Response of stream macroinvertebrates to changes in salinity and the development of a salinity index

2005 ◽  
Vol 56 (6) ◽  
pp. 825 ◽  
Author(s):  
Nelli Horrigan ◽  
Satish Choy ◽  
Jonathan Marshall ◽  
Friedrich Recknagel
Keyword(s):  
Water Quality ◽  
Network Models ◽  
Macroinvertebrate Communities ◽  
Quality Factors ◽  
Neural Network Models ◽  
Sensitivity Score ◽  
Partial Canonical Correspondence Analysis ◽  
Salinity Increase ◽  
Salinity Index ◽  
Occurrence Patterns

Many streams and wetlands have been affected by increasing salinity, leading to significant changes in flora and fauna. The study investigates relationships between macroinvertebrate taxa and conductivity levels (µS cm−1) in Queensland stream systems. The analysed dataset contained occurrence patterns of frequently found macroinvertebrate taxa from edge (2580 samples) and riffle (1367 samples) habitats collected in spring and autumn over 8 years. Sensitivity analysis with predictive artificial neural network models and the taxon-specific mean conductivity values were used to assign a salinity sensitivity score (SSS) to each taxon (1—very tolerant, 5—tolerant, 10—sensitive). Salinity index (SI) based on the cumulative SSS was proposed as a measurement of change in macroinvertebrate communities caused by salinity increase. Changes in macroinvertebrate communities were observed at relatively low salinities, with SI rapidly decreasing to ~800–1000 µS cm−1 and decreasing further at a slower rate. Natural variability and water quality factors were ruled out as potential primary causes of the observed changes by using partial canonical correspondence analysis and subsets of the data with only good water quality.

scholarly journals Forecasting Water Quality Index in Groundwater Using Artificial Neural Network

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5875
Author(s):  
Monika Kulisz ◽  
Justyna Kujawska ◽  
Bartosz Przysucha ◽  
Wojciech Cel
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Water Quality Index ◽  
Quality Index ◽  
Network Models ◽  
Multiple Regression Model ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Groundwater quality monitoring in the vicinity of drilling sites is crucial for the protection of water resources. Selected physicochemical parameters of waters were marked in the study. The water was collected from 19 wells located close to a shale gas extraction site. The water quality index was determined from the obtained parameters. A secondary objective of the study was to test the capacity of the artificial neural network (ANN) methods to model the water quality index in groundwater. The number of ANN input parameters was optimized and limited to seven, which was derived using a multiple regression model. Subsequently, using the stepwise regression method, models with ever fewer variables were tested. The best parameters were obtained for a network with five input neurons (electrical conductivity, pH as well as calcium, magnesium and sodium ions), in addition to five neurons in the hidden layer. The results showed that the use of the parameters is a convenient approach to modeling water quality index with satisfactory and appropriate accuracy. Artificial neural network methods exhibited the capacity to predict water quality index at the desirable level of accuracy (RMSE = 0.651258, R = 0.9992 and R2 = 0.9984). Neural network models can thus be used to directly predict the quality of groundwater, particularly in industrial areas. This proposed method, using advanced artificial intelligence, can aid in water treatment and management. The novelty of these studies is the use of the ANN network to forecast WQI groundwater in an area in eastern Poland that was not previously studied—in Lublin.

scholarly journals Water Quality Modeling in Reservoirs Using Multivariate Linear Regression and Two Neural Network Models

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Wei-Bo Chen ◽  
Wen-Cheng Liu
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Linear Regression ◽  
Network Models ◽  
Water Quality Modeling ◽  
Neural Network Models ◽  
Anfis Model ◽  
Inference System ◽  
The Neural Network ◽  
Study Results

