Modeling total dissolved gas (TDG) concentration at Columbia river basin dams: high-order response surface method (H-RSM) vs. M5Tree, LSSVM, and MARS

Keshtegar, Behrooz; Heddam, Salim; Kişi, Özgür; Zhu, Shun‐Peng

doi:10.1007/s12517-019-4687-3

“…e data used in their study was collected from four United States Geological Survey (SGS) stations at Columbia River. It was reported that the HRSM with five variables demonstrated the best performance with regard to predicting TDG among the other models used in their study, where the HRSM recorded 0.911 coefficient of correlation [34].…”

Section: Introductionmentioning

confidence: 83%

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

AlOmar

¹

,

Hameed

²

,

Al‐Ansari

³

et al. 2020

Advances in Civil Engineering

View full text Add to dashboard Cite

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.

show abstract

“…The fourth-order expansion of Eq. ( 9) is applied as an approximation to the actual function in a relatively small region [32].…”

Section: B Response Surface Methodology (Rsm)mentioning

confidence: 99%

Optimizing Equivalent Circuit Model Parameters of DFB Lasers With RSM Model and NSGA-II Algorithm

Liuge

¹

,

Cheng

²

,

Liu

³

et al. 2022

View full text Add to dashboard Cite

The parameters group of the distributed feedback (DFB) laser equivalent circuit model based on the single-mode rate equations is the key to precisely presenting DFB response characteristics, so a novel optimization solution based on the response surface methodology (RSM) is proposed to rapidly select the optimized parameters by the multi-objective algorithm. The RSM model is designed to match the DFB laser characteristics related to the direct-current and small-signal frequency response, and non-dominated sorting genetic algorithm-II (NSGA-II) attributes to elevating the RSM model optimizing to screen out an optimal set of parameters by Pareto sorting. To further verify the accuracy of the model, the resonant frequency (fr) and the threshold current (Ith) are considered the objective optimization variables to set the target values as 18 GHz and 11.5 mA. The single-objective and multi-objective optimization are analyzed and compared to each other, and the optimized results have shown good agreement with predicted values, such as lower Ith in the multi-objective optimization while close fr in both cases. It has been demonstrated that optimization makes it possible not only to exploit the potential of existing DFB lasers but also to provide guidance for the inverse design of laser.

show abstract

“…The implementation of MT was conducted by Weka3.9 software. To monitor the water quality status of water bodies, the surveying on the related works proved that applying multivariate linear equations provided the best performance for training and testing phases (e.g., [3,[41][42][43]). In this way, we used the following expression to approximate WQI:…”

Section: Model Treementioning

confidence: 99%

Evaluation of River Water Quality Index Using Remote Sensing and Artificial Intelligence Models

Najafzadeh

¹

,

Basirian

²

2023

View full text Add to dashboard Cite

To restrict the entry of polluting components into water bodies, particularly rivers, it is critical to undertake timely monitoring and make rapid choices. Traditional techniques of assessing water quality are typically costly and time-consuming. With the advent of remote sensing technologies and the availability of high-resolution satellite images in recent years, a significant opportunity for water quality monitoring has arisen. In this study, the water quality index (WQI) for the Hudson River has been estimated using Landsat 8 OLI-TIRS images and four Artificial Intelligence (AI) models, such as M5 Model Tree (MT), Multivariate Adaptive Regression Spline (MARS), Gene Expression Programming (GEP), and Evolutionary Polynomial Regression (EPR). In this way, 13 water quality parameters (WQPs) (i.e., Turbidity, Sulfate, Sodium, Potassium, Hardness, Fluoride, Dissolved Oxygen, Chloride, Arsenic, Alkalinity, pH, Nitrate, and Magnesium) were measured between 14 March 2021 and 16 June 2021 at a site near Poughkeepsie, New York. First, Multiple Linear Regression (MLR) models were created between these WQPs parameters and the spectral indices of Landsat 8 OLI-TIRS images, and then, the most correlated spectral indices were selected as input variables of AI models. With reference to the measured values of WQPs, the WQI was determined according to the Canadian Council of Ministers of the Environment (CCME) guidelines. After that, AI models were developed through the training and testing stages, and then estimated values of WQI were compared to the actual values. The results of the AI models’ performance showed that the MARS model had the best performance among the other AI models for monitoring WQI. The results demonstrated the high effectiveness and power of estimating WQI utilizing a combination of satellite images and artificial intelligence models.

show abstract

Modeling total dissolved gas (TDG) concentration at Columbia river basin dams: high-order response surface method (H-RSM) vs. M5Tree, LSSVM, and MARS

Cited by 28 publications

References 42 publications

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

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

Optimizing Equivalent Circuit Model Parameters of DFB Lasers With RSM Model and NSGA-II Algorithm

Evaluation of River Water Quality Index Using Remote Sensing and Artificial Intelligence Models

Contact Info

Product

Resources

About