Multiple statistical model ensemble predictions of residual chlorine in drinking water: applications of various deep learning and machine learning algorithms

HIGHLIGHTS

• who: Charles Onyutha from the Department of Civil and Environmental Engineering, Kyambogo University, POBox, Kyambogo, Kampala, Uganda have published the research work: Multiple Statistical Model Ensemble Predictions of Residual Chlorine in Drinking Water: Applications of Various Deep Learning and Machine Learning Algorithms, in the Journal: Journal of Environmental and Public Health of 25/07/2022
• what: This study demonstrated the acceptability of statistical MMEs in predicting chlorine residual concentration in drinking water. This study is aimed at addressing this knowledge gap in scientific research. The aim of a feedforward neural_network (FFN) is to approximate some . . .
  

   

  

  If you want to have access to all the content you need to log in!

  Thanks :)

  Username or Email

  Password

  Remember me

  Lost your password?

  If you don't have an account, you can create one here.