Open Access
E3S Web of Conf.
Volume 381, 2023
International Scientific and Practical Conference “Development and Modern Problems of Aquaculture” (AQUACULTURE 2022)
Article Number 01036
Number of page(s) 9
Section Agriculture, River Ecosystems and Environment
Published online 14 April 2023
  1. O. Boulariah, P. A. Mikhailov, A. Longobardi, A. N. Elizariev, S. G. Aksenov, Journal of Groundwater Science and Engineering 9(2), 161-170 (2021) DOI: 10.19637 /j.cnki.2305-7068.2021.02.008 [Google Scholar]
  2. A. Longobardi, G. Fattoruso, G. Guarnieri, A. Di Pietro, L. La Porta, A. Tofani, M. Pollino, Water distribution network perspective in RAFAEL Project, a system for critical infrastructure risk analysis and forecast, in Computational Science and Its Applications–ICCSA 2021: 21st International Conference, 13–16 September, Cagliari, Italy (2021) [Google Scholar]
  3. Y. Chen et al, Aerosol lidar intercomparison in the framework of the MEMO project. 1. Lidar self calibration and 1st comparison observation calibration based on statistical analysis method, in International Conf. on Meteorology Observations (ICMO), pp. 1-5 (2019) [Google Scholar]
  4. M. Moishin, R. C. Deo, R. Prasad, N. Raj, S. Abdulla, IEEE 9, 50982-50993 (2021) [Google Scholar]
  5. H. Mincong, X. Jiancang, C. Yang, W. Ni, Z. Yongjin, Application of middleware technique in Web of flood forecasting system with multiple models, in 2006 International Conference on Hybrid Information Technology, pp. 505-508 (2006) [Google Scholar]
  6. S. Singhal, L.V. Real, T. George, S. Aneja, Y. Sabharwal, A hybrid parallelization approach for high resolution operational flood forecasting, in 20th Annual International Conference on High Performance Computing, pp. 405-414 (2013) [Google Scholar]
  7. Z. Wang, C. Li, River ice forecasting based on genetic neural network, in 2009 International Conf. on Information Engineering and Computer Science, pp. 1-4 (2009) [Google Scholar]
  8. С. Svensson, Hydrological Sciences Journal 61(1), 19-35 (2016) [CrossRef] [Google Scholar]
  9. B. Zhou, P. Zhai, Weather and Forecasting 31(4), 1325-1341 (2016) [CrossRef] [Google Scholar]
  10. S. Banik, F. H. Chanchary, K. Khan, R. A. Rouf, M. Anwer, Neural network and genetic algorithm approaches for forecasting bangladeshi monsoon rainfall, in 2008 11th International Conference on Computer and Information Technology, pp. 735-740 (2008) [Google Scholar]
  11. H. Ding, X. Li, W. Liao, A Prediction Model Base on Evolving Neural Network Using Genetic Algorithm Coupled with Simulated Annealing for Water-level, in 2012 Fifth International Joint Conference on Computational Sciences and Optimization, pp. 896-899 (2012) [Google Scholar]
  12. N. Krasnogorskaya, E. Belozerova, A. Longobardi, E Nafikova, International Journal of Hydrology Science and Technologythis link is disabled 9(6) 603–626 (2019)DOI:10.1504/IJHST.2019.103440 [CrossRef] [Google Scholar]
  13. E. Nasyrova, A. Elizaryev, S. Aksenov, Y. Baiduk, E. Kamaeva, R. Akhtyamov, E3S Web of Conferences EDP Sciences 110 (2019) [CrossRef] [EDP Sciences] [PubMed] [Google Scholar]

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