An integrated catchment-coastal modelling system for real-time water quality forecasts

2014 ◽  
Vol 61 ◽  
pp. 458-476 ◽  
Author(s):  
Zeinab Bedri ◽  
Aisling Corkery ◽  
John J. O'Sullivan ◽  
Marcos X. Alvarez ◽  
Anders Chr. Erichsen ◽  
...  
Keyword(s):  
Water Quality ◽  
Real Time ◽  
Coastal Modelling ◽  
Modelling System

A Real-Time Mathematical Model for Three-Dimensional Tidal Flow and Water Quality

1991 ◽  
Vol 24 (6) ◽  
pp. 171-177 ◽  
Author(s):  
Zeng Fantang ◽  
Xu Zhencheng ◽  
Chen Xiancheng
Keyword(s):  
Mathematical Model ◽  
Water Quality ◽  
Real Time ◽  
Control Volume ◽  
Tidal Flow ◽  
Three Dimensional ◽  
River Estuary ◽  
Pearl River Estuary ◽  
Practical Application ◽  
The Pearl River

A real-time mathematical model for three-dimensional tidal flow and water quality is presented in this paper. A control-volume-based difference method and a “power interpolation distribution” advocated by Patankar (1984) have been employed, and a concept of “separating the top-layer water” has been developed to solve the movable boundary problem. The model is unconditionally stable and convergent. Practical application of the model is illustrated by an example for the Pearl River Estuary.

Flat Flexible Thin Milli-electrode Array for Real-time in situ Water Quality Monitoring in Distribution Systems

2017 ◽  
Vol 2017 (4) ◽  
pp. 5598-5617
Author(s):  
Zhiheng Xu ◽  
Wangchi Zhou ◽  
Qiuchen Dong ◽  
Yan Li ◽  
Dingyi Cai ◽  
...  
Keyword(s):  
Water Quality ◽  
Real Time ◽  
Distribution Systems ◽  
Electrode Array ◽  
Water Quality Monitoring ◽  
Quality Monitoring

scholarly journals Comparison of Forecasting Models for Real-Time Monitoring of Water Quality Parameters Based on Hybrid Deep Learning Neural Networks

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1547
Author(s):  
Jian Sha ◽  
Xue Li ◽  
Man Zhang ◽  
Zhong-Liang Wang
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Deep Learning ◽  
Real Time ◽  
Best Management Practices ◽  
Input Data ◽  
Management Practices ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Real Time Monitoring

Accurate real-time water quality prediction is of great significance for local environmental managers to deal with upcoming events and emergencies to develop best management practices. In this study, the performances in real-time water quality forecasting based on different deep learning (DL) models with different input data pre-processing methods were compared. There were three popular DL models concerned, including the convolutional neural network (CNN), long short-term memory neural network (LSTM), and hybrid CNN–LSTM. Two types of input data were applied, including the original one-dimensional time series and the two-dimensional grey image based on the complete ensemble empirical mode decomposition algorithm with adaptive noise (CEEMDAN) decomposition. Each type of input data was used in each DL model to forecast the real-time monitoring water quality parameters of dissolved oxygen (DO) and total nitrogen (TN). The results showed that (1) the performances of CNN–LSTM were superior to the standalone model CNN and LSTM; (2) the models used CEEMDAN-based input data performed much better than the models used the original input data, while the improvements for non-periodic parameter TN were much greater than that for periodic parameter DO; and (3) the model accuracies gradually decreased with the increase of prediction steps, while the original input data decayed faster than the CEEMDAN-based input data and the non-periodic parameter TN decayed faster than the periodic parameter DO. Overall, the input data preprocessed by the CEEMDAN method could effectively improve the forecasting performances of deep learning models, and this improvement was especially significant for non-periodic parameters of TN.

scholarly journals Measurement of Physical Parameters of Water Quality in Real-Time Based on Arduino

2021 ◽  
Vol 1751 ◽  
pp. 012067
Author(s):  
Junaidi ◽  
T M Putra ◽  
A Surtono ◽  
G A Puazi ◽  
S W Suciyati ◽  
...  
Keyword(s):  
Water Quality ◽  
Real Time ◽  
Physical Parameters

Smart and Low Cost Real Time Water Quality Monitoring System Using IoT

2019 ◽  
Author(s):  
Jeba Anandh S ◽  
Anandharaj M ◽  
Aswinrajan J ◽  
Karankumar G ◽  
Karthik P
Keyword(s):  
Water Quality ◽  
Real Time ◽  
Monitoring System ◽  
Low Cost ◽  
Water Quality Monitoring ◽  
Quality Monitoring

scholarly journals A study on prediction method for ozone dosage and residual ozone concentration in advanced ozone water treatment

2017 ◽  
Vol 12 (1) ◽  
pp. 87-96 ◽  
Author(s):  
J. S. Hyung ◽  
K. B. Kim ◽  
M. C. Kim ◽  
I. S. Lee ◽  
J. Y. Koo
Keyword(s):  
Water Quality ◽  
Hydrogen Peroxide ◽  
Water Treatment ◽  
Environmental Factors ◽  
Real Time ◽  
Ozone Concentration ◽  
Prediction Method ◽  
Optimum Amount ◽  
Factors Affecting ◽  
Water Treatment Plants

Ozone dosage in most water treatment plants is operated by determining the ozone concentration with the experience of the operation. In this case, it is not economical. This study selected the factors affecting residual ozone concentration and attempted to estimate the optimum amount of hydrogen peroxide dosage for the control of the residual ozone concentration by developing a model for the prediction of the residual ozone concentration. The prediction formulas developed in this study can quickly respond to the environment of water quality and surrounding environmental factors, which change in real time, so it is judged that they could be used for the operation of the optimum ozone process, and the control of ozone dosage could be used as a new method in controlling the concentration of ozone dosage and the concentration of residual ozone.

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