Hybrid Fuzzy Regression–Artificial Neural Network for Improvement of Short-Term Water Consumption Estimation and Forecasting in Uncertain and Complex Environments: Case of a Large Metropolitan City-Reference-Cited by-同舟云学术

Hybrid Fuzzy Regression–Artificial Neural Network for Improvement of Short-Term Water Consumption Estimation and Forecasting in Uncertain and Complex Environments: Case of a Large Metropolitan City

Published:2012-01 Issue:1 Volume:138 Page:71-75
ISSN:0733-9496
Container-title:Journal of Water Resources Planning and Management
language:en
Short-container-title:J. Water Resour. Plann. Manage.

Author:

Azadeh A.¹²³,Neshat N.¹²³,Hamidipour H.¹²³

Affiliation:

1. Dept. of Industrial Engineering and Center of Excellence for Intelligent Based Mechanical Experiments, College of Engineering, Univ. of Tehran, P.O. Box 11365-4563, Iran (corresponding author).

2. Dept. of Industrial Engineering, Sharif Univ. of Technology, Tehran, Iran.

3. Dept. of Industrial Engineering and Center of Excellence for Intelligent Based Mechanical Experiments, College of Engineering, Univ. of Tehran, P.O. Box 11365-4563, Iran.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Management, Monitoring, Policy and Law,Water Science and Technology,Geography, Planning and Development,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29WR.1943-5452.0000152

Reference26 articles.

1. Water Consumption Prediction of Istanbul City by Using Fuzzy Logic Approach

2. Applying knowledge discovery to predict water-supply consumption

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4. A simulated-based neural network algorithm for forecasting electrical energy consumption in Iran

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