EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 393 (2023) E3S Web of Conf., 393 (2023) 02003 References
Open Access
Issue
E3S Web of Conf.
Volume 393, 2023
2023 5th International Conference on Environmental Prevention and Pollution Control Technologies (EPPCT 2023)
Article Number 02003
Number of page(s) 11
Section Ecological Protection and Sustainable Development Research
DOI https://doi.org/10.1051/e3sconf/202339302003
Published online 02 June 2023
  1. Woolway, R. I.; Kraemer, B. M.; Lenters, J. D.; et al. (2020) Global lake responses to climate change. J. Nature Reviews: Earth & Environment., 13 (3686): 1–16. [Google Scholar]
  2. Zhao, G.; Li, Y.; Zhou, L. M.; et al. (2022) Evaporative water loss of 1.42 million global lakes. J. Nature Communications., 1: 388–403. [Google Scholar]
  3. Jiang, T.; Zhang, Y. J.; Bao, Z. F.; et al. (2018) Water level control for quality of lakes: A case study of Cihu Lake. J. Engineering Journal of Wuhan University., 51 (7): 570-576. [Google Scholar]
  4. He, K. D.; Gao, W.; Duan, C. Q.; et al. (2019) Water level variation and its driving factors in lake Dianchi, Fuxian and Yangzong during 1988-2015. J. Journal of Lake Sciences., 31 (05): 1379-1390. [CrossRef] [Google Scholar]
  5. Liu, X. D.; Hou, Z. Y.; Xie, Y. H.; et al. (2021) Influence of water level on four typical submerged plants in wetlands of Lake Dongting. J. Journal of Lake Sciences., 33 (01): 181-191. [CrossRef] [Google Scholar]
  6. VanDeWeghe, A.; Lin, V.; Jayaram, J.; et al. (2022) Changes in Large Lake water level dynamics in response to climate change. J. Frontiers in water., 4(4): 1-12. [Google Scholar]
  7. Tyler, J. J.; Marshall, J. C.; Schulz, C.; et al. (2022) Hydrological and Isotopic Variability of Perched Wetlands on North Stradbroke Island (Minjerribah), Australia: Implications for Understanding the Effects of Past and Future Climate Change. J. Frontiers in Environmental Science., 10 (7): 1-21. [Google Scholar]
  8. Tetsuya, T.; Ralph, D. L. (2019) Modeling of Seasonal Lake Level Fluctuations of Titan’s Seas/Lakes. J. Journal of Geophysical Research: Planets., 124(2): 617-635. [CrossRef] [Google Scholar]
  9. Jayanthi, S. L. S. V.; Keesara, V. R.; Sridhar, V. (2022) Prediction of Future Lake Water Availability Using SWAT and Support Vector Regression. J. Sustainability., 14 (12): 6974: 1-17. [CrossRef] [Google Scholar]
  10. Yang, Z. H.; Zhu, Z. T.; Huai, W. X.; et al. (2018) Study on the influence of Poyang Lake Hydraulic project on hydrodynamics and water-quality in wet and dry year. J. Journal of Hydraulic Engineering., 49(2): 156-167. [Google Scholar]
  11. Li, J. L.; Li, H. Y.; Luo, L. C.; et al. (2022) Water level retrieval for the past and prediction for the next 30 years at Lake Fuxian. J. Journal of Lake Sciences., 34 (3): 958–971. [CrossRef] [Google Scholar]
  12. Du, Y.; Xie, M. W.; Hu, M.; et al. (2014) Construction of Nam Co Lake water level variation model and its driving mechanism identification. J. Yangtze River., 45 (5): 19–23. [Google Scholar]
  13. Dong, C. Y.; Wang, N. A.; Li, Z. L.; et al. (2009) Forecast of lake level in Lake Qinghai based on energy-water balance model. J. Journal of Lake Sciences., 21 (4): 587–593. [CrossRef] [Google Scholar]
  14. Li, Y.; Zhang, Y. X.; Zhang, X. Z.; et al. (2020) A continuous simulation of Holocene effective moisture change represented by variability of virtual lake level in East and Central Asia. J. Scientia Sinica(Terrae)., 50 (8): 1106-1121. [Google Scholar]
