Anomaly Detection and Classification in Water Distribution Networks Integrated with Hourly Nodal Water Demand Forecasting Models and Feature Extraction Technique-Reference-Cited by-同舟云学术

Anomaly Detection and Classification in Water Distribution Networks Integrated with Hourly Nodal Water Demand Forecasting Models and Feature Extraction Technique

Published:2022-11 Issue:11 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:

Jian Cai¹^ORCID,Gao Jinliang²,Xu Yongpeng²

Affiliation:

1. Ph.D. Student, School of Environment, Harbin Institute of Technology, Harbin 150090, China (corresponding author). ORCID: .

2. Professor, School of Environment, Harbin Institute of Technology, Harbin 150090, China.

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

https://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29WR.1943-5452.0001616

Reference49 articles.

1. A pressure-based method for monitoring leaks in a pipe distribution system: A Review

2. Urban Residential Water Demand Prediction Based on Artificial Neural Networks and Time Series Models

3. A Leak Zone Location Approach in Water Distribution Networks Combining Data-Driven and Model-Based Methods

4. Chen T. and C. Guestrin. 2016. “XGBoost: A scalable tree boosting system.” In Proc. 22nd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining 785–794. New York: Association for Computing Machinery. https://doi.org/10.1145/2939672.2939785.

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