scholarly journals Prediction of dissolved oxygen in urban rivers at the Three Gorges Reservoir, China: extreme learning machines (ELM) versus artificial neural network (ANN)

2019 ◽  
Vol 55 (1) ◽  
pp. 106-118 ◽  
Author(s):  
Senlin Zhu ◽  
Salim Heddam
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Dissolved Oxygen ◽  
Three Gorges Reservoir ◽  
Model Performance ◽  
Quality Data ◽  
Urban Rivers ◽  
Three Gorges ◽  
Water Quality Data ◽  
The Three Gorges

Abstract In the present study, two non-linear mathematical modelling approaches, namely, extreme learning machine (ELM) and multilayer perceptron neural network (MLPNN) were developed to predict daily dissolved oxygen (DO) concentrations. Water quality data from four urban rivers in the backwater zone of the Three Gorges Reservoir, China were used. The water quality data selected consisted of daily observed water temperature, pH, permanganate index, ammonia nitrogen, electrical conductivity, chemical oxygen demand, total nitrogen, total phosphorus and DO. The accuracy of the ELM model was compared with the standard MLPNN using several error statistics such as root mean squared error, mean absolute error, the coefficient of correlation and the Willmott index of agreement. Results showed that the ELM and MLPNN models perform well for the Wubu River, acceptably for the Yipin River and moderately for the Huaxi River, while poor model performance was obtained at the Tributary of Huaxi River. Model performance is negatively correlated with pollution level in each river. The MLPNN model slightly outperforms the ELM model in DO prediction. Overall, it can be concluded that MLPNN and ELM models can be applied for DO prediction in low-impacted rivers, while they may not be appropriate for DO modelling for highly polluted rivers. This article has been made Open Access thanks to the kind support of CAWQ/ACQE (https://www.cawq.ca).

Mulberry trees conserved soil and protected water quality in the riparian zone of the Three Gorges Reservoir, China

2015 ◽  
Vol 23 (6) ◽  
pp. 5288-5295 ◽  
Author(s):  
Yun Liu ◽  
J. H. Martin Willison ◽  
Pan Wan ◽  
Xing-zheng Xiong ◽  
Yang Ou ◽  
...  
Keyword(s):  
Water Quality ◽  
Three Gorges Reservoir ◽  
Riparian Zone ◽  
Three Gorges ◽  
The Three Gorges Reservoir ◽  
The Three Gorges

scholarly journals Visualizing Water Quality Sampling-Events in Florida

2015 ◽  
Vol II-4/W2 ◽  
pp. 73-79 ◽  
Author(s):  
M. D. Bolt
Keyword(s):  
Water Quality ◽  
Dissolved Oxygen ◽  
Data Visualization ◽  
Coastal Water ◽  
Environmental Quality ◽  
Additional Data ◽  
Monitoring Program ◽  
Quality Data ◽  
Water Quality Data ◽  
Environmental Narratives

Water quality sampling in Florida is acknowledged to be spatially and temporally variable. The rotational monitoring program that was created to capture data within the state’s thousands of miles of coastline and streams, and millions of acres of lakes, reservoirs, and ponds may be partly responsible for inducing the variability as an artifact. Florida’s new dissolved-oxygen-standard methodology will require more data to calculate a percent saturation. This additional data requirement’s impact can be seen when the new methodology is applied retrospectively to the historical collection. To understand how, where, and when the methodological change could alter the environmental quality narrative of state waters requires addressing induced bias from prior sampling events and behaviors. Here stream and coastal water quality data is explored through several modalities to maximize understanding and communication of the spatiotemporal relationships. Previous methodology and expected-retrospective calculations outside the regulatory framework are found to be significantly different, but dependent on the spatiotemporal perspective. Data visualization is leveraged to demonstrate these differences, their potential impacts on environmental narratives, and to direct further review and analysis.

Hydrology and Water Quality Survey Near the Gezhouba Dam in the Winter

2013 ◽  
Vol 726-731 ◽  
pp. 3256-3261
Author(s):  
Jia Fei Zhou ◽  
Cong Feng Wang ◽  
De Fu Liu ◽  
Jing Wen Xiang ◽  
Ping Zhao ◽  
...  
Keyword(s):  
Water Quality ◽  
Dissolved Oxygen ◽  
Water Temperature ◽  
Flow Velocity ◽  
Water Column ◽  
Quality Data ◽  
Chinese Sturgeon ◽  
Water Quality Data ◽  
Gezhouba Dam ◽  
Survival Condition

Filed hydrology and water quality data were collected near the Gezhouba Dam early December of 2012 to analyze the response of Chinese Sturgeon survival condition to water temperature, dissolved oxygen (DO), pH, transparency (SD) and bottom flow-velocity. The results showed that water temperature lag is unconspicuous. The water temperature of Gezhouba Dam Sanjiang (GDS) was lower than that of Gezhouba Dam River (GDR), and it hindered propagation of sturgeon eggs. DO decreased fast in the vertical water column of GDS, pH ranged from 7.5 to 7.71. The hydrology and water quality were suitable for the life condition of sturgeon eggs and fry, except index of bottom flow-velocity.

