Συζήτηση του άρθρου "Γενικευμένα νευρωνικά δίκτυα παλινδρόμησης για τη μοντελοποίηση της εξατμοδιαπνοής"

D. Koutsoyiannis, Discussion of "Generalized regression neural networks for evapotranspiration modelling", Hydrological Sciences Journal, 52 (4), 832–835, 2007.

[Συζήτηση του άρθρου "Γενικευμένα νευρωνικά δίκτυα παλινδρόμησης για τη μοντελοποίηση της εξατμοδιαπνοής"]

[doc_id=788]

[Αγγλικά]

Βλέπε επίσης: http://dx.doi.org/10.1623/hysj.52.4.832

Άλλες εργασίες που αναφέρονται σ' αυτή την εργασία: Δείτε τις στο Google Scholar ή στο ResearchGate

Άλλες εργασίες που αναφέρονται σ' αυτή την εργασία (αυτός ο κατάλογος μπορεί να μην είναι ενημερωμένος):

1.	Kisi, O, Reply to discussion of "Generalized regression neural networks for evapotranspiration modelling" by D. Koutsoyiannis, Hydrological Sciences Journal, 52(4), 836-839, 2007.
2.	Aksoy, H., A. Guven, A. Aytek, M.I. Yuce and N. E. Unal, Comment on 'Kisi O. 2007. Evapotranspiration modelling from climatic data using a neural computing technique', Hydrological Processes, 22(14), 2715–2717, 2008.
3.	#Wang, Y.-M., S. Traore and Kerh, Monitoring event-based suspended sediment concentration by artificial neural network models WSEAS Transactions on Computers, 7(5), 559-568, 2008.
4.	Guven, A., A. Aytek, M.I. Yuce and H. Aksoy, Genetic Programming-Based Empirical Model for Daily Reference Evapotranspiration Estimation, Clean - Soil, Air, Water, 36 (10 – 11), 36 (10-11), 905 - 912, 2008.
5.	Aytek, A., A. Guven, M.I. Yuce and H. Aksoy, An explicit neural network formulation for evapotranspiration, Hydrological Sciences Journal, 53 (4), 893-904, 2008.
6.	#Abrahart, R.J., L.M. See and C.W. Dawson, Neural network hydroinformatics: maintaining scientific rigour, Practical hydroinformatics , ed. by R.J. Abrahart, L. M. See, and D. P. Solomatine, 33-47, Springer, DOI: 10.1007/978-3-540-79881-1_3, 2008.
7.	Dahamsheh, A., and H. Aksoy, Artificial neural network models for forecasting intermittent monthly precipitation in arid regions, Meteorological Applications, 16 (3), 325-337, 2009.
8.	Aytek, A., A. Guven, M.I. Yuce and H. Aksoy, Reply to Discussion of "An explicit neural network formulation for evapotranspiration", Hydrological Sciences Journal, 54 (2), 389-393, 2009.
9.	Aksoy, H., and A. Dahamsheh, Artificial neural network models for forecasting monthly precipitation in Jordan, Stochastic Environmental Research and Risk Assessment, 23 (7), 917-931, 2009.
10.	Remesan, R., M. A. Shamim, D. Han, and J. Mathew, Runoff prediction using an integrated hybrid modelling scheme, Journal of Hydrology, 372(1-4), 48-60, 2009.
11.	Wang, Y.-M., and S. Traore, Time-lagged recurrent network for forecasting episodic event suspended sediment load in typhoon prone area, International Journal of Physical Sciences, 4 (9), 519-528, 2009.
12.	Kişi, O., and M. Çimen, Evapotranspiration modelling using support vector machines, Hydrological Sciences Journal, 54 (5), 918-928, 2009.
13.	#Wang, Y.-M., S. Traore and W.-G.Chung, Time-lagged recurrent network for forecasting river suspended sediment load in Southern Taiwan, Proceedings of the IASTED International Conference on Environmental Management and Engineering, EME 2009, 103-110, 2009.
