Data-Driven Model for the Prediction of Total Dissolved Gas: Robust Artificial Intelligence Approach

Abstract

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 ( R 2 ), mean absolute relative error (MAE), and uncertainty at 95% ( U 95 ). 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 R 2 of 0.986 (0.986), MAE of 0.316 (0.441), and U 95 of 3.592 (3.869). Lastly, for USGS 14181500 station, the statistical measures for ELM (SVR) were, respectively, reported as R 2 of 0.991 (0.991), MAE of 0.338 (0.396), and U 95 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.

An Extra Tree Regression Model for Discharge Coefficient Prediction: Novel, Practical Applications in the Hydraulic Sector and Future Research Directions

“…e dataset used in this study involves geometrical parameters of the rectangular sharp-crested side weirs and other parameters related to the discharge characteristics. ere were 84 samples collected from different studies in the literature [28,29]. Emiroglu et al [30] performed their experiments in the hydraulic laboratory using a rectangular channel with a length, depth, width, and gradient of 12 m, 0.5 m, 0.5 m, and 0.01, respectively.…”

“…While AI modeling approaches were adopted, a range of limitations such as the tuning of hyperparameters for the model, case studies stochasticity, and model stability have been observed [24]. However, machine learning models such as extreme learning machine (ELM), random forest (RF), and extra tree regression (ETR) provide a way to overcome these limitations and have recently become increasingly popular [25][26][27][28][29]. Machine learning models essentially build on data extraction and pattern recognition between data, through the development of algorithms using a dataset subset known as training data and the prediction accuracy verification using the separate dataset subset known as the testing set.…”

“…is phase is very important in assessing the qualification of predicted models, because, in the testing phase, the models deal with a new dataset that was excluded during the training phase. Besides, in the testing phase, the issues related to the predictive models such as generalization (overfitting) and the ability to address outlier values can be disclosed more clearly than in the training phase [29,[51][52][53].…”

Intangible Cultural Heritage Management Using Machine Learning Model: A Case Study of Northwest Folk Song Huaer

“…Artificial intelligence is the greatest scientific and technological innovation in the 21st century [1][2][3][4][5]. As the core field of artificial intelligence, the goal of machine learning is to let computers learn by themselves.…”

Application of Artificial Intelligence Models for Evapotranspiration Prediction along the Southern Coast of Turkey

“…Recently, due to its higher efficiency and much quicker calculation speed, a newly proposed machine learning technology called the extreme learning machine (ELM) has confirmed it to be a promising ET o estimation tool [69]. First, Abdullah et al (2015) used ELM to forecast ET o at three Iraqi stations and concluded that the ELM model is highly efficient and computerized at high generalization speeds [70,71]. Ever since, the ELM for ET o predictions has been used by many studies in different climate environments [72][73][74].…”