  15. Yasuda, H.; Fenta, A. A.; Berihun, M. L.; et al.(2022) Water level change of Lake Tana, source of the Blue Nile: Prediction using teleconnections with sea surface temperatures. J. Journal of Great Lakes Research., 48 (2): 468-477. [CrossRef] [Google Scholar]
  16. Fathian, F.; Modarres, R.; Dehghan, Z. (2016) Urmia Lake water-level change detection and modeling. J. Model. Earth Syst. Environ., 2 (203): 1-16. [CrossRef] [Google Scholar]
  17. Kakahaji, H.; Banadaki, H. D.; Kakahaji, A.; et al. (2013) Prediction of Urmia Lake Water-Level Fluctuations by Using Analytical, Linear Statistic and Intelligent Methods. J. Water Resources Management., 27: 4469-4492. [CrossRef] [Google Scholar]
  18. Ehteram, M.; Ferdowsi, A.; Faramarzpour, M.; et al. (2021) Hybridization of artificial intelligence models with nature inspired optimization algorithms for lake water level prediction and uncertainty analysis. J. Alexandria Engineering Journal., 60(2): 2193-2208 [CrossRef] [Google Scholar]
  19. Hossein, K.; Hamed, D.; Abbas, K.; et al. (2013) Prediction of Urmia Lake Water-Level Fluctuations by Using Analytical, Linear Statistic and Intelligent Methods. J. Water resources management., 27(13): 4469-4492. [CrossRef] [Google Scholar]
  20. Phan, T. T. H.; Nguyen, X. H. (2020) Combining statistical machine learning models with ARIMA for water level forecasting: The case of the Red River. J. Advances in Water Resources., 142 (103656): 1-15. [Google Scholar]
  21. John, R.; John, M. (2019) Adaptation of the visibility graph algorithm for detecting time lag between rainfall and water level fluctuations in Lake Okeechobee. J. Advances in Water Resources., 134(9), 103429: 1-16. [Google Scholar]
  22. Cheng, V. Y. S.; Saber, A.; Arnillas, C. A.; et al. (2021) Effects of hydrological forcing on short-and long-term water level fluctuations in Lake Huron-Michigan: A continuous wavelet analysis. J. Journal of Hydrology., 603: 127164. [CrossRef] [Google Scholar]
  23. Zhu, S. L.; Hrnjica, B.; Ptak, M.; et al. (2020) Forecasting of water level in multiple temperate lakes using machine learning models. J. Journal of Hydrology., 585, 124819. [CrossRef] [Google Scholar]
  24. Morovati, K.; Nakhaei, P.; Tian, F. Q.; et al. (2021) A Machine learning framework to predict reverse flow and water level: A case study of Tonle Sap Lake. J. Journal of Hydrology., 603, 127168. [CrossRef] [Google Scholar]
  25. Barzegar, R.; Aalami, M. T.; Adamowski, J. (2021) Coupling a hybrid CNN-LSTM deep learning model with a Boundary Corrected Maximal Overlap Discrete Wavelet Transform for multiscale Lake water level forecasting. J. Journal of Hydrology., 598, 126196. [CrossRef] [Google Scholar]
  26. Mohammadi, B.; Guan, Y. Q.; Aghelpour, P.; et al. (2021) Simulation of Titicaca Lake Water Level Fluctuations Using Hybrid Machine Learning Technique Integrated with Grey Wolf Optimizer Algorithm. J. Water., 12(11), 2015: 1-18. [Google Scholar]
  27. Fan, H. X.; He, H. D.; Xu, L. G.; et al. (2021) Simulation and attribution analysis based on the long-short-term-memory network for detecting the dominant cause of runoff variation in the Lake Poyang Basin. J. Journal of Lake Sciences., 33(3): 866-878. [CrossRef] [Google Scholar]
  28. Zhang, F.; Wang, J. Q.; Luo, H. (2016) Analysis and Forecast of Poyang Lake’s water level base on Time Series. J. Geomatics & Spatial Information Technology., 39 (08): 35-37+41. [Google Scholar]
  29. Li, Y. L.; Zhang, Q.; Li, M.; et al. (2015) Using BP neural networks for Water level simulation in Poyang Lake. J. Resources and Environment in the Yangtze Basin., 24 (2): 233–240. [Google Scholar]
