S&M Young Researcher Paper Award 2020
Recipients: Ding Jiao, Zao Ni, Jiachou Wang, and Xinxin Li [Winner's comments]
Paper: High Fill Factor Array of Piezoelectric Micromachined
Ultrasonic Transducers with Large Quality Factor

S&M Young Researcher Paper Award 2021
Award Criteria
Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

Instructions to authors
English    日本語

Instructions for manuscript preparation
English    日本語

Template
English

Publisher
 MYU K.K.
 Sensors and Materials
 1-23-3-303 Sendagi,
 Bunkyo-ku, Tokyo 113-0022, Japan
 Tel: 81-3-3827-8549
 Fax: 81-3-3827-8547

MYU Research, a scientific publisher, seeks a native English-speaking proofreader with a scientific background. B.Sc. or higher degree is desirable. In-office position; work hours negotiable. Call 03-3827-8549 for further information.


MYU Research

(proofreading and recording)


MYU K.K.
(translation service)


The Art of Writing Scientific Papers

(How to write scientific papers)
(Japanese Only)

Sensors and Materials, Volume 32, Number 11(4) (2020)
Copyright(C) MYU K.K.
pp. 3879-3892
S&M2382 Review Paper of Special Issue
https://doi.org/10.18494/SAM.2020.2953
Published in advance: October 14, 2020
Published: November 30, 2020

Comprehensive Review on Application of Machine Learning Algorithms for Water Quality Parameter Estimation Using Remote Sensing Data [PDF]

Nimisha Wagle, Tri Dev Acharya, and Dong Ha Lee

(Received May 29, 2020; Accepted October 1, 2020)

Keywords: remote sensing, water quality parameters, machine learning, estimation, review

Water is an integral aspect of the world necessary for living creatures to thrive. Owing to unplanned urbanization, rapid industrialization, and uncontrollable human intervention, water quality is gradually degrading. This affects not only marine animals but also humans. Thus, the quality of water should be examined regularly. Water quality parameters should be estimated to monitor water quality. In general, water quality parameters are measured by in situ measurements. Although these measurements are accurate, they are costly and do not provide real-time spatial and temporal changes in water quality. To overcome this limitation, water quality parameters can be estimated using machine learning (ML) along with remote sensing (RS) data. A combination of ML and RS data is a powerful approach for the routine assessment of spatial and temporal variations in water quality parameters. In this paper, some articles based on this approach are reviewed. By analyzing the literature, it was found that the integrated use of RS-based geospatial data with ML helps to produce an accurate result. Most of the authors used the regression algorithm in the estimation of the water quality parameters, with a support vector machine (SVM) regression intensively used. The artificial neural network (ANN) algorithm was the most used algorithm of ML in many of the studies. The researchers used multispectral images for their study. By applying ML to RS data, water quality monitoring systems are evolving into real-time artificial intelligence (AI)-enabled models that provide valuable recommendations and insights to support farmers to make decisions and take action in aquaculture.

Corresponding author: Dong Ha Lee


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Cite this article
Nimisha Wagle, Tri Dev Acharya, and Dong Ha Lee, Comprehensive Review on Application of Machine Learning Algorithms for Water Quality Parameter Estimation Using Remote Sensing Data, Sens. Mater., Vol. 32, No. 11, 2020, p. 3879-3892.



Forthcoming Regular Issues


Forthcoming Special Issues

Special Issue on Advanced Materials and Sensing Technologies on IoT Applications: Part 1(1)
Guest editor, Teen-Hang Meen (National Formosa University), Wenbing Zhao (Cleveland State University), and Cheng-Fu Yang (National University of Kaohsiung)
Call for paper


Special Issue on the International Multi-Conference on Engineering and Technology Innovation 2020 (IMETI2020)
Guest editor, Wen-Hsiang Hsieh (National Formosa University)
Conference website


Special Issue on Advanced Materials and Sensing Technologies on IoT Applications: Part 1(2)
Guest editor, Teen-Hang Meen (National Formosa University), Wenbing Zhao (Cleveland State University), and Cheng-Fu Yang (National University of Kaohsiung)
Call for paper


Special Issue on Artificial Intelligence and Advanced Technologies for Power and Renewable Energy Systems Mainly from IS3C2020
Guest editor, Shiue Der Lu , Meng Hui Wang, Kuei Hsiang Chao, and Her Terng Yau (National Chin-Yi University of Technology)
Conference website
Call for paper


Special Issue on Human-in-the-loop Sensing in Cognitive Robotic Systems
Guest editor, Weiwei Wan (Osaka University), Yiming Jiang (Hunan University), and Daolin Ma (MIT)
Call for paper


Special Issue on Sensing and Data Analysis Technologies for Living Environment, Health Care, Production Management and Engineering/Science Education Applications: Part 2
Guest editor, Chien-Jung Huang (National University of Kaohsiung), Rey-Chue Hwang (I-Shou University), Ja-Hao Chen (Feng Chia University), Ba-Son Nguyen (Research Center for Applied Sciences)
Call for paper


Copyright(C) MYU K.K. All Rights Reserved.