Comparison of Multivariate Regression and Artificial Neural Networks for Peak Urban Water-Demand Forecasting: Evaluation of Different ANN Learning Algorithms-Reference-Cited by-同舟云学术

Comparison of Multivariate Regression and Artificial Neural Networks for Peak Urban Water-Demand Forecasting: Evaluation of Different ANN Learning Algorithms

Published:2010-10 Issue:10 Volume:15 Page:729-743
ISSN:1084-0699
Container-title:Journal of Hydrologic Engineering
language:en
Short-container-title:J. Hydrol. Eng.

Author:

Adamowski Jan¹²,Karapataki Christina¹²

Affiliation:

1. Assistant Professor, Dept. of Bioresource Engineering, McGill Univ., Macdonald Campus, 21 111 Lakeshore Rd., Ste. Anne de Bellevue QC, Canada H9X 3V9 (corresponding author).

2. Researcher, Dept. of Chemical Engineering, Univ. of Cambridge, Pembroke St., Cambridge CB2 3RA, U.K.

Publisher

American Society of Civil Engineers (ASCE)

Subject

General Environmental Science,Water Science and Technology,Civil and Structural Engineering,Environmental Chemistry

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29HE.1943-5584.0000245

Reference49 articles.

1. Peak Daily Water Demand Forecast Modeling Using Artificial Neural Networks

2. WATER SAVINGS FROM LAWN WATERING RESTRICTIONS DURING A DROUGHT YEAR, FORT COLLINS, COLORADO

3. Neural Networks for Real Time Catchment Flow Modeling and Prediction

4. Artemis C. (2006). “Country report—Cyprus.” Conf. of the Water Directors of the Euro-Mediterranean and Southeastern European Countries Dept. of Water Development Nicosia Cyprus.

Cited by 199 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A new single multiplicative neuron model artificial neural network based on black hole optimization algorithm: forecasting the amounts of clean water given to metropolis;Stochastic Environmental Research and Risk Assessment;2024-08-29

2. Artificial Intelligence for Water Consumption Assessment: State of the Art Review;Water Resources Management;2024-04-25

3. Biomechanical analysis of real‐time vibration exposure during mini combine harvester operation: A hybrid ANN–GA approach;Journal of Field Robotics;2024-03-28

4. A multi‐level approach to predict the seismic response of rigid rocking structures using artificial neural networks;Earthquake Engineering & Structural Dynamics;2024-03-11

5. Deep learning–based short-term water demand forecasting in urban areas: A hybrid multichannel model;AQUA — Water Infrastructure, Ecosystems and Society;2024-02-28