Data Mining Algorithms for Water Main Condition Prediction—Comparative Analysis-Reference-Cited by-同舟云学术

Data Mining Algorithms for Water Main Condition Prediction—Comparative Analysis

Published:2022-02 Issue:2 Volume:148 Page:
ISSN:0733-9496
Container-title:Journal of Water Resources Planning and Management
language:en
Short-container-title:J. Water Resour. Plann. Manage.

Author:

Assad Ahmed¹^ORCID,Bouferguene Ahmed²

Affiliation:

1. Postdoctoral Fellow, Dept. of Building, Civil and Environmental Engineering, Univ. of Alberta, Edmonton, AB, Canada T6G 1H9 (corresponding author). ORCID: .

2. Professor, Dept. of Building, Civil and Environmental Engineering, Univ. of Alberta, Edmonton, AB, Canada T6G 1H9.

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.1943-5452.0001512

Reference71 articles.

1. Intelligent Approaches for Predicting Failure of Water Mains

2. ASCE. 2021. “2021 report card for America’s infrastructure.” Accessed May 1 2021. https://infrastructurereportcard.org/cat-item/drinking-water/.

3. A New Metric for Assessing Resilience of Water Distribution Networks

4. Resilience-Driven Multiobjective Restoration Planning for Water Distribution Networks

5. Resilience-Driven Sustainability-Based Rehabilitation Planning for Water Distribution Networks

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