Enhancing riverine load prediction of anthropogenic pollutants: Harnessing the potential of feed-forward backpropagation (FFBP) artificial neural network (ANN) models

Assessing riverine pollutant loads is a more realistic method for analysing point and non-point anthropogenic pollution sources throughout a watershed. This study compares numerous mathematical modelling strategies for estimating riverine loads based on the chosen water quality parameters: Biochemic...

Full description

Saved in:

Bibliographic Details
Main Authors:	Khairudin K., Ul-Saufie A.Z., Senin S.F., Zainudin Z., Rashid A.M., Abu Bakar N.F., Anas Abd Wahid M.Z., Azha S.F., Abd-Wahab F., Wang L., Sahar F.N., Osman M.S.
Other Authors:	57838061200
Format:	Article
Published:	Elsevier B.V. 2025
Subjects:	Ammonia Biochemical oxygen demand Dissolved oxygen Errors Mean square error Radial basis function networks River pollution Rivers Suspended sediments Water quality Artificial neural network Feed-forward backpropagation algorithm Feedforward backpropagation Input variables Load predictions Multiple linear regressions Pollutant concentration Radial basis neural networks Riverine load Statistic modeling Multiple linear regression
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!

Enhancing riverine load prediction of anthropogenic pollutants: Harnessing the potential of feed-forward backpropagation (FFBP) artificial neural network (ANN) models

Similar Items