Monthly runoff prediction using phase-space reconstruction

Forskningsoutput: TidskriftsbidragArtikel i vetenskaplig tidskrift

Abstract

A nonlinear prediction method, developed based on the ideas gained from deterministic chaos theory, is employed: (a) to predict monthly runoff; and (b) to detect the possible presence of chaos in runoff dynamics. The method first reconstructs the single-dimensional (or variable) runoff series in a multi-dimensional phase space to represent its dynamics, and then uses a local polynomial approach to make predictions. Monthly runoff series observed at the Coaracy Nunes/Araguari River basin in northern Brazil is studied. The predictions are found to be in close agreement with the observed runoff, with high correlation coefficient and coefficient of efficiency values, indicating the suitability of the nonlinear prediction method for predicting the runoff dynamics. The results also reveal the presence of low-dimensional chaos in the runoff dynamics, when an inverse approach is adopted for identification, as: (a) an optimal embedding dimension exists, and (b) the prediction accuracy decreases with an increase in prediction lead lime.

Detaljer

Författare
Enheter & grupper
Externa organisationer
  • University of California, Davis
Forskningsområden

Ämnesklassifikation (UKÄ) – OBLIGATORISK

  • Oceanografi, hydrologi, vattenresurser

Nyckelord

Originalspråkengelska
Sidor (från-till)377-387
Antal sidor11
TidskriftHydrological Sciences Journal
Volym46
Utgåva nummer3
StatusPublished - 2001
PublikationskategoriForskning
Peer review utfördJa