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 standsbehind the reasons for increasing the mortality rates of fish and aquatic organisms. +e accurate and more reliable prediction ofTDG 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 beenapplied to predict the saturated TDG% at USGS 14150000 and USGS 14181500 stations which are located in the USA. For theUSGS 14150000 station, the recorded samples from 13 October 2016 to 14 March 2019 (75%) were used for training set, and therest from 15 March 2019 to 13 October 2019 (25%) were used for testing requirements. Similarly, for USGS 14181500 station, thehourly data samples which covered the period from 9 June 2017 till 11 March 2019 were used for calibrating the models and from12 March 2019 until 9 October 2019 were used for testing the predictive models. Eight input combinations based on differentparameters have been established as well as nine statistical performance measures have been used for evaluating the accuracy ofadopted models, for instance, not limited, correlation of determination (R2), mean absolute relative error (MAE), and uncertaintyat 95% (U95). +e obtained results of the study for both stations revealed that the ELM managed efficiently to estimate the TDG incomparison to SVR technique. For USGS 14181500 station, the statistical measures for ELM (SVR) were, respectively, reported asR2 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 forELM (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’straining process computational time is stated to be much shorter than that of SVM. +e results also showed that the temperatureparameter 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 tothe basic knowledge of environmental considerations.Validerad;2021;Nivå 2;2021-04-19 (alebob);Finansiär: AlMaarif University College