Long term forecasting of groundwater levels with evidence of non-stationary and nonlinear characteristics

Maheswaran Rathinasamy, Rakesh Khosa

Research output: Contribution to journalArticlepeer-review

35 Scopus citations

Abstract

Groundwater systems are in general characterised by non-stationary and nonlinear features. Modelling of these systems and forecasting their future states requires identification and capture of these underlying features that seem to drive these processes. Recently, wavelets have been used extensively in the area of hydrologic and environmental time series forecasting owing to its ability to unravel these aforementioned component features. In this paper, dynamic wavelet based nonlinear model (Wavelet Volterra coupled model) is tested for its ability to yield reliable long term forecasts of groundwater levels at two sites in Canada. The model results are compared with the results from other recent techniques like wavelet neural network (WA-ANN), Wavelet linear regression (WLR), Artificial neural network and dynamic auto regressive (DAR) Models. The results of the study show the potential of wavelet Volterra coupled models in forecasting groundwater levels in addition to being more versatile and simpler to use when compared with other competing models.

Original languageEnglish (US)
Pages (from-to)422-436
Number of pages15
JournalComputers and Geosciences
Volume52
DOIs
StatePublished - Jan 1 2013

Keywords

  • Groundwater level forecasting
  • Long term forecasting
  • Wavelets

Fingerprint Dive into the research topics of 'Long term forecasting of groundwater levels with evidence of non-stationary and nonlinear characteristics'. Together they form a unique fingerprint.

Cite this