Comparison of novel physics-guided machine learning models with empirical equations for predicting longitudinal dispersion coefficient in diverse natural river systems-Reference-Cited by-同舟云学术

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Comparison of novel physics-guided machine learning models with empirical equations for predicting longitudinal dispersion coefficient in diverse natural river systems

Published:2024-09 Issue: Volume:66 Page:106067
ISSN:2214-7144
Container-title:Journal of Water Process Engineering
language:en
Short-container-title:Journal of Water Process Engineering

Author:

Shadkani Sadra,Hemmatzadeh Yousef,Shiri Jalal,Saber Ali,Pak Amirreza

Publisher

Elsevier BV

Reference46 articles.

1. Assessment of surface water quality: case study of Oued Fez catchment areas (Morocco);Sanae;Environ. Sustain. Indic.,2024

2. River Pollution in India: Exploring Regulatory and Remedial Paths;Kumar,2024

3. Analysis of pollutant dispersion patterns in rivers under different rainfall based on an integrated water-land model;Lin;J. Environ. Manag.,2024

4. Predicting longitudinal dispersion coefficient in natural streams by artificial neural network;Tayfur;J. Hydraul. Eng.,2005

5. Estimating longitudinal dispersion coefficient in natural streams using empirical models and machine learning algorithms;Kargar;Eng. Appl. Computa. Fluid Mech.,2020

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