Articles | Volume 17, issue 6
https://doi.org/10.5194/hess-17-2297-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/hess-17-2297-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
25 Jun 2013
Research article |
| 25 Jun 2013
Stochastic modeling of Lake Van water level time series with jumps and multiple trends
Istanbul Technical University, Istanbul, Turkey
N. E. Unal
Istanbul Technical University, Istanbul, Turkey
E. Eris
Ege University, Izmir, Turkey
M. I. Yuce
University of Gaziantep, Gaziantep, Turkey
Viewed
Total article views: 4,664 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 22 Feb 2013)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,844 | 2,656 | 164 | 4,664 | 133 | 112 |
- HTML: 1,844
- PDF: 2,656
- XML: 164
- Total: 4,664
- BibTeX: 133
- EndNote: 112
Total article views: 3,630 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 25 Jun 2013)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,403 | 2,100 | 127 | 3,630 | 118 | 106 |
- HTML: 1,403
- PDF: 2,100
- XML: 127
- Total: 3,630
- BibTeX: 118
- EndNote: 106
Total article views: 1,034 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 22 Feb 2013)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 441 | 556 | 37 | 1,034 | 15 | 6 |
- HTML: 441
- PDF: 556
- XML: 37
- Total: 1,034
- BibTeX: 15
- EndNote: 6
Cited
23 citations as recorded by crossref.
- Prediction of Water Level Using Machine Learning and Deep Learning Techniques I. Ayus et al. 10.1007/s40996-023-01053-6
- Regressive-stochastic models for predicting water level in Lake Urmia B. Vaheddoost & H. Aksoy 10.1080/02626667.2021.1974447
- Water Inflow Forecasting Based on Visual MODFLOW and GS-SARIMA-LSTM Methods Z. Yang et al. 10.3390/w16192749
- Monthly reservoir inflow forecasting using a new hybrid SARIMA genetic programming approach H. Moeeni et al. 10.1007/s12040-017-0798-y
- Application of stochastic models in predicting Lake Malawi water levels M. Rodgers et al. 10.5897/IJWREE2017.0740
- Short-term forecast of Yangtze River water level based on Long Short-Term Memory neural network S. Chen & Y. Qiao 10.1088/1755-1315/831/1/012051
- Modeling 25 years of spatio-temporal surface water and inundation dynamics on large river basin scale using time series of Earth observation data V. Heimhuber et al. 10.5194/hess-20-2227-2016
- Multiple Remotely Sensed Lines of Evidence for a Depleting Seasonal Snowpack in the Near East Y. Yılmaz et al. 10.3390/rs11050483
- Investigation of recent level changes in Lake Van using water balance, LSTM and ANN approaches M. Aydin et al. 10.1007/s13201-023-02095-x
- Lake water-level fluctuation forecasting using machine learning models: a systematic review S. Zhu et al. 10.1007/s11356-020-10917-7
- Remote sensing monitoring of ecological changes in Lake Yueliang wetland and its response to inundation frequency in Western Songnen Plain during 1994-2018 L. Xiaodong et al. 10.18307/2022.0421
- Statistical assessment of interbasin water transfer for karst areas (Turkey) A. Sanlı et al. 10.1007/s12517-021-08693-w
- Improved river water-stage forecasts by ensemble learning S. Li & J. Yang 10.1007/s00366-022-01751-1
- Urmia Lake water-level change detection and modeling F. Fathian et al. 10.1007/s40808-016-0253-0
- Determination of Burdur Lake’s areal change in upcoming years using geographic information systems and the artificial neural network method K. Hepdeniz 10.1007/s12517-020-06137-5
- A REVIEW OF K-MEDOID ALGORITHM BASED LEVEL LAKE DETECTION D. PRIYANKA & S. SANJIVANI 10.26634/jse.12.1.13920
- Forecasting of water level in multiple temperate lakes using machine learning models S. Zhu et al. 10.1016/j.jhydrol.2020.124819
- Prediction of water levels in large reservoirs base on optimization of deep learning algorithms H. Li et al. 10.1007/s12145-024-01670-3
- A Spaceborne Multisensory, Multitemporal Approach to Monitor Water Level and Storage Variations of Lakes A. Taravat et al. 10.3390/w8110478
- GÖL SEVİYE TAHMİNİ: EĞİRDİR GÖLÜ M. KESKİN et al. 10.21923/jesd.340383
- Forecasting surface water-level fluctuations of a small glacial lake in Poland using a wavelet-based artificial intelligence method A. Piasecki et al. 10.1007/s11600-018-0183-5
- Unravelling the spatiotemporal variation in the water levels of Poyang Lake with the variational mode decomposition model M. Gan et al. 10.1002/hyp.15239
- Research on the long-term and short-term forecasts of navigable river’s water-level fluctuation based on the adaptive multilayer perceptron T. Zhou et al. 10.1016/j.jhydrol.2020.125285
22 citations as recorded by crossref.