  30. Guo, Y.; Lai, X. J. (2021) Research on water level prediction of Dongting lake based on Recurrent Neural Network. J. Resources and Environment in the Yangtze Basin., 30 (03): 689-698. [Google Scholar]
  31. Wang, M. M.; Dai, L. Q.; Dai, H. C.; et al. (2017) Support vector regression based model for predicting water level of Dongting Lake. J. Journal of Drainage and Irrigation Machinery., 35 (11): 954-961. [Google Scholar]
  32. Li, Y.; Shi, H.; Liu, H. (2020) A Hybrid Model for River Water Level Forecasting: Cases of Xiangjiang River and Yuanjiang River, China. J. Journal of Hydrology., 587, 124934. [CrossRef] [Google Scholar]
  33. Nair, A. S.; Kumar, N.; Indu, J. Vivek B. (2021) Monitoring Lake Levels From Space: Preliminary Analysis With SWOT. J. Frontiers in water., 3(12), 717852: 1-9. [Google Scholar]
  34. Bandini, F.; Jakobsen, J.; Olesen, D.; et al. (2017) Measuring water level in rivers and lakes from light-weight Unmanned Aerial Vehicles. J. Journal of Hydrology., 548: 237-250. [CrossRef] [Google Scholar]
  35. Wu, G. P.; Liu, Y. B.; Liu, R. (2022) Assessing the performance of the Tiangong-2 wide-swath imaging altimeter observations for water level monitoring over complex and shallow lakes. J. Journal of Hydrology., 612, 124819. [Google Scholar]
  36. Zhu, C. M.; Zhang, X.; Huang, Q. H. (2018) Lake hydrological information estimation based on remote sensing. J. Journal of China Hydrology., 38(5): 29-33. [Google Scholar]
  37. Devia, G. K.; Ganasri, B. P.; Dwarakish, G. S. (2015) A Review on Hydrological Models. J. Aquatic Procedia., 4: 1001-1007. [CrossRef] [Google Scholar]
  38. Karimi, S.; Shiri, J.; Kisi, O.; et al. (2012) Forecasting Water Level Fluctuations of Urmieh Lake Using Gene Expression Programming and Adaptive Neuro-Fuzzy Inference System. J. The International Journal of Ocean and Climate Systems., 3(2): 109-125. [CrossRef] [Google Scholar]
  39. Karimi, S.; Kisi, O.; Shiri, J.; et al. (2013) Neurofuzzy and neural network techniques for forecasting sea level in Darwin Harbor, Australia. J. Computers and Geosciences., 52: 50-59. [CrossRef] [Google Scholar]
  40. Wang, C. H. (2016) Application of wavelet neural network model in Liaoning plain groundwater prediction. J. Water Sciences and Engineering Technology., (03): 44-46. [Google Scholar]
  41. Buyukyildiz, M.; Tezel, G.; Yilmaz, V. (2014) Estimation of the Change in Lake Water Level by Artificial Intelligence Methods. J. Water Resources Management., 28(13): 4747-4763. [CrossRef] [Google Scholar]
  42. Wang, X. J.; Mao, H. T.; Huang, Q. H.; et al. (2021) Analysis of rainfall-runoff relationship before and after impoundment of Three Gorges Reservoir Wan County section based on Copula function. J. Journal of Water Resources and Water Engineering., 32 (02): 23-30+37. [Google Scholar]
  43. Haff, L.H.; Frigessi, A.; Maraun, D. (2015) How well do regional climate models simulate the spatial dependence of precipitation? An application of pair-copula constructions. J. Geophys. Res. Atmos., 120 (7): 2624-2646. [CrossRef] [Google Scholar]
  44. Wu, H. O.; Tu, X. J.; Du, Y. L.; et al. (2019) Multi-dimensional analysis of wetness-dryness encountering of streamflow based on the Copula function in Lake Poyang Basin. J. Journal of Lake Sciences., 31 (03): 801-813. [CrossRef] [Google Scholar]
  45. Chen, L.; Guo, S. L. (2019) Copulas and Its Application in Hydrology and Water Resources. M. Singapore., 97-116. [CrossRef] [Google Scholar]
