Related Experiment Video
Updated: Aug 7, 2026

Capturing Flow-weighted Water and Suspended Particulates from Agricultural Canals During Drainage Events
Published on: November 7, 2017
Neural networks modelling of streamflow, phosphorus, and suspended solids: application to the Canadian Boreal forest
M H Nour1, D W Smith, M Gamal El-Din
1Department of Civil and Environmental Engineering, University of Alberta, Edmonton, Alberta, Canada. mnour@ualberta.ca
Abstract:
Sediment has long been identified as an important vector for the transport of nutrients and contaminants such as heavy metals and microorganisms. The respective nutrient loading to water bodies can potentially lead to dissolved oxygen depletion, cyanobacteria toxin production and ultimately eutrophication. This study proposed an artificial neural network (ANN) modelling algorithm that relies on low cost readily available meteorological data for simulating streamflow (Q), total suspended solids (TSS) concentration, and total phosphorus (TP) concentration. The models were applied to a 130-km2 watershed in the Canadian Boreal Plain. Our results demonstrated that through careful manipulation of time series analysis and rigorous optimization of ANN configuration, it is possible to simulate Q, TSS, and TP reasonably well. R2 values exceeding 0.89 were obtained for all modelled data cases. The proposed models can provide real time predictions of the modelled parameters, can answer questions related to the impact of climate change scenarios on water quantity and quality, and can be implemented in water resources management through Monte Carlo simulations.
Related Concept Videos
Typical Model Studies
Rapidly Varying Flow
Modeling and Similitude
Design Example: Creating a Hydraulic Model of a Dam Spillway
Gradually Varying Flow