01202010.1155/2020/66188426618842Data-Driven Model for the Prediction of Total Dissolved Gas: Robust Artificial Intelligence ApproachMohamed Khalid AlOmar0Mohammed Majeed Hameed1Nadhir Al-Ansari2Mohammed Abdulhakim AlSaadi3Department of Civil Engineering, Al-Maaref University College, Ramadi, IraqDepartment of Civil Engineering, Al-Maaref University College, Ramadi, IraqCivil Engineering Department, Environmental and Natural Resources Engineering, Lulea University of Technology,, 97187 Lulea, SwedenNational Chair of Materials Science and Metallurgy, University of Nizwa, Nizwa, OmanSaturated total dissolved gas (TDG) is recently considered as a serious issue in the environmental engineering field since it stands behind the reasons for increasing the mortality rates of fish and aquatic organisms. The accurate and more reliable prediction of TDG has a very significant role in preserving the diversity of aquatic organisms and reducing the phenomenon of fish deaths. Herein, two machine learning approaches called support vector regression (SVR) and extreme learning machine (ELM) have been applied to predict the saturated TDG% at USGS 14150000 and USGS 14181500 stations which are located in the USA. For the USGS 14150000 station, the recorded samples from 13 October 2016 to 14 March 2019 (75%) were used for training set, and the rest from 15 March 2019 to 13 October 2019 (25%) were used for testing requirements. Similarly, for USGS 14181500 station, the hourly data samples which covered the period from 9 June 2017 till 11 March 2019 were used for calibrating the models and from 12 March 2019 until 9 October 2019 were used for testing the predictive models. Eight input combinations based on different parameters have been established as well as nine statistical performance measures have been used for evaluating the accuracy of adopted models, for instance, not limited, correlation of determination (R2), mean absolute relative error (MAE), and uncertainty at 95% (U95). The obtained results of the study for both stations revealed that the ELM managed efficiently to estimate the TDG in comparison to SVR technique. For USGS 14181500 station, the statistical measures for ELM (SVR) were, respectively, reported as R2 of 0.986 (0.986), MAE of 0.316 (0.441), and U95 of 3.592 (3.869). Lastly, for USGS 14181500 station, the statistical measures for ELM (SVR) were, respectively, reported as R2 of 0.991 (0.991), MAE of 0.338 (0.396), and U95 of 0.832 (0.837). In addition, ELM’s training process computational time is stated to be much shorter than that of SVM. The results also showed that the temperature parameter was the most significant variable that influenced TDG relative to the other parameters. Overall, the proposed model (ELM) proved to be an appropriate and efficient computer-assisted technology for saturated TDG modeling that will contribute to the basic knowledge of environmental considerations.http://dx.doi.org/10.1155/2020/6618842 |
| spellingShingle | Mohamed Khalid AlOmar Mohammed Majeed Hameed Nadhir Al-Ansari Mohammed Abdulhakim AlSaadi Data-Driven Model for the Prediction of Total Dissolved Gas: Robust Artificial Intelligence Approach Advances in Civil Engineering |
| title | Data-Driven Model for the Prediction of Total Dissolved Gas: Robust Artificial Intelligence Approach |
| title_full | Data-Driven Model for the Prediction of Total Dissolved Gas: Robust Artificial Intelligence Approach |
| title_fullStr | Data-Driven Model for the Prediction of Total Dissolved Gas: Robust Artificial Intelligence Approach |
| title_full_unstemmed | Data-Driven Model for the Prediction of Total Dissolved Gas: Robust Artificial Intelligence Approach |
| title_short | Data-Driven Model for the Prediction of Total Dissolved Gas: Robust Artificial Intelligence Approach |
| title_sort | data driven model for the prediction of total dissolved gas robust artificial intelligence approach |
| url | http://dx.doi.org/10.1155/2020/6618842 |
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