Open Access
Issue |
MATEC Web Conf.
Volume 295, 2019
Smart Underground Space and Infrastructures – Lille 2019
|
|
---|---|---|
Article Number | 02002 | |
Number of page(s) | 5 | |
Section | Resilient Infrastructures | |
DOI | https://doi.org/10.1051/matecconf/201929502002 | |
Published online | 18 October 2019 |
- Y. Abou Rjeily, O. Abbas, M. Sadek, I. Shahrour and F. Hage Chehade (2017) Flood forecasting within urban drainage systems using NARX neural network , Water Science & Technology [Google Scholar]
- Bhattacharya, B., Price, R. K., and Solomatine, D. P. (2007). “Machine learning approach to modeling sediment transport.” J. Hydraul. Eng., 133(4), 440–450 [CrossRef] [Google Scholar]
- Beeneken, T., Erbe, V., Messmer, A., Reder, C., Rohlfing, R.,Scheer, M., Schuetze, M., Schumacher, B., Weilandt, M. & Weyand, M. (2014). Real time control (RTC) of urban drainage systems – A discussion of the additional efforts compared to conventionally operated systems. UrbanWater Journal 10 [Google Scholar]
- Berkhahn S, Fuchs, L., Neuweiler I. An ensemble neural network model for real-time prediction of urban floods.(2019) Journal of hydrology Volume 575,Volume 575, Pages 743-754 [CrossRef] [Google Scholar]
- Goyal MK, Ojha CSP (2014) Evaluation of rule and decision tree induction algorithms for generating climate change scenarios for temperature and pan evaporation on a Lake Basin. ASCE J Hydrol Eng 10.1061/(ASCE)HE. 1943–5584.0000615 [Google Scholar]
- Singh G, Sachdeva SN, Pal M (2016) M5 model tree based predictive modeling of road accidents on non-urban sections of highways in India. Accid Anal Prev 96:108–117 [CrossRef] [Google Scholar]
- Haykin, S. _1999_. Neural networks: A comprehensive foundation,Prentice-Hall, Englewood Cliffs, N.J. [Google Scholar]
- Pal M, Deswal S (2009) M5 model tree based modelling of reference evapotranspiration. Hydrol Process 23:1437–1443 [CrossRef] [Google Scholar]
- Pang, X.; Zhou, Y.; Wang, P.; Lin, W.; Chang, V. (2018). An innovative neural network approach for stock market prediction. J. Supercomput, 1–21. [Google Scholar]
- Quinlan, J.R., (1992). Learning with continuous classes. In: Proceedings of ustralianJoint Conference on Artificial Intelligence, World Scientific Press: Singapore,pp. 343–348. [Google Scholar]
- Rahimikhoob, A. (2014). Comparison between M5 model tree and neural networks for estimating reference evapotranspiration in an arid environment. Water Resour. Manage. 28, 657–669. [CrossRef] [Google Scholar]
- Solomatine, D. P. & Dulal, K. N. (2003) Model trees as an alternative to neural networks in rainfall–runoff modelling. Hydrological Sciences Journal. 48(3), 399–411. [CrossRef] [Google Scholar]
- Solomatine DP, Xue Y (2004) M5 model trees compared to neural networks: application to flood forecasting in the upper reach of the Huai River in China. J Hydr Engrg 9(6):491–501 [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.