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Maschinelles Lernen in der Siedlungswasserwirtschaft

  • Published:2019-05-21 Issue:7-8 Volume:71 Page:359-368
  • ISSN:0945-358X
  • Container-title:Österreichische Wasser- und Abfallwirtschaft
  • language:de
  • Short-container-title:Österr Wasser- und Abfallw

Author:

Sappl Johannes,Harders Matthias,Rauch Wolfgang

Funder

University of Innsbruck and Medical University of Innsbruck

Publisher

Springer Science and Business Media LLC

Subject

Fluid Flow and Transfer Processes,General Energy,Water Science and Technology

Reference39 articles.

1. Adamowski, J., Fung Chan, H., Prasher, S.O., Ozga-Zielinski, B., Sliusarieva, A.: Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada. Water Resources Research 48(1) (2012). https://doi.org/10.1029/2010WR009945 . URL http://doi.wiley.com/10.1029/2010WR009945

2. Alejo, L., Atkinson, J., Guzmán-Fierro, V., Roeckel, M.: Effluent composition prediction of a two-stage anaerobic digestion process: machine learning and stoichiometry techniques. Environmental Science and Pollution Research 25(21), 21149–21163 (2018). https://doi.org/10.1007/s11356-018-2224-7 . URL http://linkspringer.com/10.1007/s11356-018-2224-7

3. Carbajal, J.P., Leitão, J.P., Albert, C., Rieckermann, J.: Appraisal of data-driven and mechanistic emulators of nonlinear simulators: The case of hydrodynamic urban drainage models. Environmental Modelling & Software 92, 17–27 (2017). https://doi.org/10.1016/J.ENVSOFT.2017.02.006 . URL https://www.sciencedirect.com/science/article/pii/S1364815216307964

4. Cheng, J.C.,Wang, M.: Automated detection of sewer pipe defects in closed-circuit television images using deep learning techniques. Automation in Construction 95, 155–171 (2018). https://doi.org/10.1016/j.autcon.2018.08.006 . URL https://www.sciencedirect.com/science/article/pii/S0926580518303273

5. De Clercq, D., Smith, K., Chou, B., Gonzalez, A., Kothapalle, R., Li, C., Dong, X., Liu, S.,Wen, Z.: Identification of urban drinking water supply patterns across 627 cities in China based on supervised and unsupervised statistical learning. Journal of environmental management 223, 658–667 (2018)

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