scholarly journals Fish Rescue during Streamflow Intermittency May Not Be Effective for Conservation of Rio Grande Silvery Minnow

Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3371
Author(s):  
Thomas P. Archdeacon ◽  
Tracy A. Diver ◽  
Justin K. Reale
Keyword(s):  
Water Quality ◽  
Dissolved Oxygen ◽  
Fish Assemblages ◽  
Sublethal Effects ◽  
Rio Grande ◽  
Quality Data ◽  
Water Quality Data ◽  
Low Dissolved Oxygen ◽  
Rio Grande Silvery Minnow ◽  
Oxygen Concentrations

Streamflow intermittency can reshape fish assemblages and present challenges to recovery of imperiled species. During streamflow intermittency, fish can be subjected to a variety of stressors, including exposure to crowding, high water temperatures, and low dissolved oxygen, resulting in sublethal effects or mortality. Rescue of fishes is often used as a conservation tool to mitigate the negative impacts of streamflow intermittency. The effectiveness of such actions is rarely evaluated. Here, we use multi-year water quality data collected from isolated pools during rescue of Rio Grande silvery minnow Hybognathus amarus, an endangered minnow. We examined seasonal and diel water quality patterns to determine if fishes are exposed to sublethal and critical water temperatures or dissolved oxygen concentrations during streamflow intermittency. Further, we determined survival of rescued Rio Grande silvery minnow for 3–5 weeks post-rescue. We found that isolated pool temperatures were much warmer (>40 °C in some pools) compared to upstream perennial flows, and had larger diel fluctuations, >10 °C compared to ~5 °C, and many pools had critically low dissolved oxygen concentrations. Survival of fish rescued from isolated pools during warmer months was <10%. Reactive conservation actions such as fish rescue are often costly, and in the case of Rio Grande silvery minnow, likely ineffective. Effective conservation of fishes threatened by streamflow intermittency should focus on restoring natural flow regimes that restore the natural processes under which fishes evolved.

The Discharge Outlets Layout Optimization of Typical River Section in the Three Gorges Reservoir Area

2014 ◽  
Vol 955-959 ◽  
pp. 3287-3294
Author(s):  
Zheng Wang ◽  
Ying Liu
Keyword(s):  
Water Quality ◽  
Three Gorges Reservoir ◽  
Three Gorges Reservoir Area ◽  
Three Gorges ◽  
Mixing Zone ◽  
Unit Load ◽  
Reservoir Area ◽  
The Three Gorges Reservoir ◽  
The Three Gorges ◽  
The Impact

To improve water quality and alleviate pollution in Changshou-Fuling section of the Three Gorges Reservoir Area, an analysis methodology of regional key discharge outlet based on hydrodynamic-water quality model was developed. The EFDC model was used to study the impact of different discharge outlets on the transport of contamination in the study area by using the concept of pollutant mixing zone and pollutant mixing zone per unit load. Model calibration was conducted using observed data in 2008. Results indicated that EFDC could perfectly simulate hydrodynamics characteristics and contaminant transport process. Calculated results of pollutant mixing zone per unit load showed that the discharge outlet location in Fuling is more reasonable than that in Changshou. This study provides useful information for optimization of discharge outlets location and prediction of pollutant mixing zone in the study area, which is important for the government to make water pollution control measures.

scholarly journals River Dissolved Oxygen Prediction Based on Random Forest and LSTM

2021 ◽  
Vol 37 (5) ◽  
pp. 901-910
Author(s):  
Juan Huan ◽  
Bo Chen ◽  
Xian Gen Xu ◽  
Hui Li ◽  
Ming Bao Li ◽  
...  
Keyword(s):  
Water Quality ◽  
Random Forest ◽  
Quality Management ◽  
Dissolved Oxygen ◽  
Prediction Model ◽  
Water Quality Management ◽  
Quality Data ◽  
Mean Square ◽  
Water Quality Data ◽  
Better Than

HighlightsRandom Forest (RF) and LSTM were developed for river DO prediction.PH is the most important feature affecting DO prediction.The model base on RF is better than the model not on RF, and the dimensionality of the input data is reduced by RF.RF-LSTM model is outperformed SVR, RF-SVR, BP, RF-BP, LSTM, RNN models in DO prediction.Abstract. In order to improve the prediction accuracy of dissolved oxygen in rivers, a dissolved oxygen prediction model based on Random Forest (RF) and Long Short Term Memory networks (LSTM) is proposed. First, the Random Forest performs feature selection, which reduces the input dimension of the data and eliminates the influence of irrelevant variables on the prediction of dissolved oxygen. Then build the LSTM river dissolved oxygen prediction model to fit the relationship between water quality data and dissolved oxygen, and finally use real water quality data in the river for verification. The experimental results show that the mean square error (MSE), absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (R2) of the RF-LSTM model are 0.658, 0.528, 13.502, 0.811, 0.744, respectively, which are better than other models. The RF-LSTM model has good predictive performance and can provide a reference for river water quality management. Keywords: Dissolved oxygen prediction, LSTM, Random forest, Time series, Water quality management.

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