The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Digital Archiving Policy
  • Promote your Publication
  • Metadata Harvesting (OAI2)

IJACSA

  • About the Journal
  • Call for Papers
  • Editorial Board
  • Author Guidelines
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Fees/ APC
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • Guest Editors
  • SUSAI-EE 2025
  • ICONS-BA 2025
  • IoT-BLOCK 2025

Future of Information and Communication Conference (FICC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Editorial Board
  • Guidelines
  • Submit
  • Current Issue
  • Archives
  • Indexing
  • Fees
  • Reviewers
  • Subscribe

DOI: 10.14569/IJACSA.2020.0110883
PDF

Deep Learning Approach for Forecasting Water Quality in IoT Systems

Author 1: Nguyen Thai-Nghe
Author 2: Nguyen Thanh-Hai
Author 3: Nguyen Chi Ngon

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 8, 2020.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: Global climate change and water pollution effects have caused many problems to the farmers in fish/shrimp raising, for example, the shrimps/fishes had early died before harvest. How to monitor and manage quality of the water to help the farmers tackling this problem is very necessary. Water quality monitoring is important when developing IoT systems, especially for aquaculture and fisheries. By monitoring the real-time sensor data indicators (such as indicators of salinity, temperature, pH, and dissolved oxygen - DO) and forecasting them to get early warning, we can manage the quality of the water, thus collecting both quality and quantity in shrimp/fish raising. In this work, we introduce an architecture with a forecasting model for the IoT systems to monitor water quality in aquaculture and fisheries. Since these indicators are collected every day, they becomes sequential/time series data, we propose to use deep learning with Long-Short Term Memory (LSTM) algorithm for forecasting these indicators. Experimental results on several data sets show that the proposed approach works well and can be applied for the real systems.

Keywords: Forecasting model; deep learning; Long-Short Term Memory (LSTM); water quality indicators

Nguyen Thai-Nghe, Nguyen Thanh-Hai and Nguyen Chi Ngon, “Deep Learning Approach for Forecasting Water Quality in IoT Systems” International Journal of Advanced Computer Science and Applications(IJACSA), 11(8), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110883

@article{Thai-Nghe2020,
title = {Deep Learning Approach for Forecasting Water Quality in IoT Systems},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110883},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110883},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {8},
author = {Nguyen Thai-Nghe and Nguyen Thanh-Hai and Nguyen Chi Ngon}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

IJACSA

Upcoming Conferences

IntelliSys 2025

28-29 August 2025

  • Amsterdam, The Netherlands

Future Technologies Conference 2025

6-7 November 2025

  • Munich, Germany

Healthcare Conference 2026

21-22 May 2026

  • Amsterdam, The Netherlands

Computing Conference 2026

9-10 July 2026

  • London, United Kingdom

IntelliSys 2026

3-4 September 2026

  • Amsterdam, The Netherlands

Computer Vision Conference 2026

15-16 October 2026

  • Berlin, Germany
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference
  • Communication Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

© The Science and Information (SAI) Organization Limited. All rights reserved. Registered in England and Wales. Company Number 8933205. thesai.org