  46. Scott, C. W.; Scott, S.; William, F.; et al. (2019) Copula theory as a generalized framework for flow-duration curve based streamflow estimates in ungaged and parially gaged catchments. J. Water Resources Research., 55 (11): 9378-9397. [Google Scholar]
  47. Gu, S. X.; Zhao, Z.; Chen, J.; et al. (2020) Daily reference evapotranspiration and meteorological drought forecast using high-dimensional Copula joint distribution model. J. Transactions of the Chinese Society of Agricultural Engineering., 36 (09): 143-151. [Google Scholar]
  48. Yan, B. W.; Pan, Z.; Xue, Y.; et al. (2017) Modeling dependence and correlation in hydrological calculation. J. Journal of Hydraulic Engineering., 48 (09): 1039-1046. [Google Scholar]
  49. Fan, G. B.; Zeng, Y.; Huang, W. G. (2013) A new approach for measuring the risk of portfolios with Multiple Assets. J. Journal of Quantitative & Technological Economics., 30 (01): 88-102. [Google Scholar]
  50. Daneshkhah, A.; Remesan, R.; Chatrabgoun, O.; et al. (2016) Probabilistic Modeling of Flood Characterizations with Parametric and Minimum Information Pair-Copula Model. J. Journal of Hydrology., 540: 469-487. [CrossRef] [Google Scholar]
  51. Haberlandt, U.; Ebner von Eschenbach, A.-D.; Buchwald, I. A. (2008) Space-Time Hybrid Hourly Rainfall Model for Derived Flood Frequency Analysis. J. Hydrology and Earth System Sciences., 12(6): 1353-1367. [CrossRef] [Google Scholar]
  52. Ji, C. M.; Ma, H. Y.; Peng, Y.; et al. (2021) Prediction of Meteorological drought in Huai River Basin considering multiple climatic indices. J. China Rural Water and Hydropower., (04): 16-21. [Google Scholar]
  53. Pereira, G.; Veiga, A. (2018) PAR(p) -vine copula based model for stochastic streamflow scenario generation. J. Stoch. Environ. Res. Risk Assess., 32 (3): 833-842. [CrossRef] [Google Scholar]
  54. Pereira, G. A. D.; Veiga, A. (2019) Periodic copula autoregressive model designed to multivariate stream-flow time series modelling. J. Water Resour. Manag., 33 (10): 3417-3431. [CrossRef] [Google Scholar]
  55. Pham, M. T.; Vernieuwe, H.; De Baets, B.; et al. (2016) Stochastic simulation of precipitation-consistent daily reference evapotranspiration using vine copulas. J. Stochastic Environmental Research and Risk Assessment., 30(8): 2197-2214. [CrossRef] [Google Scholar]
  56. Edited by Nanjing Institute of geography and lakes, Chinese Academy of Sciences China Lake survey report. M. Beijing: Science Press, 2019. [Google Scholar]
  57. Gowing, J. w.; Ejieji, C. J. (2001) Real-time scheduling of supplemental irrigation for potatoes using a decision model and short-term weather forecasts . J. Agricultural Water Management., 47: 137-153. [CrossRef] [Google Scholar]
  58. Kjersti, A.; Claudia, C.; Arnoldo, F.; et al. (2007) Paircopula constructions of multiple dependence. J. Insurance Mathematics and Economics., 44(2): 182-198. [Google Scholar]
  59. Shan, B. Y.; Guo, S. S.; Wang, Y. Z.; et al. (2021) Vine copula and cloud model-based programming approach for agricultural water allocation under uncertainty. J. Stochastic Environmental Research and Risk Assessment., : 1-21. [Google Scholar]
  60. Su, J. G.; Wang, F.; Gao, R.; et al. (2020) Water resources allocation in Erhai irrigated district considering catchment water-cycle health. A. IOP Conference Series: Earth and Environmental Science., 508(1), 01215. [Google Scholar]
  61. Deng, W.; Chen, J.; Gu, S. X.; et al. (2020) Hydrological Variability of Water Level of Dianchi Lake and Its Application. A. IOP Conference Series: Materials Science and Engineering., 780(1), 062048. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.