scholarly journals Research on Baiyangdian Lake Water Body Changes and Water Quality Parameters Inversion Based on Landsat Dense Time Series Data

2021 ◽  
Vol 783 (1) ◽  
pp. 012134
Author(s):  
Minghua Cheng ◽  
Miao Jiang ◽  
Zhaoqiang Huang ◽  
Hua Lei ◽  
Dongchuan Yan ◽  
...  
Water ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1929
Author(s):  
Jianzhuo Yan ◽  
Ya Gao ◽  
Yongchuan Yu ◽  
Hongxia Xu ◽  
Zongbao Xu

Recently, the quality of fresh water resources is threatened by numerous pollutants. Prediction of water quality is an important tool for controlling and reducing water pollution. By employing superior big data processing ability of deep learning it is possible to improve the accuracy of prediction. This paper proposes a method for predicting water quality based on the deep belief network (DBN) model. First, the particle swarm optimization (PSO) algorithm is used to optimize the network parameters of the deep belief network, which is to extract feature vectors of water quality time series data at multiple scales. Then, combined with the least squares support vector regression (LSSVR) machine which is taken as the top prediction layer of the model, a new water quality prediction model referred to as PSO-DBN-LSSVR is put forward. The developed model is valued in terms of the mean absolute error (MAE), the mean absolute percentage error (MAPE), the root mean square error (RMSE), and the coefficient of determination ( R 2 ). Results illustrate that the model proposed in this paper can accurately predict water quality parameters and better robustness of water quality parameters compared with the traditional back propagation (BP) neural network, LSSVR, the DBN neural network, and the DBN-LSSVR combined model.


2010 ◽  
Vol 113-116 ◽  
pp. 1367-1370 ◽  
Author(s):  
Bin Sheng Liu ◽  
Ying Wang ◽  
Xue Ping Hu

There are many ways to predict drinking water quality such as neural network, gray model, ARIMA. But the prediction precise is need to improve. This paper proposes a new forecast method according the characteristic of drinking water quality and the evidence showed that the prediction is effectively. So it is able to being used in actual prediction.


2021 ◽  
Author(s):  
Liqing Li ◽  
Xinghong Chen ◽  
Meiyi Zhang ◽  
Weijun Zhang ◽  
Dongsheng Wang ◽  
...  

Abstract Baiyangdian Lake (BYD), a large shallow lake in North China, has complex water landscape patterns that are underlies spatial variations in water quality. In this study, we collected 61 water samples from three water landscapes (reed littoral zones, fish ponds and open water) and analyzed them for water quality parameters, such as dissolved organic carbon (DOC), total nitrogen (TN), and total phosphorus (TP). Water landscape distribution (determined using remote sensing imagery) was then used to assess correlations between water quality parameters and water landscape proportion in differently scaled buffer zones. There was substantial variation across all subareas, with TN and TP concentrations ranging from 0.90–4.10 mg/L and 0.06–0.18 mg/L, respectively. Spatial variations in water quality were mainly caused by water landscape distribution and external nutrient inputs. There were negative correlations between DOC, TN, and TP concentrations and the area proportion of reed littoral zones in the 300 and 500 m buffers. In contrast, DOC, TN and TP concentrations were significantly positively correlated with the area proportion of fish ponds in the 100 m buffer. Furthermore, compared with reed littoral zones, a lower nitrogen to phosphorus ratio and a higher proportion of dissolved organic nitrogen and tyrosine-like proteins were found in fish ponds. These effects were mainly attributed to development of internal sediment loadings due to nutrient exchange between sediment and overlying water. Therefore, dredging-based sediment removal from fish ponds should be considered to suppress internal phosphorus loading and accelerate recovery of the BYD ecosystem.


2017 ◽  
Vol 52 (2) ◽  
pp. 176-188
Author(s):  
Triantafyllia-Maria Perivolioti ◽  
Antonios Mouratidis ◽  
Dimitra Bobori ◽  
Georgia Doxani ◽  
Dimitrios Terzopoulos

Author(s):  
Yuyan Liu ◽  
Fangfang Ding ◽  
Caiye Ji ◽  
Dan Wu ◽  
Lin Wang ◽  
...  

Abstract Palladium (Pd) is widely used in vehicle exhaust catalysts (VECs) to reduce toxic emissions from motor vehicles. The study aimed to quantitatively determine Pd content and water quality parameters, to analyze the variation differences and to explore the effect of water quality parameters on Pd content in the urban water environment system (wet deposition–rainfall runoff–receiving water body–estuary) of the city of Haikou, Hainan Island, China. The method used in this study included microwave digestion under high pressure and temperature, analysis by inductively coupled plasma mass spectrometry, quality control of the experimental procedure and guaranteed recovery (85% −125%). The results showed that the dissolved Pd average content in the urban water environment system was the highest in rainfall runoff (4.93 ng/L), followed by that in the receiving water body (4.56 ng/L), and it was the lowest in wet deposition (0.1 ng/L). The suspended Pd average content was the highest in the estuary (2.83 ng/L), followed by that in rainfall runoff (1.26 ng/L), and it was the lowest in wet deposition (6 × 10−4 ng/L). The particle–water partition ratio of the estuary Pd was the highest (1.26), followed by that of Pd in rainfall runoff (0.26). The particle–water partition ratio of the wet deposition Pd was the lowest (6 × 10−3). The dissolved Pd was correlated with the pH, Cl−, and total suspended solids (TSS) (correlation coefficient = 0.52, −0.68, 0.39, p < 0.05; regression coefficient = 1.27, −1.39, 0.01). The suspended Pd was only correlated with Cl− and TSS (correlation coefficient = −0.36, 0.76, p < 0.05; regression coefficient = −1.45, 0.01). Cl− and TSS were the most closely related to Pd in the water environment system. Although individual factors such as pH, Cl−, and TSS had certain migration and transformation effects on Pd in the wet deposition–rainfall runoff–receiving water body–estuary system, the probability of strong correlations was not high. In particular, Eh was not related to the dissolved nor suspended Pd content (correlation coefficient = 0.14, 0.13), which may be due to the synergistic effect of the multiple physical factors on Pd. This study was helpful to better understand the environmental behavior of Pd and provided important theoretical support for the prevention and protection against urban water environmental pollution.


2021 ◽  
Vol 3 (1) ◽  
pp. 170-204
Author(s):  
Michael C. Thrun ◽  
Alfred Ultsch ◽  
Lutz Breuer

The understanding of water quality and its underlying processes is important for the protection of aquatic environments. With the rare opportunity of access to a domain expert, an explainable AI (XAI) framework is proposed that is applicable to multivariate time series. The XAI provides explanations that are interpretable by domain experts. In three steps, it combines a data-driven choice of a distance measure with supervised decision trees guided by projection-based clustering. The multivariate time series consists of water quality measurements, including nitrate, electrical conductivity, and twelve other environmental parameters. The relationships between water quality and the environmental parameters are investigated by identifying similar days within a cluster and dissimilar days between clusters. The framework, called DDS-XAI, does not depend on prior knowledge about data structure, and its explanations are tendentially contrastive. The relationships in the data can be visualized by a topographic map representing high-dimensional structures. Two state of the art XAIs called eUD3.5 and iterative mistake minimization (IMM) were unable to provide meaningful and relevant explanations from the three multivariate time series data. The DDS-XAI framework can be swiftly applied to new data. Open-source code in R for all steps of the XAI framework is provided and the steps are structured application-oriented.


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