Prediction of Water Quality Parameters of Tigris River in Baghdad City by Using Artificial Intelligence Methods

• Autorzy: Jaafer Noora Sadeq , Al-Mukhtar Mustafa

Identyfikatory

DOI

10.12912/27197050/189572

• Języki publikacji: EN

Abstrakty

EN

The purpose of this research is to assess the efficacy of five distinct artificial intelligence model techniques: AdaBoost, Gradient Boosting, Tree, Random Forest, and KNN, to estimate the water quality parameters of dissolved oxygen (DO) and biochemical oxygen demand (BOD). The performance of each model was assessed using two datasets: AlMuthanna Bridge and Al-Aammah Bridge on the Tigris River in Baghdad City. The data was randomly divided into two categories: 70% for training and 30% for testing. Principal component analysis (PCA) was used to identify the most effective input parameters for modeling DO and BOD. The four performance criteria – coefficient of determination (R² ), root mean square error (RMSE), mean absolute error (MAE), and mean square error (MSE) – were applied in order to evaluate the models’ effectiveness. It was demonstrated that the AdaBoost and Gradient Boosting models were superior for predicting DO and BOD. For DO prediction, the coefficient of determination R² of Gradient Boosting (AdaBoost) at Al-Muthanna Bridge and Al-Aammah Bridge were 0.994 (0.992) and 0.994 (0.991), respectively. For BOD prediction, the correlation coefficients R² of Gradient Boosting (AdaBoost) were 0.992 (0.982) and 0.989 (0.990), respectively. This study has shown that sophisticated machine learning techniques, such as gradient boosting and AdaBoost, are suitable for predicting water quality indices. They could also be helpful for predicting and managing the water quality parameters of different water supply systems in the future in water-related communities where artificial intelligence technology is still being thoroughly investigated.

Słowa kluczowe

EN

artificial intelligence; biochemical oxygen demand; dissolved oxygen; machine learning models; water quality parameters

• Wydawca: Lubelski Oddział Polskiego Towarzystwo Inżynierii Ekologicznej ; Lublin University of Technology ; WNGB Wydawnictwo Naukowe
• Czasopismo: Ecological Engineering & Environmental Technology
• Rocznik: 2024
• Tom: Vol. 25, iss. 9
• Strony: 13--25
• Opis fizyczny: Bibliogr. 48 poz., rys., tab.

Twórcy

autor

Jaafer Noora Sadeq

• Civil Engineering Department. University of Technology, Baghdad, Iraq

• https://orcid.org/0009-0003-9510-4100

autor

Al-Mukhtar Mustafa

• Civil Engineering Department. University of Technology, Baghdad, Iraq

• https://orcid.org/0000-0002-8850-0899

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