scholarly journals Water Quality Dynamic Monitoring Technology and Application Based on Ion Selective Electrodes

10.5772/28245 ◽  
2011 ◽  
Author(s):  
Dongxian He ◽  
Weifen Du ◽  
Juanxiu Hu
Keyword(s):  
Water Quality ◽  
Dynamic Monitoring ◽  
Ion Selective Electrodes ◽  
Monitoring Technology

Remote sensing inversion of water quality in coastal sea area based on machine learning: a case study of Shenzhen bay, China

2021 ◽  
Author(s):  
Xiaotong Zhu ◽  
Jinhui Jeanne Huang
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Water Quality ◽  
Predictive Accuracy ◽  
Water Environment ◽  
Quality Parameters ◽  
Machine Learning Algorithms ◽  
Dynamic Monitoring ◽  
Support Vector ◽  
Seawater Quality

<p>Remote sensing monitoring has the characteristics of wide monitoring range, celerity, low cost for long-term dynamic monitoring of water environment. With the flourish of artificial intelligence, machine learning has enabled remote sensing inversion of seawater quality to achieve higher prediction accuracy. However, due to the physicochemical property of the water quality parameters, the performance of algorithms differs a lot. In order to improve the predictive accuracy of seawater quality parameters, we proposed a technical framework to identify the optimal machine learning algorithms using Sentinel-2 satellite and in-situ seawater sample data. In the study, we select three algorithms, i.e. support vector regression (SVR), XGBoost and deep learning (DL), and four seawater quality parameters, i.e. dissolved oxygen (DO), total dissolved solids (TDS), turbidity(TUR) and chlorophyll-a (Chla). The results show that SVR is a more precise algorithm to inverse DO (R<sup>2</sup> = 0.81). XGBoost has the best accuracy for Chla and Tur inversion (R<sup>2</sup> = 0.75 and 0.78 respectively) while DL performs better in TDS (R<sup>2</sup> =0.789). Overall, this research provides a theoretical support for high precision remote sensing inversion of offshore seawater quality parameters based on machine learning.</p>

scholarly journals Temporal and Spatial Variations of Chlorophyll a Concentration and Eutrophication Assessment (1987–2018) of Donghu Lake in Wuhan Using Landsat Images

Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2192
Author(s):  
Xujie Yang ◽  
Yan Jiang ◽  
Xuwei Deng ◽  
Ying Zheng ◽  
Zhiying Yue
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Chlorophyll A ◽  
Dynamic Monitoring ◽  
Water Quality Parameter ◽  
Landsat Images ◽  
Chl A ◽  
Donghu Lake ◽  
Cubic Model

Chlorophyll a (Chl-a) concentration, which reflects the biomass and primary productivity of phytoplankton in water, is an important water quality parameter to assess the eutrophication status of water. The band combinations shown in the images of Donghu Lake (Wuhan City, China) captured by Landsat satellites from 1987 to 2018 were analyzed. The (B4 − B3)/(B4 + B3) [(Green − Red)/(Green + Red)] band combination was employed to construct linear, power, exponential, logarithmic and cubic polynomial models based on Chl-a values in Donghu Lake in April 2016. The correlation coefficient (R2), the relative error (RE) and the root mean square error (RMSE) of the cubic model were 0.859, 9.175% and 11.194 μg/L, respectively and those of the validation model were 0.831, 6.509% and 19.846μg/L, respectively. Remote sensing images from 1987 to 2018 were applied to the model and the spatial distribution of Chl-a concentrations in spring and autumn of these years was obtained. At the same time, the eutrophication status of Donghu Lake was monitored and evaluated based on the comprehensive trophic level index (TLI). The results showed that the TLI (∑) of Donghu Lake in April 2016 was 63.49 and the historical data on Chl-a concentration showed that Donghu Lake had been eutrophic. The distribution of Chl-a concentration in Donghu Lake was affected by factors such as construction of bridges and dams, commercial activities and enclosure culture in the lake. The overall distribution of Chl-a concentration in each sub-lake was higher than that in the main lake region and Chl-a concentration was highest in summer, followed by spring, autumn and winter. Based on the data of three long-term (2005–2018) monitoring points in Donghu Lake, the matching patterns between meteorological data and Chl-a concentration were analyzed. It revealed that the Chl-a concentration was relatively high in warmer years or rainy years. The long-term measured data also verified the accuracy of the cubic model for Chl-a concentration. The R2, RE and RMSE of the validation model were 0.641, 2.518% and 22.606 μg/L, respectively, which indicated that it was feasible to use Landsat images to retrieve long-term Chl-a concentrations. Based on longitudinal remote sensing data from 1987 to 2018, long-term and large-scale dynamic monitoring of Chl-a concentrations in Donghu Lake was carried out in this study, providing reference and guidance for lake water quality management in the future.

