A fusion-based neural network methodology for monthly reservoir inflow prediction using MODIS products

Mahboubeh Ghazali, Tooraj Honar*, Mohammad Reza Nikoo

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

A model fusion approach was developed based on five artificial neural networks (ANNs) and MODIS products. Static and dynamic ANNs–the multi-layer perceptron (MLP) with one and two hidden layers, general regression neural network (GRNN), radial basis function (RBF) and nonlinear autoregressive network with exogenous inputs (NARX)–were used to predict the monthly reservoir inflow in Mollasadra Dam, Fars Province, Iran. Leaf area index and snow cover from MODIS, and rainfall and runoff data were used to identify eight different combinations to train the models. Statistical error indices and the Borda count method were used to verify and rank the identified combinations. The best results for individual ANNs were combined with MODIS products in a fusion model. The results show that using MODIS products increased the accuracy of predictions, with the MLP with two hidden layers giving the best performance. Also, the fusion model was found to be superior to the best individual ANNs.

Original languageEnglish
Pages (from-to)2076-2096
Number of pages21
JournalHydrological Sciences Journal
Volume63
Issue number15-16
DOIs
Publication statusPublished - Dec 10 2018

Keywords

  • artificial neural network
  • Borda count method
  • leaf area index (LAI)
  • Model fusion
  • MODIS
  • monthly reservoir runoff
  • snow cover

ASJC Scopus subject areas

  • Water Science and Technology

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