Comparing the Performance of Deep Learning and Machine Learning Methods in Predicting Dissolved Oxygen Content | ||
| تحقیقات آب و خاک ایران | ||
| Volume 53, Issue 8, October 2023, Pages 1885-1900 PDF (2.3 M) | ||
| Document Type: Research Paper | ||
| DOI: 10.22059/ijswr.2022.344088.669288 | ||
| Authors | ||
| kiyoumars roushangar* 1; Sina Davoudi2 | ||
| 1Professor, Department of Water Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran | ||
| 2M.Sc. of Water and Hydraulic Structures, Department of Water Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran | ||
| Abstract | ||
| Water quality forecasting plays an important role in environmental monitoring, ecosystem sustainability and aquaculture. Traditional forecasting methods cannot show the non-linearity and instability of water quality well. In the present study, the water quality parameter of dissolved oxygen was modeled using intelligent Support Vector Machine (SVM), Gaussian Process Regression (GPR) and Long Short-Term Memory (LSTM) methods on three consecutive stations on Savanah River located in USA. For this purpose, six different flow hydraulic and hydrological parameters including water temperature, turbidity, discharge, mean water velocity, pH and specific conductivity were used daily for seven years (2021-2015) as input parameters to model dissolved oxygen. The results showed the complete superiority of the deep learning method over the machine learning methods. According to the results, the long short-term memory method for the last model, which included all parameters, in the third station with correlation coefficient, coefficient of determination and root mean square error, respectively R = 0.981, DC = 0.956 and RMSE = 0.034 for test data performed better. Finally, by performing sensitivity analysis, by removing the water temperature parameter, it was found that DC evaluation criteria decreased by 14% and RMSE increased by 100%. Therefore, water temperature was introduced as the most influential parameter in predicting dissolved oxygen in water. | ||
| Keywords | ||
| Dissolved Oxygen parameter; Long Short-Term Memory; Water Quality; Support Vector Machine; Gaussian Process Regression | ||
| References | ||
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