Open Access
MATEC Web of Conferences
Volume 25, 2015
2015 International Conference on Energy, Materials and Manufacturing Engineering (EMME 2015)
Article Number 01002
Number of page(s) 7
Section Energy Engineering
Published online 06 October 2015
  1. Jing Ding & Yuren Deng. 1988. Stochastic Hydrology. Chengdu: Press of Chengdu University of Science and Technology.
  2. Biao Jin, Beiping Wu & Yanfang Li. 2009. Application of curve fitting and auto-regression model in under-ground deformation monitoring. Mine Mapping 25 (1): 35–37, 44.
  3. Liuzhu Zhang, Guangbai Cui & Changjian Liu. 2005. Discussion of the least square regression related to hydrology. Hydrology 25 (4): 1–5.
  4. Qingguo Li & Shouyu Chen. 2005. Regression prediction method of support vector machine based on fuzzy pattern recognition. Advances in Water Science 16 (5): 741–746.
  5. Jun Huang, Pute Wu & Xining Zhao. 2011. Soil infiltration research based on neural network and grey correlation analysis. Soil Journal 48 (6): 1282–1286.
  6. Wang, Z.K., Wu, P.T. & Zhao, X.N., et al. 2013. GANN models for reference evapotranspiration estimation developed with weather data from different climatic regions. Theoretical and Applied Climatology 116(3-4): 481–489. [CrossRef]
  7. Tiesong Hu, Peng Yuan & Jing Ding. 1995. Application of artificial neural networks in hydrology and water resources. Advances in Water Science 6 (1): 76–82.
  8. Guodong Liu & Jing Ding. 1999. Discussion of several problems of BP network used for hydrologic prediction. Journal of Hydraulic Engineering (1): 66–71.
  9. Cunjun Li, Hongxia Deng, Bing Zhu, Wensheng Wang. 2007. Adaptability analysis of daily runoff series data for BP neural network prediction. Journal of Sichuan University (Engineering Science) 39 (2): 25–29.
  10. Baoming Jin. 2010. Application of BP neural network in flow prediction in Shili Temple of Minjiang. Hydropower Energy Science 28 (9): 12–14.
  11. Dongwen Cui. 2013. Application of multi-hidden layer BP neural network model in runoff prediction. Hydrology 33 (1): 68–73.
  12. Deng, J.L. 1982. Control problems of grey systems. Systems & Control Letters 1(5): 288–294. [CrossRef]
  13. Yi Liu & Ping Zhang. 1992. Discussion of grey topological prediction method and its application in hydrologic prediction. Yangtze River 23 (1): 19–27.
  14. Wenming Wang, Wenke Wang & Dong Du. 2007. Application of Grey Prediction Model GM (1.1) in Hydrologic Prediction – taking Manas River as an example. Groundwater 29 (2): 10–12.
  15. Nash, J.E. & Sutcliffe, G.J. 1970. River flow prediction through conceptual models, I. A discussion of principles. Journal of Hydrology 10: 282–290. [CrossRef]
  16. Xueyan Wang. 2011. Analysis of variation of hydrologic features at Boluo Hydrologic Station in the Dongjiang River. Guangdong Water Resources and Hydropower (1): 61–64.
  17. Ju, Q., Yu, Z. & Hao, Z., et al. 2009. Division-based rainfall-runoff simulations with BP neural networks and Xinanjiang model. Neurocomputing 72(13): 2873–2883. [CrossRef]
  18. Abdi, H., Valentin, D. & Edelman, B., et al. 1996. A Widrow–Hoff Learning Rule for a Generalization of the Linear Auto-associator. Journal of Mathematical Psychology 40(2): 175–182. [CrossRef]
  19. El-Din, A.G. & Smith, D.W. 2002. A neural network model to predict the wastewater inflow incorporating rainfall events. Water Research 36(5): 1115–1126. [CrossRef]
  20. Yesilnacar, M.I., Sahinkaya, E. & Naz, M., et al. 2007. Neural network prediction of nitrate in groundwater of Harran Plain, Turkey. Environmental Geology 56(1): 19–25. [CrossRef]
  21. Raman, H. & Sunilkumar, N. 1995. Multivariate modeling of water resources time series using artificial neural networks. Hydrologic Sciences Journal 40(2): 145–163. [CrossRef]

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.