Developing nonlinear models for sediment load estimation in an irrigation canal

Ahmed, F.; Hassan, M.; Hashmi, H. N.

doi:10.1007/s11600-018-0221-3

Artykuł - szczegóły

Tytuł artykułu

Developing nonlinear models for sediment load estimation in an irrigation canal

Autorzy

Ahmed F. , Hassan M. , Hashmi H. N.

Wybrane pełne teksty z tego czasopisma

https://www.springer.com/journal/11600

Identyfikatory

DOI

10.1007/s11600-018-0221-3

Warianty tytułu

Języki publikacji

Abstrakty

The study was performed to estimate the weekly sediment load in Thal canal located in Mianwali district Punjab, Pakistan. Past records of sediments and discharge have been considered as the input parameters. The best input combinations have been identified with the help of advanced algorithms including full, sequential and increasing embedding, genetic algorithm and hill climbing in combination with the gamma test. Model training has been carried out using two artificial neural networkbased algorithms, namely Broyden–Fletcher–Goldfarb–Shanno (BFGS), back-propagation and a local linear regression technique. A variety of statistical parameters including R square, root mean squared error, mean square error and mean bias error (MBE) has been calculated in order to evaluate the best models. The results strongly suggested that BFGS-based model performed better than all other models with remarkably low values of MBE. Significantly high values of correlation coefficient (R square) in both training and testing evidenced a close similarity between actual and predicted sediment load values for the same model.

Słowa kluczowe

artificial neural networks gamma test sediment load training testing

sztuczne sieci neuronowe test gamma ładunek osadu trening testowanie

Wydawca

Instytut Geofizyki PAN
Springer

Czasopismo

Acta Geophysica

Rocznik

2018

Tom

Vol. 66, no. 6

Strony

1485--1494

Opis fizyczny

Bibliogr. 72 poz.

Twórcy

autor

Ahmed F.

fahad.ahmed@uos.edu.pk

Department of Civil Engineering, College of Engineering and Technology, University of Sargodha, Sargodha 40100, Pakistan

autor

Hassan M.

hassan.ce@must.edu.pk

Department of Civil Engineering, Mirpur University of Science and Technology, Azad Kashmir, Pakistan

autor

Hashmi H. N.

hashim.nisar@uettaxila.edu.pk

Department of Civil Engineering, University of Engineering and Technology Taxila, Taxila, Pakistan

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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-94764c4a-5db5-4081-bbcb-08c96c79a8d3