In this study, two artificial neural network models (i.e., a radial basis function neural network, RBFN, and an adaptive neurofuzzy inference system approach, ANFIS) and a multilinear regression (MLR) model were developed to simulate the DO, TP, Chl a, and SD in the Mingder Reservoir of central Taiwan. The input variables of the neural network and the MLR models were determined using linear regression. The performances were evaluated using the RBFN, ANFIS, and MLR models based on statistical errors, including the mean absolute error, the root mean square error, and the correlation coefficient, computed from the measured and the model-simulated DO, TP, Chl a, and SD values. The results indicate that the performance of the ANFIS model is superior to those of the MLR and RBFN models. The study results show that the neural network using the ANFIS model is suitable for simulating the water quality variables with reasonable accuracy, suggesting that the ANFIS model can be used as a valuable tool for reservoir management in Taiwan.

New approach to use of total coliform test for watershed management

2000 ◽  
Vol 42 (1-2) ◽  
pp. 65-69 ◽  
Author(s):  
G.M. Brion ◽  
H.H. Mao ◽  
S. Lingireddy
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Membrane Filtration ◽  
Surface Water Quality ◽  
Network Models ◽  
Total Coliform ◽  
Neural Network Models ◽  
New Approach ◽  
Viral Indicators ◽  
Reference Index

This study monitored surface water quality around a reservoir for a 2-year period. It was found that the total coliform test could be used in new ways, and in conjunction with other bacterial and viral indicators, to provide valuable information on the sources of fecal inputs and their potential impact on water quality. Two new approaches to the use of total coliforms were developed. Specifically, it was found that atypical colonies (AC) from the total coliform, membrane filtration test were invaluable input parameters for neural network models that could be trained to recognize and predict potentially hazardous fecal sources from agricultural activities. AC counts were also used in conjunction with total coliphage (TP) concentrations to create a reference index relative to domestic sewage to rank the level of fecal contamination at sites within the watershed. Atypical colonies isolated from total coliform tests of surface water samples were further classified with the API 20E system. The classification showed that the heterogeneous group known as atypicals consisted of three main groups of bacteria: modified coliforms, Aeromonas, and a mix of predominantly Vibrio and Samonella.

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.

Statistical assessment and neural network modeling of stream water quality observations of Green River watershed, KY, USA

2019 ◽  
Vol 19 (6) ◽  
pp. 1831-1840 ◽  
Author(s):  
Jagadeesh Anmala ◽  
Turuganti Venkateshwarlu
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Nonlinear Regression ◽  
Regression Models ◽  
Stream Water ◽  
Network Models ◽  
Stream Water Quality ◽  
Neural Network Models ◽  
Green River ◽  
Nonlinear Regression Models

Abstract The measurement and statistical modeling of water quality data are essential to developing a region-based stream-wise database that would be of great use to the EPA's needs. Such a database would also be useful in bio-assessment and in the modeling of processes that are related to riparian vegetation surrounding a water body such as a stream network. With the help of easily measurable data, it would be easier to come up with database-intensive numerical and computer models that explain the stream water quality distribution and biological integrity and predict stream water quality patterns. Statistical assessments of nutrients, stream water metallic and non-metallic pollutants, organic matter, and biological species data are needed to accurately describe the pollutant effects, to quantify health hazards, and in the modeling of water quality and its risk assessment. The study details the results of statistical nonlinear regression and artificial neural network models for Upper Green River watershed, Kentucky, USA. The neural network models predicted the stream water quality parameters with more accuracy than the nonlinear regression models in both training and testing phases. For example, neural network models of pH, conductivity, salinity, total dissolved solids, and dissolved oxygen gave an R2 coefficient close to 1.0 in the testing phase, while the nonlinear regression models resulted in less than 0.6. For other parameters also, neural networks showed better generalization compared with nonlinear regression models.

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.

Method of complex copper-zinc ore typification using neural network models

2020 ◽  
Vol 5 ◽  
pp. 140-147 ◽  
Author(s):  
T.N. Aleksandrova ◽  
◽  
E.K. Ushakov ◽  
A.V. Orlova ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Copper Zinc ◽  
Complex Copper
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