Predictions of ice-cover development in streams and its effect on dissolved oxygen modelling

1979 ◽  
Vol 6 (2) ◽  
pp. 197-207 ◽  
Author(s):  
E. McBean ◽  
G. Farquhar ◽  
N. Kouwen ◽  
O. Dubek
Keyword(s):  
Mathematical Model ◽  
Water Quality ◽  
Dissolved Oxygen ◽  
Prediction Model ◽  
Data Input ◽  
Quality Model ◽  
Two Stage ◽  
Second Stage ◽  
Series Of Experiments ◽  
Ice Edge

A two-stage mathematical model is developed for predicting dissolved oxygen levels in ice-covered rivers. The first stage of the model is a prediction model for ice-edge progression as a function of time, and the second stage consists of an extrapolation of a widely used 'summer condition' water-quality model. The results of a series of experiments, both field and laboratory-based, which served as data input generators and calibration testing of the model, are provided.Briefcase-study applications of elements of the model to the Speed River and to the Saint John River are included.

scholarly journals Simulating Diurnal Variations of Water Temperature and Dissolved Oxygen in Shallow Minnesota Lakes

Water ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 1980
Author(s):  
Bushra Tasnim ◽  
Jalil A. Jamily ◽  
Xing Fang ◽  
Yangen Zhou ◽  
Joel S. Hayworth
Keyword(s):  
Water Quality ◽  
Dissolved Oxygen ◽  
Water Temperature ◽  
Shallow Lakes ◽  
Quality Model ◽  
Lake Water Quality ◽  
Ecological Processes ◽  
Turbulent Diffusion Coefficient ◽  
Hourly Data ◽  
Mean Square Errors

In shallow lakes, water quality is mostly affected by weather conditions and some ecological processes which vary throughout the day. To understand and model diurnal-nocturnal variations, a deterministic, one-dimensional hourly lake water quality model MINLAKE2018 was modified from daily MINLAKE2012, and applied to five shallow lakes in Minnesota to simulate water temperature and dissolved oxygen (DO) over multiple years. A maximum diurnal water temperature variation of 11.40 °C and DO variation of 5.63 mg/L were simulated. The root-mean-square errors (RMSEs) of simulated hourly surface temperatures in five lakes range from 1.19 to 1.95 °C when compared with hourly data over 4–8 years. The RMSEs of temperature and DO simulations from MINLAKE2018 decreased by 17.3% and 18.2%, respectively, and Nash-Sutcliffe efficiency increased by 10.3% and 66.7%, respectively; indicating the hourly model performs better in comparison to daily MINLAKE2012. The hourly model uses variable hourly wind speeds to determine the turbulent diffusion coefficient in the epilimnion and produces more hours of temperature and DO stratification including stratification that lasted several hours on some of the days. The hourly model includes direct solar radiation heating to the bottom sediment that decreases magnitude of heat flux from or to the sediment.

Study on Prediction Model of Dissolved Oxygen about Water Quality Monitoring System Based on BP Neural Network

2014 ◽  
Vol 912-914 ◽  
pp. 1407-1411 ◽  
Author(s):  
Jing Xin Yan ◽  
Li Juan Yu ◽  
Wen Wu Mao ◽  
Shou Qi Cao
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Dissolved Oxygen ◽  
Prediction Model ◽  
Monitoring System ◽  
Bp Neural Network ◽  
Water Quality Monitoring ◽  
High Water ◽  
Eriocheir Sinensis ◽  
Quality Monitoring

Eriocheir sinensis should cultivate in high water quality ponds, which is affected by many combined factors such as physics, chemistry, biology etc. Using the real-time water quality monitoring historical data to test one of the water quality indexes and predict this index in the next time has great significance. The dissolved oxygen is one of the most important indexes in aquaculture, such as in the Eriocheir sinensis pond. This paper established a dissolved oxygen prediction model of water quality monitoring system based on BP neural network. The forecast data which is predicted by the established model could fit the actual monitoring data very well.

