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...

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Veröffentlicht in:	Results in engineering Jg. 22; S. 102072
Hauptverfasser:	Khairudin, Khairunnisa, Ul-Saufie, Ahmad Zia, Senin, Syahrul Fithry, Zainudin, Zaki, Rashid, Ammar Mohd, Abu Bakar, Noor Fitrah, Anas Abd Wahid, Muhammad Zakwan, Azha, Syahida Farhan, Abd-Wahab, Firdaus, Wang, Lei, Sahar, Farisha Nerina, Osman, Mohamed Syazwan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.06.2024 Elsevier
Schlagworte:	Artificial neural network (ANN) Feed-forward backpropagation algorithm Multiple linear regression Radial basis neural network Riverine load Riverine load Radial basis neural network Artificial neural network (ANN) Multiple linear regression Feed-forward backpropagation algorithm
ISSN:	2590-1230, 2590-1230
Online-Zugang:	Volltext
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