Literature DB >> 35336587

Towards Synoptic Water Monitoring Systems: A Review of AI Methods for Automating Water Body Detection and Water Quality Monitoring Using Remote Sensing.

Liping Yang1,2,3, Joshua Driscol1,2, Sarigai Sarigai1,2, Qiusheng Wu4, Christopher D Lippitt1,2, Melinda Morgan1.   

Abstract

Water features (e.g., water quantity and water quality) are one of the most important environmental factors essential to improving climate-change resilience. Remote sensing (RS) technologies empowered by artificial intelligence (AI) have become one of the most demanded strategies to automating water information extraction and thus intelligent monitoring. In this article, we provide a systematic review of the literature that incorporates artificial intelligence and computer vision methods in the water resources sector with a focus on intelligent water body extraction and water quality detection and monitoring through remote sensing. Based on this review, the main challenges of leveraging AI and RS for intelligent water information extraction are discussed, and research priorities are identified. An interactive web application designed to allow readers to intuitively and dynamically review the relevant literature was also developed.

Entities:  

Keywords:  artificial intelligence; computer vision; convolutional neural networks; deep learning; machine learning; remote sensing; surface water; surface water extraction; water body detection; water quality monitoring

Mesh:

Year:  2022        PMID: 35336587      PMCID: PMC8949619          DOI: 10.3390/s22062416

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.576


  22 in total

1.  Geovisualization for knowledge construction and decision support.

Authors:  Alan M MacEachren; Mark Gahegan; William Pike; Isaac Brewer; Guoray Cai; Eugene Lengerich; Frank Hardisty
Journal:  IEEE Comput Graph Appl       Date:  2004 Jan-Feb       Impact factor: 2.088

2.  Sustainability. Planetary boundaries: guiding human development on a changing planet.

Authors:  Will Steffen; Katherine Richardson; Johan Rockström; Sarah E Cornell; Ingo Fetzer; Elena M Bennett; Reinette Biggs; Stephen R Carpenter; Wim de Vries; Cynthia A de Wit; Carl Folke; Dieter Gerten; Jens Heinke; Georgina M Mace; Linn M Persson; Veerabhadran Ramanathan; Belinda Reyers; Sverker Sörlin
Journal:  Science       Date:  2015-01-15       Impact factor: 47.728

3.  A comprehensive review of deep learning applications in hydrology and water resources.

Authors:  Muhammed Sit; Bekir Z Demiray; Zhongrun Xiang; Gregory J Ewing; Yusuf Sermet; Ibrahim Demir
Journal:  Water Sci Technol       Date:  2020-12       Impact factor: 1.915

4.  High-resolution mapping of global surface water and its long-term changes.

Authors:  Jean-François Pekel; Andrew Cottam; Noel Gorelick; Alan S Belward
Journal:  Nature       Date:  2016-12-07       Impact factor: 49.962

5.  The measurement of observer agreement for categorical data.

Authors:  J R Landis; G G Koch
Journal:  Biometrics       Date:  1977-03       Impact factor: 2.571

6.  Deep learning-based remote sensing estimation of water transparency in shallow lakes by combining Landsat 8 and Sentinel 2 images.

Authors:  Yuhuan Cui; Zhongnan Yan; Jie Wang; Shuang Hao; Youcun Liu
Journal:  Environ Sci Pollut Res Int       Date:  2021-08-18       Impact factor: 4.223

7.  Intelligent Object Recognition of Urban Water Bodies Based on Deep Learning for Multi-Source and Multi-Temporal High Spatial Resolution Remote Sensing Imagery.

Authors:  Shiran Song; Jianhua Liu; Yuan Liu; Guoqiang Feng; Hui Han; Yuan Yao; Mingyi Du
Journal:  Sensors (Basel)       Date:  2020-01-10       Impact factor: 3.576

8.  Interrater reliability: the kappa statistic.

Authors:  Mary L McHugh
Journal:  Biochem Med (Zagreb)       Date:  2012       Impact factor: 2.313

9.  Evaluation of water quality based on a machine learning algorithm and water quality index for the Ebinur Lake Watershed, China.

Authors:  Xiaoping Wang; Fei Zhang; Jianli Ding
Journal:  Sci Rep       Date:  2017-10-09       Impact factor: 4.379

10.  Four Major South Korea's Rivers Using Deep Learning Models.

Authors:  Sangmok Lee; Donghyun Lee
Journal:  Int J Environ Res Public Health       Date:  2018-06-24       Impact factor: 3.390

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