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Phase-space reconstruction and self-exciting threshold models in lake level forecasting: a case study of the three largest lakes of Sweden

Tongal, Hakan and Berndtsson, Ronny LU (2014) In Stochastic Environmental Research and Risk Assessment 28(4). p.955-971
Abstract
Lake water level forecasting is very important for an accurate and reliable management of local and regional water resources. In the present study two nonlinear approaches, namely phase-space reconstruction and self-exciting threshold autoregressive model (SETAR) were compared for lake water level forecasting. The modeling approaches were applied to high-quality lake water level time series of the three largest lakes in Sweden; Vänern, Vättern, and Mälaren. Phase-space reconstruction was applied by the k-nearest neighbor (k-NN) model. The k-NN model parameters were determined using autocorrelation, mutual information functions, and correlation integral. Jointly, these methods indicated chaotic behavior for all lake water levels. The... (More)
Lake water level forecasting is very important for an accurate and reliable management of local and regional water resources. In the present study two nonlinear approaches, namely phase-space reconstruction and self-exciting threshold autoregressive model (SETAR) were compared for lake water level forecasting. The modeling approaches were applied to high-quality lake water level time series of the three largest lakes in Sweden; Vänern, Vättern, and Mälaren. Phase-space reconstruction was applied by the k-nearest neighbor (k-NN) model. The k-NN model parameters were determined using autocorrelation, mutual information functions, and correlation integral. Jointly, these methods indicated chaotic behavior for all lake water levels. The correlation dimension found for the three lakes was 3.37, 3.97, and 4.44 for Vänern, Vättern, and Mälaren, respectively. As a comparison, the best SETAR models were selected using the Akaike Information Criterion. The best SETAR models in this respect were (10,4), (5,8), and (7,9) for Vänern, Vättern, and Mälaren, respectively. Both model approaches were evaluated with various performance criteria. Results showed that both modeling approaches are efficient in predicting lake water levels but the phase-space reconstruction (k-NN) is superior to the SETAR model. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Chaos, Phase-space reconstruction, method, K-nearest neighbor (k-NN), Self-exciting threshold autoregressive model (SETAR)
in
Stochastic Environmental Research and Risk Assessment
volume
28
issue
4
pages
955 - 971
publisher
Springer
external identifiers
  • wos:000334446000017
  • scopus:84898421926
ISSN
1436-3240
DOI
10.1007/s00477-013-0795-x
language
English
LU publication?
yes
id
f7105300-f029-4117-8c4c-740e3c7efcf1 (old id 4221643)
date added to LUP
2014-01-16 11:10:52
date last changed
2017-07-30 04:10:19
@article{f7105300-f029-4117-8c4c-740e3c7efcf1,
  abstract     = {Lake water level forecasting is very important for an accurate and reliable management of local and regional water resources. In the present study two nonlinear approaches, namely phase-space reconstruction and self-exciting threshold autoregressive model (SETAR) were compared for lake water level forecasting. The modeling approaches were applied to high-quality lake water level time series of the three largest lakes in Sweden; Vänern, Vättern, and Mälaren. Phase-space reconstruction was applied by the k-nearest neighbor (k-NN) model. The k-NN model parameters were determined using autocorrelation, mutual information functions, and correlation integral. Jointly, these methods indicated chaotic behavior for all lake water levels. The correlation dimension found for the three lakes was 3.37, 3.97, and 4.44 for Vänern, Vättern, and Mälaren, respectively. As a comparison, the best SETAR models were selected using the Akaike Information Criterion. The best SETAR models in this respect were (10,4), (5,8), and (7,9) for Vänern, Vättern, and Mälaren, respectively. Both model approaches were evaluated with various performance criteria. Results showed that both modeling approaches are efficient in predicting lake water levels but the phase-space reconstruction (k-NN) is superior to the SETAR model.},
  author       = {Tongal, Hakan and Berndtsson, Ronny},
  issn         = {1436-3240},
  keyword      = {Chaos,Phase-space reconstruction,method,K-nearest neighbor (k-NN),Self-exciting threshold autoregressive model (SETAR)},
  language     = {eng},
  number       = {4},
  pages        = {955--971},
  publisher    = {Springer},
  series       = {Stochastic Environmental Research and Risk Assessment},
  title        = {Phase-space reconstruction and self-exciting threshold models in lake level forecasting: a case study of the three largest lakes of Sweden},
  url          = {http://dx.doi.org/10.1007/s00477-013-0795-x},
  volume       = {28},
  year         = {2014},
}