Modelling aquatic vegetation and dissolved oxygen after a flood event in the Bow River, Alberta, Canada

2009 ◽  
Vol 36 (3) ◽  
pp. 492-503 ◽  
Author(s):  
K. L. Robinson ◽  
C. Valeo ◽  
M. C. Ryan ◽  
A. Chu ◽  
M. Iwanyshyn
Keyword(s):  
Water Quality ◽  
Dissolved Oxygen ◽  
Aquatic Vegetation ◽  
Dominant Role ◽  
Dry Weight ◽  
Flood Event ◽  
Species Competition ◽  
Quality Model ◽  
Wastewater Infrastructure ◽  
The City

Traditionally, macrophyte density has been considered the primary factor affecting the large dissolved oxygen fluctuations in the Bow River. After a major flood event scoured macrophytes in 2005, and subsequently changed river dynamics, the City of Calgary needed to update their predictive computer model for water quality to reflect the new conditions, which led to this study. A 2006 aquatic vegetation survey was also completed to assess post-flood conditions. The survey found that the average macrophyte dry weight was much lower (28 g/m2 ± 100 (p = 0.05)) than the historic average of 241 g/m2 ± 29, while the average periphyton chlorophyll-a concentration was higher (343 mg/m2 ± 71) than the historic average (158 mg/m2 ± 17)). Dissolved oxygen (DO) fluctuations were similar to pre-flood levels despite changes in the dominant vegetation. Using the results of this survey, the significant and previously unrecognized effects of periphyton diurnal processes on DO concentrations in the Bow River were identified and the Bow River water quality model (BRWQM) was recalibrated to reflect these findings. Adjustments were made to the BRWQM’s periphyton submodel to account for the more dominant role played by these organisms in river processes, and a competitive shading factor between macrophytes and periphyton was also introduced to more accurately model the species' competition for available sunlight. This newly calibrated and validated version of BRWQM was tested and found capable of predicting the occurrence of low DO concentrations in the Bow River and can provide a useful tool for forecasting the water quality effects of the city's planned wastewater infrastructure expansion.

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.

scholarly journals CE-QUAL-W2 model of dam outflow elevation impact on temperature, dissolved oxygen and nutrients in a reservoir

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Karl-Erich Lindenschmidt ◽  
Meghan K. Carr ◽  
Amir Sadeghian ◽  
Luis Morales-Marin
Keyword(s):  
Water Quality ◽  
Dissolved Oxygen ◽  
Water Temperature ◽  
Nutrient Dynamics ◽  
Water Withdrawal ◽  
Quality Model ◽  
Temperature Regimes ◽  
Labile Phosphorus ◽  
Daily Water ◽  
Animal Consumption

AbstractDams are typically designed to serve as flood protection, provide water for irrigation, human and animal consumption, and harness hydropower. Despite these benefits, dam operations can have adverse effects on in-reservoir and downstream water temperature regimes, biogeochemical cycling and aquatic ecosystems. We present a water quality dataset of water withdrawal scenarios generated after implementing the 2D hydrodynamic and water quality model, CE-QUAL-W2. The scenarios explore how six water extraction scenarios, starting at 5 m above the reservoir bottom at the dam and increasing upward at 10 m intervals to 55 m, influence water quality in Lake Diefenbaker reservoir, Saskatchewan, Canada. The model simulates daily water temperature, dissolved oxygen, total phosphorus, phosphate as phosphorus, labile phosphorus, total nitrogen, nitrate as nitrogen, labile nitrogen, and ammonium at 87 horizontal segments and at 60 water depths during the 2011–2013 period. This dataset intends to facilitate a broader investigation of in-reservoir nutrient dynamics under dam operations, and to extend the understanding of reservoir nutrient dynamics globally.

