Development of sediment load estimation models by using artificial neural networking techniques.
Environ Monit Assess
; 187(11): 686, 2015 Nov.
Article
en En
| MEDLINE
| ID: mdl-26463089
ABSTRACT
This study aims at the development of an artificial neural network-based model for the estimation of weekly sediment load at a catchment located in northern part of Pakistan. The adopted methodology has been based upon antecedent sediment conditions, discharge, and temperature information. Model input and data length selection was carried out using a novel mathematical tool, Gamma test. Model training was carried out by using three popular algorithms namely Broyden-Fletcher-Goldfarb-Shanno (BFGS), back propagation (BP), and local linear regression (LLR) using forward selection of input variables. Evaluation of the best model was carried out on the basis of basic statistical parameters namely R-square, root mean squared error (RMSE), and mean biased error (MBE). Results indicated that BFGS-based ANN model outperformed all other models with significantly low values of RMSE and MBE. A strong correlation was also found between the observed and estimated sediment load values for the same model as the value of Nash-Sutcliffe model efficiency coefficient (R-square) was found to be quite high as well.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Contexto en salud:
2_ODS3
Problema de salud:
2_quimicos_contaminacion
Asunto principal:
Monitoreo del Ambiente
/
Redes Neurales de la Computación
/
Sedimentos Geológicos
/
Contaminantes Ambientales
Tipo de estudio:
Prognostic_studies
País/Región como asunto:
Asia
Idioma:
En
Revista:
Environ Monit Assess
Asunto de la revista:
SAUDE AMBIENTAL
Año:
2015
Tipo del documento:
Article
País de afiliación:
Pakistán