Next Article in Journal
Implementing the Prepaid Smart Meter System for Irrigated Groundwater Production in Northern China: Status and Problems
Next Article in Special Issue
SWE-SPHysics Simulation of Dam Break Flows at South-Gate Gorges Reservoir
Previous Article in Journal
Effects of Dredging and Lanthanum-Modified Clay on Water Quality Variables in an Enclosure Study in a Hypertrophic Pond
Previous Article in Special Issue
A Stochastic Multi-Objective Chance-Constrained Programming Model for Water Supply Management in Xiaoqing River Watershed
Article Menu
Issue 6 (June) cover image

Export Article

Open AccessFeature PaperArticle
Water 2017, 9(6), 381; doi:10.3390/w9060381

Optimising Fuzzy Neural Network Architecture for Dissolved Oxygen Prediction and Risk Analysis

1
Department of Civil Engineering, Lassonde School of Engineering, York University, 4700 Keele St, Toronto, ON M3J 1P3, Canada
2
Department of Mechanical Engineering, University of Victoria, PO Box 1700 STN CSC, Victoria, BC V8P 5C2, Canada
*
Author to whom correspondence should be addressed.
Academic Editors: Yurui Fan and Jiangyong Hu
Received: 30 March 2017 / Revised: 17 May 2017 / Accepted: 24 May 2017 / Published: 28 May 2017
(This article belongs to the Special Issue Modeling of Water Systems)
View Full-Text   |   Download PDF [2958 KB, uploaded 28 May 2017]   |  

Abstract

A fuzzy neural network method is proposed to predict minimum daily dissolved oxygen concentration in the Bow River, in Calgary, Canada. Owing to the highly complex and uncertain physical system, a data-driven and fuzzy number based approach is preferred over traditional approaches. The inputs to the model are abiotic factors, namely water temperature and flow rate. An approach to select the optimum architecture of the neural network is proposed. The total uncertainty of the system is captured in the fuzzy numbers weights and biases of the neural network. Model predictions are compared to the traditional, non-fuzzy approach, which shows that the proposed method captures more low DO events. Model output is then used to quantify the risk of low DO for different conditions. View Full-Text
Keywords: dissolved oxygen; water quality; artificial neural networks; fuzzy numbers; risk analysis; uncertainty dissolved oxygen; water quality; artificial neural networks; fuzzy numbers; risk analysis; uncertainty
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

Khan, U.T.; Valeo, C. Optimising Fuzzy Neural Network Architecture for Dissolved Oxygen Prediction and Risk Analysis. Water 2017, 9, 381.

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