22 citations as recorded by crossref.

1. Prediction of Water Level Using Machine Learning and Deep Learning Techniques I. Ayus et al. 10.1007/s40996-023-01053-6
2. Regressive-stochastic models for predicting water level in Lake Urmia B. Vaheddoost & H. Aksoy 10.1080/02626667.2021.1974447
3. Water Inflow Forecasting Based on Visual MODFLOW and GS-SARIMA-LSTM Methods Z. Yang et al. 10.3390/w16192749
4. Monthly reservoir inflow forecasting using a new hybrid SARIMA genetic programming approach H. Moeeni et al. 10.1007/s12040-017-0798-y
5. Application of stochastic models in predicting Lake Malawi water levels M. Rodgers et al. 10.5897/IJWREE2017.0740
6. 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
7. 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
8. Multiple Remotely Sensed Lines of Evidence for a Depleting Seasonal Snowpack in the Near East Y. Yılmaz et al. 10.3390/rs11050483
9. 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
10. Lake water-level fluctuation forecasting using machine learning models: a systematic review S. Zhu et al. 10.1007/s11356-020-10917-7
11. 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
12. Statistical assessment of interbasin water transfer for karst areas (Turkey) A. Sanlı et al. 10.1007/s12517-021-08693-w
13. Improved river water-stage forecasts by ensemble learning S. Li & J. Yang 10.1007/s00366-022-01751-1
14. Urmia Lake water-level change detection and modeling F. Fathian et al. 10.1007/s40808-016-0253-0
15. 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
16. A REVIEW OF K-MEDOID ALGORITHM BASED LEVEL LAKE DETECTION D. PRIYANKA & S. SANJIVANI 10.26634/jse.12.1.13920
17. Forecasting of water level in multiple temperate lakes using machine learning models S. Zhu et al. 10.1016/j.jhydrol.2020.124819
18. A Spaceborne Multisensory, Multitemporal Approach to Monitor Water Level and Storage Variations of Lakes A. Taravat et al. 10.3390/w8110478
19. GÖL SEVİYE TAHMİNİ: EĞİRDİR GÖLÜ M. KESKİN et al. 10.21923/jesd.340383
20. 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
21. 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
22. 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