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Prediction of daily suspended sediment load of rivers using novel hybrid deep learning algorithms (studied river: Mississippi river)
Hydraulics And Water Resources EngineeringBachelor'sUniversity of Tabriz2018-08-31 Iran
Title :
Numerical modeling of discharge in water supply networks
Work Experiences
Teaching Assistant
at University of Tabriz
I am currently working in this role
Research Assistant
at University of Tabriz
I am currently working in this role
Reviewer of Journals
at -
I am currently working in this role
English Scores
IELTS24-Feb-2023
6.5/9Total
Reading :
6/9
Speaking :
7/9
Listening :
7/9
Writing :
6/9
IELTS05-Feb-2019
7.5/9Total
Reading :
7.5/9
Speaking :
7/9
Listening :
8.5/9
Writing :
7/9
Journal Publications
Publication Title :
Comparison of the efficacy of particle swarm optimization and stochastic gradient descent algorithms on multi-layer perceptron model to estimate longitudinal dispersion coefficients in natural streamsThird author
Journal :
Engineering Applications of Computational Fluid Mechanics
Hybrid models for suspended sediment prediction: optimized random forest and multi-layer perceptron through genetic algorithm and stochastic gradient descent methodsThird author
Comparative study of multilayer perceptron-stochastic gradient descent and gradient boosted trees for predicting daily suspended sediment load: The case study of the Mississippi River, U.S.First author
Random Forest and Multilayer Perceptron hybrid models integrated with the genetic algorithm for predicting pan evaporation of the target site using a limited set of neighboring referencing station dataFirst author