Development of sediment load estimation models by using artificial neural networking techniques

Environ Monit Assess. 2015 Nov;187(11):686. doi: 10.1007/s10661-015-4866-y. Epub 2015 Oct 13.


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.

Keywords: Artificial neural networks; Dead storage; Gamma test; Physical parameters; Sedimentation.

MeSH terms

  • Algorithms
  • Environmental Monitoring / methods*
  • Environmental Pollutants / analysis*
  • Environmental Pollutants / chemistry
  • Geologic Sediments / chemistry*
  • Linear Models
  • Models, Theoretical
  • Neural Networks, Computer*
  • Pakistan


  • Environmental Pollutants