Next Article in Journal
Predicting the Specific Energy Consumption of Reverse Osmosis Desalination
Previous Article in Journal
Generation of a Design Flood-Event Scenario for a Mountain River with Intense Sediment Transport
Article Menu

Export Article

Open AccessArticle
Water 2016, 8(12), 599; doi:10.3390/w8120599

Application of a Classifier Based on Data Mining Techniques in Water Supply Operation

1,2
,
2,* , 2
and
2
1
Key Laboratory of Beijing for Water Quality Science and Water Environment Recovery Engineering, College of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, China
2
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
*
Author to whom correspondence should be addressed.
Academic Editors: David K. Kreamer and Andreas N. Angelakis
Received: 30 September 2016 / Revised: 28 November 2016 / Accepted: 12 December 2016 / Published: 16 December 2016
View Full-Text   |   Download PDF [2514 KB, uploaded 16 December 2016]   |  

Abstract

Data mining technology is applied to extract the water supply operation rules in this study. Five characteristic attributes—reservoir storage water, operation period number, water demand, runoff, and hydrological year—are chosen as the dataset, and these characteristic attributes are applied to build a mapping relation with the optimal operation mode calculated by dynamic programming (DP). A Levenberg-Marquardt (LM) neural network and a classification and regression tree (CART) are chosen as data mining algorithms to build the LM neural network classifier and CART decision tree classifier, respectively. In order to verify the classification effect of the LM and CART, the two classifiers are applied to the operation mode recognition for the Heiquan reservoir, which is located in the Qinghai Province of China. The accuracies of the two classifiers are 73.6% and 86.9% for the training sample, and their accuracies are 65.8% and 83.3%, respectively, for the test sample, which indicates that the classification result of the CART classifier is better than that of the LM neural network classifier. Thus, the CART classifier is chosen to guide the long-series water supply operation. Compared to the operation result with the other operation scheme, the result shows that the water deficit index of the CART is mostly closest to the DP scheme, which indicates that the CART classifier can guide reservoir water supply operation effectively. View Full-Text
Keywords: data mining; LM neural network; CART decision tree; water supply operation data mining; LM neural network; CART decision tree; water supply operation
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Ji, Y.; Lei, X.; Cai, S.; Wang, X. Application of a Classifier Based on Data Mining Techniques in Water Supply Operation. Water 2016, 8, 599.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Water EISSN 2073-4441 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top