scholarly journals Water Quality Evaluation of the Yangtze River in China Using Machine Learning Techniques and Data Monitoring on Different Time Scales

Water ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 339 ◽  
Author(s):  
Zhenzhen Di ◽  
Miao Chang ◽  
Peikun Guo
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Yangtze River ◽  
Quality Evaluation ◽  
Yangtze River Basin ◽  
Developed Countries ◽  
Machine Learning Techniques ◽  
Polluted Water ◽  
The Yangtze River ◽  
Water Quality Evaluation

Unlike developed countries, China has a nationally unified water environment standard and a specific watershed protection bureau to perform water quality evaluation. It is a major challenge to assess the water quality of a large watershed at a wide spatial scale and to make decisions in a scientific way. In 2016, weekly and real-time data for four monitoring indicators (pH, dissolved oxygen, permanganate index, and ammonia nitrogen) were collected at 21 surface water sections (sites) of the Yangtze River Basin, China. Results showed that one site had a relatively low Site Water Quality Index and was polluted for 12 weeks meanwhile. By using expectation-maximization clustering and hierarchical clustering algorithms, the 21 sites were classified. Variable spatiotemporal distribution characteristics for water quality and pollutants were found; some sites exhibited similar water quality variations on the weekly scale, but had different yearly grades. The results revealed polluted water quality for short periods and abrupt anomalies, which imply potential pollution sources and negative effects on water ecosystems. Potential spatio-temporal water quality characteristics, explored by machine learning methods and evidenced by time series and statistical models, could be applied in environmental decision support systems to make watershed management more objective, reliable, and powerful.

scholarly journals Spatiotemporal Characteristics of the Water Quality and Its Multiscale Relationship with Land Use in the Yangtze River Basin

2021 ◽  
Vol 13 (16) ◽  
pp. 3309
Author(s):  
Jian Wu ◽  
Sidong Zeng ◽  
Linhan Yang ◽  
Yuanxin Ren ◽  
Jun Xia
Keyword(s):  
Water Quality ◽  
Land Use ◽  
Temporal Variation ◽  
Yangtze River ◽  
Stream Water ◽  
Yangtze River Basin ◽  
The Yangtze River ◽  
Stream Water Quality ◽  
The Yangtze River Basin

The spatiotemporal characteristics of river water quality are the key indicators for ecosystem health evaluation in basins. Land use patterns, as one of the main driving forces of water quality change, affect stream water quality differently with the variations in the spatiotemporal scales. Thus, quantitative analysis of the relationship between different land cover types and river water quality contributes to a better understanding of the effects of land cover on water quality, the landscape planning of water quality protection, and integrated water resources management. Based on water quality data of 2006–2018 at 18 typical water quality stations in the Yangtze River basin, this study analyzed the spatial and temporal variation characteristics of water quality by using the single-factor water quality identification index through statistical analysis. Furthermore, the Spearman correlation analysis method was adopted to quantify the spatial-scale and temporal-scale effects of various land uses, including agricultural land (AL), forest land (FL), grassland (GL), water area (WA), and construction land (CL), on the stream water quality of dissolved oxygen (DO), chemical oxygen demand (CODMn), and ammonia (NH3-N). The results showed that (1) in terms of temporal variation, the water quality of the river has improved significantly and the tributaries have improved more than the main rivers; (2) in the spatial variation respect, the water quality pollutants in the tributaries are significantly higher than those in the main stream, and the concentration of pollutants increases with the decrease of the distance from the estuary; and (3) the correlation between DO and land use is low, while that between NH3-N, CODMn, and land use is high. CL and AL have a negative effect on water quality, while FL and GL have a purifying effect on water quality. In particular, AL and CL have a significant positive correlation with pollutants in water. Compared with NH3-N, CODMn has a higher correlation with land use at a larger scale. The results highlight the spatial scale and seasonal dependence of land use on water quality, which can provide a scientific basis for land management and seasonal pollution control.

scholarly journals Discharge Estimation Using Integrated Satellite Data and Hybrid Model in the Midstream Yangtze River

2021 ◽  
Vol 13 (12) ◽  
pp. 2272
Author(s):  
Jinghua Xiong ◽  
Shenglian Guo ◽  
Jiabo Yin
Keyword(s):  
Machine Learning ◽  
Hybrid Model ◽  
Yangtze River ◽  
Yangtze River Basin ◽  
Machine Learning Techniques ◽  
In Situ Observation ◽  
Sensing Data ◽  
In Situ Data ◽  
Virtual Station

Remotely sensing data have advantages in filling spatiotemporal gaps of in situ observation networks, showing potential application for monitoring floods in data-sparse regions. By using the water level retrievals of Jason-2/3 altimetry satellites, this study estimates discharge at a 10-day timescale for the virtual station (VS) 012 and 077 across the midstream Yangtze River Basin during 2009–2016 based on the developed Manning formula. Moreover, we calibrate a hybrid model combined with Gravity Recovery and Climate Experiment (GRACE) data, by coupling the GR6J hydrological model with a machine learning model to simulate discharge. To physically capture the flood processes, the random forest (RF) model is employed to downscale the 10-day discharge into a daily scale. The results show that: (1) discharge estimates from the developed Manning formula show good accuracy for the VS012 and VS077 based on the improved Multi-subwaveform Multi-weight Threshold Retracker; (2) the combination of the GR6J and the LSTM models substantially improves the performance of the discharge estimates solely from either the GR6J or LSTM models; (3) RF-downscaled daily discharge demonstrates a general consistency with in situ data, where NSE/KGE between them are as high as 0.69/0.83. Our approach, based on multi-source remotely sensing data and machine learning techniques, may benefit flood monitoring in poorly gauged areas.

scholarly journals Improving the Water Quality Monitoring System in the Yangtze River Basin—Legal Suggestions to the Implementation of the Yangtze River Protection Law

Laws ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 25
Author(s):  
Qiu Qiu ◽  
Liping Dai ◽  
Helena F. M. W. Van Rijswick ◽  
Gang Tu
Keyword(s):  
Water Quality ◽  
Monitoring System ◽  
River Basin ◽  
Yangtze River ◽  
Yangtze River Basin ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
The Yangtze River ◽  
Legal Perspective ◽  
The Yangtze River Basin

The Yangtze River Basin is the largest river basin in China and has the most complex trans-boundary problems. The water quality monitoring system of the provincial boundary sections in the basin is the typical go-to system to show the interaction between administrative regions and basins. In this article, we discuss the water quality monitoring system in the basin from a legal perspective, explore the achievements and deficiencies of the system, and identify the main elements that constrain the effective operation of the system in the basin, including the fragmented competencies of monitoring institutions, the different monitoring techniques, the overlapping monitoring contents and scopes, the different data releasing channels, and the different applications of the data. We provide legislative suggestions to implement the newly enacted Yangtze River Protection Law and valuable lessons for the design of monitoring systems in other countries or (trans-boundary) basins that face a similar situation.

Sign in / Sign up

Export Citation Format

Share Document