scholarly journals Monthly Forecasting of Water Quality Parameters within Bayesian Networks: A Case Study of Honolulu, Pacific Ocean

2018 ◽  
Vol 4 (1) ◽  
pp. 188 ◽  
Author(s):  
Ehsan Jafari Nodoushan
Keyword(s):  
Water Quality ◽  
Time Series ◽  
Pacific Ocean ◽  
Network Models ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Neural Network Models ◽  
Great Ability ◽  
Previous Time ◽  
Ann Models

This study investigates the efficiency of Bayesian network (BN) and also artificial neural network models for predicting water quality parameters in Honolulu, Pacific Ocean. Monthly forecasting of three important characteristics of water body including water temperature, salinity and dissolved oxygen have been taken under consideration. Two separate strategies were applied in which the first strategy was related to prediction of the water quality parameters based on previous time series of the same variable. In the second strategy, an attempt was made to forecast DO using different affecting parameters such as temperature, salinity, previous time series of DO, and amount of chlorophyll. The efficiency of the models were assessed by using error measures. Results revealed that the BN models are superior over the ANN models in case of temperature and DO forecasting. Also, it was found that the first strategy is more efficient than the second strategy for predicting DO concentration. The best BN models for temperature, salinity and DO were achieved when time series of the same parameter up to 3, 2, and 3 previous months applied as input variables respectively. Overall, it can be concluded that BN and ANN models can be successfully applied for water quality modelling and forecasting in coastal waters. Moreover, the current study demonstrated that the BN models have a great ability dealing with time series including incomplete or missing data.

scholarly journals Monitoring water quality parameters of Lake Koronia by means of long time-series multispectral satellite images

2017 ◽  
Vol 52 (2) ◽  
pp. 176-188
Author(s):  
Triantafyllia-Maria Perivolioti ◽  
Antonios Mouratidis ◽  
Dimitra Bobori ◽  
Georgia Doxani ◽  
Dimitrios Terzopoulos
Keyword(s):  
Water Quality ◽  
Time Series ◽  
Satellite Images ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Long Time Series ◽  
Multispectral Satellite Images ◽  
Long Time

scholarly journals SATELLITE OBSERVED WATER QUALITY CHANGES IN THE LAURENTIAN GREAT LAKES DUE TO INVASIVE SPECIES, ANTHROPOGENIC FORCING, AND CLIMATE CHANGE

2017 ◽  
Vol XLII-3/W2 ◽  
pp. 189-195 ◽  
Author(s):  
R. A. Shuchman ◽  
K. R. Bosse ◽  
M. J. Sayers ◽  
G. L. Fahnenstiel ◽  
G. Leshkevich
Keyword(s):  
Climate Change ◽  
Water Quality ◽  
Time Series ◽  
Great Lakes ◽  
Primary Productivity ◽  
Lake Superior ◽  
Water Clarity ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Laurentian Great Lakes

Long time series of ocean and land color satellite data can be used to measure Laurentian Great Lakes water quality parameters including chlorophyll, suspended minerals, harmful algal blooms (HABs), photic zone and primary productivity on weekly, monthly and annual observational intervals. The observed changes in these water quality parameters over time are a direct result of the introduction of invasive species such as the <i>Dreissena</i> mussels as well as anthropogenic forcing and climate change. Time series of the above mentioned water quality parameters have been generated based on a range of satellite sensors, starting with Landsat in the 1970s and continuing to the present with MODIS and VIIRS. These time series have documented the effect the mussels have had on increased water clarity by decreasing the chlorophyll concentrations. Primary productivity has declined in the lakes due to the decrease in algae. The increased water clarity due to the mussels has also led to an increase in submerged aquatic vegetation. Comparing water quality metrics in Lake Superior to the lower lakes is insightful because Lake Superior is the largest and most northern of the five Great Lakes and to date has not been affected by the invasive mussels and can thus be considered a control. In contrast, Lake Erie, the most southern and shallow of the Laurentian Great Lakes, is heavily influenced by agricultural practices (i.e., nutrient runoff) and climate change, which directly influence the annual extent of HABs in the Western Basin of that lake.

scholarly journals A Review of the Artificial Neural Network Models for Water Quality Prediction

2020 ◽  
Vol 10 (17) ◽  
pp. 5776 ◽  
Author(s):  
Yingyi Chen ◽  
Lihua Song ◽  
Yeqi Liu ◽  
Ling Yang ◽  
Daoliang Li
Keyword(s):  
Water Quality ◽  
Wastewater Treatment Plants ◽  
Rapid Development ◽  
Network Models ◽  
Quality Prediction ◽  
Neural Network Models ◽  
Water Quality Prediction ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Models

Water quality prediction plays an important role in environmental monitoring, ecosystem sustainability, and aquaculture. Traditional prediction methods cannot capture the nonlinear and non-stationarity of water quality well. In recent years, the rapid development of artificial neural networks (ANNs) has made them a hotspot in water quality prediction. We have conducted extensive investigation and analysis on ANN-based water quality prediction from three aspects, namely feedforward, recurrent, and hybrid architectures. Based on 151 papers published from 2008 to 2019, 23 types of water quality variables were highlighted. The variables were primarily collected by the sensor, followed by specialist experimental equipment, such as a UV-visible photometer, as there is no mature sensor for measurement at present. Five different output strategies, namely Univariate-Input-Itself-Output, Univariate-Input-Other-Output, Multivariate-Input-Other(multi), Multivariate-Input-Itself-Other-Output, and Multivariate-Input-Itself-Other (multi)-Output, are summarized. From results of the review, it can be concluded that the ANN models are capable of dealing with different modeling problems in rivers, lakes, reservoirs, wastewater treatment plants (WWTPs), groundwater, ponds, and streams. The results of many of the review articles are useful to researchers in prediction and similar fields. Several new architectures presented in the study, such as recurrent and hybrid structures, are able to improve the modeling quality of future development.

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