14.	Moghaddamnia,A., J. Piri, S. Amin and D. Han, Closure to “Daily Pan Evaporation Modeling in a Hot and Dry Climate” by J. Piri, S. Amin, A. Moghaddamnia, A. Keshavarz, D. Han, and R. Remesan, J. Hydrologic Engrg, 15 (8), 668-669, 2010.
15.	Tiwari, M. K., and C. Chatterjee, Development of an accurate and reliable hourly flood forecasting model using Wavelet-Bootstrap-ANN (WBANN) hybrid approach, Journal of Hydrology, 394 (3-4), 458-470, 2010.
16.	Abrahart, R. J., C. W. Dawson, L. M. See, N. J. Mount and A. Y. Shamseldin, Discussion of “Evapotranspiration modelling using support vector machines”, Hydrological Sciences Journal, 55 (8), 1442-1450, 2010.
17.	Wang, Q., and Z. Yang, Seasonal environmental-flow demand calculation of reed community (phragmites australis var. baiyangdiasis) under different meteorological conditions in Baiyangdian Lake, China, Procedia Environmental Sciences, 2, 1857-1864, DOI: 10.1016/j.proenv.2010.10.197, 2010.
18.	#Sivakumar, B., and R. Berndtsson, Setting the stage, ch. 1 in Advances in Data-Based Approaches for Hydrologic Modeling and Forecasting, 1-16, World Scientific, 2010.
19.	Tiwari, M. K., and C. Chatterjee, A new wavelet-bootstrap-ANN hybrid model for daily discharge forecasting, Journal of Hydroinformatics, 13 (3), 500-519, 2011.
20.	Abrahart, R. J., F. Anctil, P. Coulibaly, C. W. Dawson, N. J. Mount, L. M. See, A. Y. Shamseldin, D. P. Solomatine, E. Toth and R. L. Wilby, Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting, Progress in Physical Geography, 36 (4), 480-513, 2012.
21.	Hormozi, H. A., N. Zohrabi, S. B. Nasab, F. Azimi and A. B. Hafshejani, Evaluation of effective parameters in the estimation of evaporation using artificial neural network model, International Journal of Agriculture and Crop Sciences, 4 (8), 461-467, 2012.
22.	Shabani, Μ., and N. Shabani, Estimation of daily suspended sediment yield using artificial neural network and sediment rating curve in Kharestan Watershed, Iran, Australian Journal of Basic and Applied Sciences, 6 (12), 157-164, 2012.
23.	Liu, Q.-J., Z.-H. Shi, N.-F. Fang, H.-D. Zhu and L. Ai, Modeling the daily suspended sediment concentration in a hyperconcentrated river on the Loess Plateau, China, using the Wavelet–ANN approach, Geomorphology, 186, 181–190, 2013.
24.	Sehgal, V., M. K. Tiwari and C. Chatterjee, Wavelet bootstrap multiple linear regression based hybrid modeling for daily river discharge forecasting, Water Resources Management, 10.1007/s11269-014-0638-7, 2014.
25.	Mishra, S., P. Gupta, S. K. Pandey and J. P. Shukla, An efficient approach of artificial neural network in runoff forecasting, International Journal of Computer Applications, 92 (5) 9-15, 2014.
26.	#Abd Elfattah, M., N. El-Bendary, M. A. Abu Elsoud, A. E. Hassanien and M. F. Tolba, An intelligent approach for galaxies images classification, 13th International Conference on Hybrid Intelligent Systems, HIS 2013, art. no. 6920476, 167-172, 2014.
27.	#Remesan, R., and J. Mathew, Machine learning and artificial intelligence-based approaches, Hydrological Data Driven Modelling – Earth Systems Data and Models, Springer International Publishing, 71-110, 10.1007/978-3-319-09235-5_4, 2015.

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