Modeling dissolved oxygen concentration using machine learning techniques with dimensionality reduction approach

Garabaghi F. H., Benzer S., Benzer R.

Environmental Monitoring and Assessment, cilt.195, sa.7, 2023 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 195 Sayı: 7
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1007/s10661-023-11492-3
  • Dergi Adı: Environmental Monitoring and Assessment
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, CAB Abstracts, Compendex, EMBASE, Environment Index, Food Science & Technology Abstracts, Geobase, Greenfile, MEDLINE, Pollution Abstracts, Public Affairs Index, Veterinary Science Database, Civil Engineering Abstracts
  • Anahtar Kelimeler: Dissolved oxygen, Feature selection, Multilayer perceptron, Prediction, Random Forest, Regression
  • Gazi Üniversitesi Adresli: Evet

Özet

Oxygen is crucial to keep the life cycle balance in any aspect. Aquatic life is highly influenced by the levels of dissolved oxygen (DO). This calls for not just constant monitoring of the DO in aquatic systems, but to generate an accurate prediction model for future levels of the DO. This study aims to propose an accurate prediction model for DO concentrations. The performance of the Random Forest (RF) and multilayer perceptron (MLP) algorithms was evaluated in generating the regression models. Moreover, the effect of dimensionality reduction of the data by the wrapper feature Selection method on the performance of the models was evaluated. The results showed that the RF regressor excelled MLP in performance with both the dataset of all variables and the dataset of reduced variables with the best performance achieved by the RF regressor by considering Pearson correlation coefficient (0.8052), Mean absolute error (0.8911), and root mean square error (1.2805) when trained by the dataset of reduced variables. As for the accuracy of the models, the estimation error deviation of both models declined significantly when trained by the reduced variables. When the accuracy of the prediction was increased by 0.95% by the RF regressor, the accuracy of the MLP was incremented by 5.7% when trained by the dataset of reduced variables. The results demonstrated the positive impact of the dimensionality reduction on the accuracy of both models. However, RF can be considered a robust regressor in predicting DO concentrations.