Prediction of wastewater treatment plant performance through machine learning techniques

• Published in: Desalination and water treatment Vol. 319; p. 100524
• Main Authors: Mahanna, Hani, El-Rashidy, Nora, Kaloop, Mosbeh R., El-Sapakh, Shaker, Alluqmani, Ayed, Hassan, Raouf
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.07.2024
• Subjects: BOD; Gradient Boosting; Machine Learning; Random Forest; Support Vector Regression; Wastewater; Gradient Boosting; Wastewater; Machine Learning; BOD; Support Vector Regression; Random Forest
• ISSN: 1944-3986
• DOI: 10.1016/j.dwt.2024.100524

This study investigates the use of developed machine learning techniques for modeling the performance of AlHayer, Saudi Arabia, wastewater treatment plant (ALWTP). Three physio-chemical characteristics were measured and predicted, including chemical oxygen demand (COD), biological oxygen demand (BOD), and suspended solids (SS), at ALWTP. The pre-evaluation of collected data revealed the effective capabilities of ALWTP for the removal of suspended solids, organic, and nutrient pollutants. To estimate the physio-chemical characteristics of ALWATP, four developed machine learning techniques were evaluated and compared. Logistic regression (LR), random forest (RF), gradient boosting (GB), and support vector regression (SVR) were designed. The evaluation of the proposed models showed RF outperformed other proposed models for estimating COD and SS with accuracy 91 % and 95 % in terms coefficient of determination (R2); however, GB was found the best, with accuracy 92 %, for detecting the BOD performance of ALWATP. This indicates ensemble learning models, RF and GB, can be considered a superiority soft solution for estimating physio-chemical characteristics of wastewater treatment plant.