Influence of organic and ammonia loading on nitrifier activity and nitrification performance for a two-stage biological aerated filter system

1999 ◽  
Vol 39 (7) ◽  
pp. 227-234 ◽  
Author(s):  
K. R. Gilmore ◽  
K. J. Husovitz ◽  
T. Holst ◽  
N. G. Love
Keyword(s):  
Water Quality ◽  
Ammonia Oxidation ◽  
Oligonucleotide Probe ◽  
Treatment Plant ◽  
Ammonia Oxidizing Bacteria ◽  
Biological Aerated Filter ◽  
Two Stage ◽  
Second Stage ◽  
Oxidation Stage ◽  
Aerated Filter

A pilot-scale, two-stage (carbon oxidation stage one, ammonia oxidation stage two) fixed film biological aerated filter (BAF) process was operated during the wintertime on-site at a domestic wastewater treatment plant. Over the study period, hydraulic loadings to the system were varied and generated a range of organic and ammonia loading conditions. Nitrification performance was monitored based on water quality along the length of the filters, effluent water quality, and activity levels of ammonia-oxidizing bacteria within the biofilm using an oligonucleotide probe. Overall nitrification efficiency for wintertime conditions (average temperature 12.4 ± 0.1°C) was greater than 90 percent when ammonia-N loadings to the second stage were 0.6 kg/m3-day or less. Nitrification efficiency started to deteriorate at loadings beyond this point. Biofilm and liquid samples were collected along the distance of the two columns at high and low ammonia loadings. The degree of activity observed by ammonia oxidizing bacteria in the biofilm corresponded with the disappearance of ammonia and the generation of nitrate as water passed through the columns. The zones of ammonia oxidizing activity progressed along the length of the columns as organic and ammonia loadings to the system increased. The oligonucleotide probe data suggest that this shift in the location of the nitrifier population is due to higher BOD loads to the second stage, which supported higher levels of heterotrophic growth in the column.

Biology and Management of Inland Striped Bass and Hybrid Striped Bass

2013 ◽  
Keyword(s):  
Water Quality ◽  
South Carolina ◽  
Dissolved Oxygen ◽  
Striped Bass ◽  
Algal Blooms ◽  
Management Practices ◽  
Nutrient Loading ◽  
Phosphorus Loading ◽  
Quality Model ◽  
Habitat Temperature

<em>Abstract</em>.—A CE-QUAL-W2 water quality model was used to characterize the availability of striped bass <em>Morone saxatilis</em> habitat in Lake Greenwood, South Carolina, during 2004 and 2005. Although the lake has a productive fishery, water quality and aquatic habitat are affected by nutrient loading, algal blooms, and extensive oxygen depletion in the bottom waters. The main objectives were to characterize habitat availability and predict the implications of a change in phosphorus loading from the Saluda and Reedy rivers. The baseline scenario of the model showed that habitat was most critical during July and August, when as little of 5% of the reservoir contained tolerable habitat (temperature <28°C and dissolved oxygen >2 mg/L). Favorable habitat (temperature <25°C and dissolved oxygen >2 mg/L) was usually absent for most of July and August. Pulses of higher inflow or freshets produced short-term increases in tolerable habitat, especially in the upper end of the reservoir. Phosphorus-loading scenarios predicted that large reductions (50% or more) would be required to improve habitat substantially during midsummer. For the manager of a striped bass fishery, water quality models can be useful tools for evaluating habitat, especially under marginal conditions, and for predicting the impact of altered water management practices.

scholarly journals Integrated versus isolated scenario for prediction dissolved oxygen at progression of water quality monitoring stations

2011 ◽  
Vol 15 (8) ◽  
pp. 2693-2708 ◽  
Author(s):  
A. Najah ◽  
A. El-Shafie ◽  
O. A. Karim ◽  
O. Jaafar
Keyword(s):  
Water Quality ◽  
Dissolved Oxygen ◽  
Prediction Model ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Effective Parameters ◽  
Data Set ◽  
Proposed Model ◽  
Input Parameters ◽  
Predicted Values

