Asset Details
Do you wish to reserve the book?
Prediction of aeration performance of different types of piano key weirs using different machine learning models
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Prediction of aeration performance of different types of piano key weirs using different machine learning models
Do you wish to request the book?
Prediction of aeration performance of different types of piano key weirs using different machine learning models
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Prediction of aeration performance of different types of piano key weirs using different machine learning models
2024
Overview
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.
