The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here:

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Profile photo of Ronny Berndtsson

Ronny Berndtsson

Professor, Dep Director, MECW Dep Scientific Coordinator

Profile photo of Ronny Berndtsson

Forecasting discharge in Amazonia using artificial neural networks


  • Cíntia Bertacchi Uvo
  • Ute Tölle
  • Ronny Berndtsson

Summary, in English

The Amazon, located in northern South America, is the world's largest river basin, and covers an area of about 6.5 million km2. The observed interannual variability in precipitation and water availability during its main discharge season has been shown to be influenced by Pacific and Atlantic Ocean sea surface temperatures (SSTs). However, the links between large-scale atmospheric motion and local and regional runoff patterns are essentially complex and still not fully understood. The processes involved are believed to be highly non-linear, spatially and temporally variable, and not easily described by physical or conceptual models. Artificial neural networks (NN) were trained to forecast discharge, one and two seasons in advance, at ten river sites in Amazonia from Pacific and Atlantic Ocean SST anomalies. The NN with an input layer of eight neurons, one hidden layer with 20 neurons and a one-neuron output layer was trained using back-propagation with momentum and gradient descendent. Results confirmed that different oceanic regions have distinct influences on different parts of the Amazonian basin. Better forecasts for basins in the northern part of Amazonia were obtained from Pacific Ocean SST and from Atlantic Ocean SST for basins in the southern part. Correlation coefficients between observed and estimated discharge (validation) were as high as 0.76 at some of the sites studied. The inclusion of precipitation as input improved the forecast for sites where NN did not perform well with training by SST only as input. The results obtained during this study corroborate and improve results obtained previously by means of linear statistical methods. Copyright (C) 2000 Royal Meteorological Society.


  • Division of Water Resources Engineering

Publishing year







International Journal of Climatology





Document type

Journal article


John Wiley & Sons Inc.


  • Water Engineering


  • Amazon River
  • Amazonia
  • Curua-Una River
  • Discharge
  • Discharge forecast
  • Neural networks
  • Sea surface temperature




  • ISSN: 0899-8418