Water quality monitoring and assessment based on cruise monitoring, remote sensing, and deep learning: A case study of Qingcaosha Reservoir
Accurate monitoring and assessment of the environmental state, as a prerequisite for improved action, is valuable and necessary because of the growing number of environmental problems that have harmful effects on natural systems and human society. This study developed an integrated novel framework c...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-10-01
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| Series: | Frontiers in Environmental Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenvs.2022.979133/full |
| _version_ | 1828160867149021184 |
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| author | Jing Qian Hongbo Liu Li Qian Jonas Bauer Xiaobai Xue Gongliang Yu Qiang He Qi Zhou Yonghong Bi Stefan Norra |
| author_facet | Jing Qian Hongbo Liu Li Qian Jonas Bauer Xiaobai Xue Gongliang Yu Qiang He Qi Zhou Yonghong Bi Stefan Norra |
| author_sort | Jing Qian |
| collection | DOAJ |
| description | Accurate monitoring and assessment of the environmental state, as a prerequisite for improved action, is valuable and necessary because of the growing number of environmental problems that have harmful effects on natural systems and human society. This study developed an integrated novel framework containing three modules remote sensing technology (RST), cruise monitoring technology (CMT), and deep learning to achieve a robust performance for environmental monitoring and the subsequent assessment. The deep neural network (DNN), a type of deep learning, can adapt and take advantage of the big data platform effectively provided by RST and CMT to obtain more accurate and improved monitoring results. It was proved by our case study in the Qingcaosha Reservoir (QCSR) that DNN showed a more robust performance (R2 = 0.89 for pH, R2 = 0.77 for DO, R2 = 0.86 for conductivity, and R2 = 0.95 for backscattered particles) compared to the traditional machine learning, including multiple linear regression, support vector regression, and random forest regression. Based on the monitoring results, the water quality assessment of QCSR was achieved by applying a deep learning algorithm called improved deep embedding clustering. Deep clustering analysis enables the scientific delineation of joint control regions and determines the characteristic factors of each area. This study presents the high value of the framework with a core of big data mining for environmental monitoring and follow-up assessment in a manner of high frequency, multidimensionality, and deep hierarchy. |
| first_indexed | 2024-04-12T00:28:36Z |
| format | Article |
| id | doaj.art-464cddc53e384553ae05d63c25f9427b |
| institution | Directory Open Access Journal |
| issn | 2296-665X |
| language | English |
| last_indexed | 2024-04-12T00:28:36Z |
| publishDate | 2022-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Environmental Science |
| spelling | doaj.art-464cddc53e384553ae05d63c25f9427b2022-12-22T03:55:25ZengFrontiers Media S.A.Frontiers in Environmental Science2296-665X2022-10-011010.3389/fenvs.2022.979133979133Water quality monitoring and assessment based on cruise monitoring, remote sensing, and deep learning: A case study of Qingcaosha ReservoirJing Qian0Hongbo Liu1Li Qian2Jonas Bauer3Xiaobai Xue4Gongliang Yu5Qiang He6Qi Zhou7Yonghong Bi8Stefan Norra9Institute of Applied Geosciences, Karlsruhe Institute of Technology, Karlsruhe, GermanySchool of Environment and Architecture, University of Shanghai for Science and Technology, Shanghai, ChinaInstitute of Informatics, Ludwig Maximilian University of Munich, Munich, GermanyInstitute of Applied Geosciences, Karlsruhe Institute of Technology, Karlsruhe, GermanyMioTech Research, Yingtou Information Technology (Shanghai) Limited, Shanghai, ChinaState Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, ChinaKey Laboratory of Eco-environments in the Three Gorges Reservoir Region, Ministry of Education, College of Environmental and Ecology, Chongqing University, Chongqing, ChinaCollege of Environmental Science and Engineering, Tongji University, Shanghai, ChinaState Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, ChinaInstitute of Applied Geosciences, Karlsruhe Institute of Technology, Karlsruhe, GermanyAccurate monitoring and assessment of the environmental state, as a prerequisite for improved action, is valuable and necessary because of the growing number of environmental problems that have harmful effects on natural systems and human society. This study developed an integrated novel framework containing three modules remote sensing technology (RST), cruise monitoring technology (CMT), and deep learning to achieve a robust performance for environmental monitoring and the subsequent assessment. The deep neural network (DNN), a type of deep learning, can adapt and take advantage of the big data platform effectively provided by RST and CMT to obtain more accurate and improved monitoring results. It was proved by our case study in the Qingcaosha Reservoir (QCSR) that DNN showed a more robust performance (R2 = 0.89 for pH, R2 = 0.77 for DO, R2 = 0.86 for conductivity, and R2 = 0.95 for backscattered particles) compared to the traditional machine learning, including multiple linear regression, support vector regression, and random forest regression. Based on the monitoring results, the water quality assessment of QCSR was achieved by applying a deep learning algorithm called improved deep embedding clustering. Deep clustering analysis enables the scientific delineation of joint control regions and determines the characteristic factors of each area. This study presents the high value of the framework with a core of big data mining for environmental monitoring and follow-up assessment in a manner of high frequency, multidimensionality, and deep hierarchy.https://www.frontiersin.org/articles/10.3389/fenvs.2022.979133/fulldeep learningenvironmental big data miningcruise monitoringremote sensingwater qualitymonitoring |
| spellingShingle | Jing Qian Hongbo Liu Li Qian Jonas Bauer Xiaobai Xue Gongliang Yu Qiang He Qi Zhou Yonghong Bi Stefan Norra Water quality monitoring and assessment based on cruise monitoring, remote sensing, and deep learning: A case study of Qingcaosha Reservoir Frontiers in Environmental Science deep learning environmental big data mining cruise monitoring remote sensing water quality monitoring |
| title | Water quality monitoring and assessment based on cruise monitoring, remote sensing, and deep learning: A case study of Qingcaosha Reservoir |
| title_full | Water quality monitoring and assessment based on cruise monitoring, remote sensing, and deep learning: A case study of Qingcaosha Reservoir |
| title_fullStr | Water quality monitoring and assessment based on cruise monitoring, remote sensing, and deep learning: A case study of Qingcaosha Reservoir |
| title_full_unstemmed | Water quality monitoring and assessment based on cruise monitoring, remote sensing, and deep learning: A case study of Qingcaosha Reservoir |
| title_short | Water quality monitoring and assessment based on cruise monitoring, remote sensing, and deep learning: A case study of Qingcaosha Reservoir |
| title_sort | water quality monitoring and assessment based on cruise monitoring remote sensing and deep learning a case study of qingcaosha reservoir |
| topic | deep learning environmental big data mining cruise monitoring remote sensing water quality monitoring |
| url | https://www.frontiersin.org/articles/10.3389/fenvs.2022.979133/full |
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