Abstract. This study examined the potential of Multi-layer Perceptron Neural Network (MLP-NN) in predicting dissolved oxygen (DO) at Johor River Basin. The river water quality parameters were monitored regularly each month at four different stations by the Department of Environment (DOE) over a period of ten years, i.e. from 1998 to 2007. The following five water quality parameters were selected for the proposed MLP-NN modelling, namely; temperature (Temp), water pH, electrical conductivity (COND), nitrate (NO3) and ammonical nitrogen (NH3-NL). In this study, two scenarios were introduced; the first scenario (Scenario 1) was to establish the prediction model for DO at each station based on five input parameters, while the second scenario (Scenario 2) was to establish the prediction model for DO based on the five input parameters and DO predicted at previous station (upstream). The model needs to verify when output results and the observed values are close enough to satisfy the verification criteria. Therefore, in order to investigate the efficiency of the proposed model, the verification of MLP-NN based on collection of field data within duration 2009–2010 is presented. To evaluate the effect of input parameters on the model, the sensitivity analysis was adopted. It was found that the most effective inputs were oxygen-containing (NO3) and oxygen demand (NH3-NL). On the other hand, Temp and pH were found to be the least effective parameters, whereas COND contributed the lowest to the proposed model. In addition, 17 neurons were selected as the best number of neurons in the hidden layer for the MLP-NN architecture. To evaluate the performance of the proposed model, three statistical indexes were used, namely; Coefficient of Efficiency (CE), Mean Square Error (MSE) and Coefficient of Correlation (CC). A relatively low correlation between the observed and predicted values in the testing data set was obtained in Scenario 1. In contrast, high coefficients of correlation were obtained between the observed and predicted values for the test sets of 0.98, 0.96 and 0.97 for all stations after adopting Scenario 2. It appeared that the results for Scenario 2 were more adequate than Scenario 1, with a significant improvement for all stations ranging from 4 % to 8 %.

Parameters Optimization of Ditcher Chain Transmission System of Multi-Functional Machine

2014 ◽  
Vol 1030-1032 ◽  
pp. 1224-1228
Author(s):  
Yu Lun Chen ◽  
Wei Qiu ◽  
Wei Min Ding ◽  
Yi Nian Li ◽  
Yu Tao Liu
Keyword(s):  
Mathematical Model ◽  
Production Cost ◽  
Optimization Method ◽  
Transmission System ◽  
Parameters Optimization ◽  
Two Stage ◽  
Second Stage ◽  
Chain Transmission ◽  
Original Scheme ◽  
The Mathematical Model

In this paper, the mathematical model based on modern design methods of machinery used to optimize the parameters of the two-stage chain transmission system was established. The optimization toolbox of Matlab was introduced as well. Optimization results showed that, the pitches of first and second-stage chains were 19.05 and 25.4 respectively; compared with the original scheme, transmission ratio of first-stage chain decrease from 2.57 to 1.77, while the second-stage increase from 1.0 to 1.45. The number of chain strands of second-stage chain decreased from 2 to 1. The teeth number of the driven sprocket of the first-stage chain decreased also from 59 to 41. Total weight of the sprockets of the chain transmission system decreased from 25.40 kg to 14.84 kg, at least 41.57% material was saved and not less than 50% production cost was reduced. The optimization method is simple, feasible and the optimized parameters are reliable in practice.

Modeling and Dynamic Evaluation of a Two-Stage Two-Spool Servovalve Used for Pressure Control

1991 ◽  
Vol 113 (4) ◽  
pp. 709-713 ◽  
Author(s):  
S. T. Tsai ◽  
A. Akers ◽  
S. J. Lin
Keyword(s):  
Mathematical Model ◽  
Control Valve ◽  
Pressure Control ◽  
Experimental Results ◽  
Two Stage ◽  
Dynamic Evaluation ◽  
Second Stage ◽  
Experimental Values ◽  
Pressure Control Valve ◽  
Good Agreement

Experimental results for a unique design of a two-spool pressure control valve were reported by Anderson (1984). The first stage is a dynamically stable flapper-nozzle valve for which a mathematical model is already available (Lin and Akers, 1989a). For the second stage, however, which consists of two parallel spools in a common body, no such model existed. The purpose of this paper was therefore to construct such a model and to compare results calculated from it to experimental values. Moderately good agreement with experimental values was obtained.

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