- Prediction of Water Level Using Machine Learning and Deep Learning Techniques I. Ayus et al. 10.1007/s40996-023-01053-6
- Regressive-stochastic models for predicting water level in Lake Urmia B. Vaheddoost & H. Aksoy 10.1080/02626667.2021.1974447
- Water Inflow Forecasting Based on Visual MODFLOW and GS-SARIMA-LSTM Methods Z. Yang et al. 10.3390/w16192749
- Monthly reservoir inflow forecasting using a new hybrid SARIMA genetic programming approach H. Moeeni et al. 10.1007/s12040-017-0798-y
- Application of stochastic models in predicting Lake Malawi water levels M. Rodgers et al. 10.5897/IJWREE2017.0740
- Short-term forecast of Yangtze River water level based on Long Short-Term Memory neural network S. Chen & Y. Qiao 10.1088/1755-1315/831/1/012051
- Modeling 25 years of spatio-temporal surface water and inundation dynamics on large river basin scale using time series of Earth observation data V. Heimhuber et al. 10.5194/hess-20-2227-2016
- Multiple Remotely Sensed Lines of Evidence for a Depleting Seasonal Snowpack in the Near East Y. Yılmaz et al. 10.3390/rs11050483
- Investigation of recent level changes in Lake Van using water balance, LSTM and ANN approaches M. Aydin et al. 10.1007/s13201-023-02095-x
- Lake water-level fluctuation forecasting using machine learning models: a systematic review S. Zhu et al. 10.1007/s11356-020-10917-7
- Remote sensing monitoring of ecological changes in Lake Yueliang wetland and its response to inundation frequency in Western Songnen Plain during 1994-2018 L. Xiaodong et al. 10.18307/2022.0421
- Statistical assessment of interbasin water transfer for karst areas (Turkey) A. Sanlı et al. 10.1007/s12517-021-08693-w
- Improved river water-stage forecasts by ensemble learning S. Li & J. Yang 10.1007/s00366-022-01751-1
- Urmia Lake water-level change detection and modeling F. Fathian et al. 10.1007/s40808-016-0253-0
- Determination of Burdur Lake’s areal change in upcoming years using geographic information systems and the artificial neural network method K. Hepdeniz 10.1007/s12517-020-06137-5
- A REVIEW OF K-MEDOID ALGORITHM BASED LEVEL LAKE DETECTION D. PRIYANKA & S. SANJIVANI 10.26634/jse.12.1.13920
- Forecasting of water level in multiple temperate lakes using machine learning models S. Zhu et al. 10.1016/j.jhydrol.2020.124819
- Prediction of water levels in large reservoirs base on optimization of deep learning algorithms H. Li et al. 10.1007/s12145-024-01670-3
- A Spaceborne Multisensory, Multitemporal Approach to Monitor Water Level and Storage Variations of Lakes A. Taravat et al. 10.3390/w8110478
- GÖL SEVİYE TAHMİNİ: EĞİRDİR GÖLÜ M. KESKİN et al. 10.21923/jesd.340383
- Forecasting surface water-level fluctuations of a small glacial lake in Poland using a wavelet-based artificial intelligence method A. Piasecki et al. 10.1007/s11600-018-0183-5
- Unravelling the spatiotemporal variation in the water levels of Poyang Lake with the variational mode decomposition model M. Gan et al. 10.1002/hyp.15239
Saved (final revised paper)
Latest update: 23 Aug 2025
Special issue