Prediction of aeration performance of different types of piano key weirs using different machine learning models

ABSTRACT Aeration is a cost-effective and efficient method for increasing the available oxygen or dissolved oxygen content in water bodies, which is crucial for the existence of aquatic life. However, conventional techniques for estimating aeration in different hydraulic structures are time-consuming and incorrect ways to approximate aeration. Therefore, new, computationally more efficient, and more accurate methods are required. In this article, three machine learning models are presented: (1) ELM (extreme learning machine) model, (2) online sequential extreme learning machine model, and (3) I-ELM (incremental extreme learning machine) model. These models assess the air conditioning capacity of the three variants of Piano Key Weirs (PKWs), denoted as A, B, and C, about Cd, Cs, and Cu, which are the three most important parameters for aeration efficiency at different temperatures. The model performance is evaluated and compared based on mean squared error, root-mean-square error, correlation coefficient, mean absolute error, and Nash–Sutcliffe efficiency. This research concludes that I-ELM is the best-performing model for complete available data that are time invariant.