E3S Web Conf.
Volume 194, 20202020 5th International Conference on Advances in Energy and Environment Research (ICAEER 2020)
|Number of page(s)||9|
|Section||Environmental Engineering, Ecological Environment and Urban Construction|
|Published online||15 October 2020|
- Budge T. Sponge cities and small towns: a new economic partnership // Rogers M.F., Jones R, eds. The Changing Nature of Australia’s Country Town. Ballarat, Australia: Victorian Universities Regional Research Network Press, 2006. [Google Scholar]
- YU Kongjian, LI Dihua, YUAN Hong, et al. “Sponge City”: Theory and practice[J]. City Planning Review, 2015, 39(06):26-36.(in Chinses) [Google Scholar]
- Jun X, Yongyong Z, Lihua X, et al. Opportunities and challenges of the Sponge City construction related to urban water issues in China[J]. Science China(Earth Sciences), 2017, 60(04):652-658. [Google Scholar]
- ZHAO Jin-hui, ZHAO Ya-qian, Xie Xi, et al. Comparison of Field Permeability Test Methods for Permeable Pavement[J]. Chian Water & Wastewater, 2019, 35(05):114-118+124.(in Chinese) [Google Scholar]
- Pan Xiyang. Research on Preparation and Performance of Porous Cement Concrete Pavement Material[D]. Chang’an University, 2010.(in Chinese) [Google Scholar]
- Li X, Xu Q, Chen S. An experimental and numerical study on water permeability of concrete[J]. Construction & Building Materials, 2016, 105:503-510. [CrossRef] [Google Scholar]
- Al-Omari A, Masad E. Three dimensional simulation of fluid flow in X‐ray CT images of porous media[J]. International Journal for Numerical & Analytical Methods in Geomechanics, 2004, 28(13):1327-1360. [Google Scholar]
- X. Kuang, J. Sansalone, G. Ying, et al. Pore-structure models of hydraulic conductivity for permeable pavement[J]. Journal of Hydrology, 2011, 399(3):148-157. [Google Scholar]
- ZHENG Wei-da, ZHANG Hui-ran, HU Hong-qing, et al. Performance prediction of perovskite materials based on different machine learning algorithms[J]. The Chinese Journal of Nonferrous Metals, 2019, 29(04):803-809.(in Chinses) [Google Scholar]
- Tibshirani R. Regression Shrinkage and Selection Via the Lasso[J]. Journal of the Royal Statistical Society, 1996, 58(1):267-288. [Google Scholar]
- B.E. Boser, I.M. Guyon, V.N. Vapnik. A training algorithm for optimal margin classifiers. In Proceedings of the 5th annual ACM Workshop on computation learning theory. ACM Press. 1992, 144-152. [Google Scholar]
- Freund Y, Schapire R, Abe N. A short introduction to boosting[J]. Journal-Japanese Society for Artificial Intelligence, 1999, 14(5: 771-780. [Google Scholar]
- Fan Li, Yiming Yang, Eric P. Xing. From LASSO regression to feature vector machine[C]// Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, NIPS 2005, December 5-8, 2005, Vancouver, British Columbia, Canada]. DBLP, 2005. [Google Scholar]
- Yuan Z, Huang B. Prediction of protein accessible surface areas by support vector regression[J]. Proteins Structure Function & Bioinformatics, 2004, 57(3):558-564. [Google Scholar]
- GONG Yue, LUO Xiao-Qin, WANG Dian-hai, et al. Urban travel time prediction based on gradient boosting regression tress[J]. Journal of Zhejiang University(Engineering Science), 2018, 52(03):453-460.(in Chinses) [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.