Study on the application of water quality monitoring technology based on unmanned ship

2018 ◽  
Vol 121 ◽  
pp. 311-315 ◽  
Author(s):  
Yu-jie Chen ◽  
Pei-Xue Liu ◽  
Bao-Hua Jiang ◽  
Su-Xia Zhang ◽  
Fei Feng
Keyword(s):  
Water Quality ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Monitoring Technology ◽  
Unmanned Ship

Research on dynamic monitoring technology of 4D seismic in coal mine goaf

2020 ◽  
Author(s):  
Xianxu Zhang* ◽  
Jinfeng Ma ◽  
Lin Li ◽  
Haofan Wang
Keyword(s):  
Coal Mine ◽  
Dynamic Monitoring ◽  
Monitoring Technology ◽  
4D Seismic

Designing a water-quality monitor with ion selective electrodes

2002 ◽  
Author(s):  
M.G. Buehler ◽  
S.P. Kounaves ◽  
D.P. Martin ◽  
S.J. West ◽  
G.M. Kuhlman
Keyword(s):  
Water Quality ◽  
Ion Selective Electrodes

The review of dynamic monitoring technology for crop growth

2010 ◽  
Author(s):  
Hong-wei Zhang ◽  
Huai-liang Chen ◽  
Chun-hui Zou ◽  
Wei-dong Yu
Keyword(s):  
Crop Growth ◽  
Dynamic Monitoring ◽  
Monitoring Technology

scholarly journals Optimization of Sustainable Land Use Management in Water Source Area Using Water Quality Dynamic Monitoring Model

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zhouyi Jin ◽  
Dabing Ge
Keyword(s):  
Water Quality ◽  
Land Use ◽  
Optimal Allocation ◽  
Supply And Demand ◽  
Water Sources ◽  
Dynamic Monitoring ◽  
Land Use Management ◽  
Land Resources ◽  
Soil Resources ◽  
Monitoring Model

Land use management is the primary source of resource planning, and the management part of the sustainable ecosystem of water and soil resources is an important evidence for the sustainable development of the economic and social system. This is guided by the concept of sustainable development, and on the basis of the accumulation of relevant research practices and outcomes at home and abroad, water and land based systems are a research object and study the status of water and soil resource utilization, the state of water and soil coupling, and the supply and demand status of water resources. A balance analysis was carried out, and the gray linear programming model was used to optimize the allocation of land resources using the water quality dynamic monitoring model, which achieved the best coupling of water and soil resources and the greatest benefit. In this paper, aiming at the two types of problems in comprehensive water quality evaluation, namely, aiming at indifference and spatiotemporal changes, this article explores a powerful calculation method based on variable identification models and compiles a GIS geostatistical model (it is a computer-based tool that can draw and analyze ground objects; event GIS technology integrates seamless visual effects between map and local analysis services and general data processing services) to perform spatial analysis and visual expression of the evaluation results, in-depth analysis of the connotation, and theory and optimal allocation model of land resources optimal allocation. On the basis of the conceptual framework of the best share of land sources, the theories that should follow in the best share of land sources are discussed, and the available models and their characteristics are analyzed and compared. Experimental results show that, in the data provided by the analysis of water supply and demand balance at the annual spring system site by constructing an energy monitoring model, the water supply conditions of different water sources are rough, but the data of this study shows that the water shortage rate has reached 25%. In addition, the article explains the setting variables for the optimal allocation of land resources in water sources and compares and analyzes the optimization and planning of land resources in water sources.

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