Identifying Prominent Explanatory Variables for Water Demand Prediction Using Artificial Neural Networks: A Case Study of Bangkok-Reference-Cited by-同舟云学术

Identifying Prominent Explanatory Variables for Water Demand Prediction Using Artificial Neural Networks: A Case Study of Bangkok

Published:2010-12-30 Issue:6 Volume:25 Page:1653-1676
ISSN:0920-4741
Container-title:Water Resources Management
language:en
Short-container-title:Water Resour Manage

Author:

Babel Mukand Singh,Shinde Victor R.

Publisher

Springer Science and Business Media LLC

Subject

Water Science and Technology,Civil and Structural Engineering

Link

http://link.springer.com/content/pdf/10.1007/s11269-010-9766-x.pdf

Reference21 articles.

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2. ADB and MWA (2009) ABB drives in urban water cycle (information brochure). Available at http://www05.abb.com/global/scot/scot216.nsf/veritydisplay/5325e072f09642a6c12571e800305fa5/$File/MWA_urban_water_cycle_low.pdf

3. ASCE Task Committee (2000) Artificial neural networks in hydrology I: preliminary concepts. J Hydrol Eng 5(2):115–123

4. Babel MS, Gupta AD, Pradhan P (2007) A multivariate econometric approach for domestic water demand modeling: an application to Kathmandu, Nepal. Water Resour Manag 21:573–589

5. Bougadis J, Adomowski K, Diduch R (2005) Short term municipal water demand forecasting. J Hydrol Eng 19(1):137–148

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