Application of Artificial Neural Networks to Classify Water Quality of the Yellow River

2009 ◽  
pp. 15-23 ◽  
Author(s):  
Li-hua Chen ◽  
Xiao-yun Zhang
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Artificial Neural Networks ◽  
Yellow River ◽  
The Yellow River ◽  
Artificial Neural

Predictive performance modeling of Habesha brewery wastewater treatment plant using artificial neural networks

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Wastewater Treatment ◽  
Artificial Neural Networks ◽  
Performance Modeling ◽  
Predictive Performance ◽  
Mathematical Tool ◽  
Effluent Water ◽  
Artificial Neural

Recently, process control in wastewater treatment plants (WWTPs) is, mostly accomplished through examining the quality of the water effluent and adjusting the processes through the operator’s experience. This practice is inefficient, costly and slow in control response. A better control of WTPs can be achieved by developing a robust mathematical tool for performance prediction. Due to their high accuracy and quite promising application in the field of engineering, Artificial Neural Networks (ANNs) are attracting attention in the domain of WWTP predictive performance modeling. This work focuses on applying ANN with a feed-forward, back propagation learning paradigm to predict the effluent water quality of the Habesha brewery WTP. Data of influent and effluent water quality covering approximately an 11-month period (May 2016 to March 2017) were used to develop, calibrate and validate the models. The study proves that ANN can predict the effluent water quality parameters with a correlation coefficient (R) between the observed and predicted output values reaching up to 0.969. Model architecture of 3-21-3 for pH and TN, and 1-76-1 for COD were selected as optimum topologies for predicting the Habesha Brewery WTP performance. The linear correlation between predicted and target outputs for the optimal model architectures described above were 0.9201 and 0.9692, respectively.

Development of an application to determine quality indexes in rivers through artificial neural networks applied to data from the Bogota river

2017 ◽  
pp. 1041-1053 ◽  
Author(s):  
Luis Enrique Mendez Lopez ◽  
Octavio Jose Salcedo Parra ◽  
Miguel J. Espitia R.
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Artificial Neural Networks ◽  
Water Index ◽  
Quality Of Water ◽  
Artificial Neural ◽  
Quality Indexes

The quality of water in a river is a factor that must be influenced by the system that surrounds it, in this work we try to determine through a historical set of measurements on the Bogota river between years 2008 to 2015 supplied by the Autonomous Regional Corporation of Cundinamarca (CAR). We want to know the variables with the greatest impact on changes in the water index of the ICA and with them to build a model using artificial neural networks to predict water quality indexes in any type of river in the same Bogota river.

An Efficient Hybrid Forecasting Approach for Wind Speed Time Series

2017 ◽  
Vol 7 (9) ◽  
pp. 13
Author(s):  
Bhargavi Munnaluri ◽  
K. Ganesh Reddy
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Speed ◽  
Fossil Fuels ◽  
Renewable Energy Sources ◽  
Support Vector ◽  
Hybrid Forecasting ◽  
Artificial Neural ◽  
Future Utilization

Wind forecasting is one of the best efficient ways to deal with the challenges of wind power generation. Due to the depletion of fossil fuels renewable energy sources plays a major role for the generation of power. For future management and for future utilization of power, we need to predict the wind speed.  In this paper, an efficient hybrid forecasting approach with the combination of Support Vector Machine (SVM) and Artificial Neural Networks(ANN) are proposed to improve the quality of prediction of wind speed. Due to the different parameters of wind, it is difficult to find the accurate prediction value of the wind speed. The proposed hybrid model of forecasting is examined by taking the hourly wind speed of past years data by reducing the prediction error with the help of Mean Square Error by 0.019. The result obtained from the Artificial Neural Networks improves the forecasting quality.

scholarly journals Draw on artificial neural networks to assess and predict water quality

2020 ◽  
Vol 612 ◽  
pp. 012028
Author(s):  
A Fernandes ◽  
H Chaves ◽  
R Lima ◽  
J Neves ◽  
H Vicente
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Artificial Neural Networks ◽  
Artificial Neural

scholarly journals Predicting culturable enterococci exceedances at Escambron Beach, San Juan, Puerto Rico using satellite remote sensing and artificial neural networks

2018 ◽  
Vol 17 (1) ◽  
pp. 137-148
Author(s):  
Abdiel E. Laureano-Rosario ◽  
Andrew P. Duncan ◽  
Erin M. Symonds ◽  
Dragan A. Savic ◽  
Frank E. Muller-Karger
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Puerto Rico ◽  
Artificial Neural Networks ◽  
Dew Point ◽  
San Juan ◽  
Recreational Water ◽  
Recreational Water Quality ◽  
Artificial Neural ◽  
The U.S

Abstract Predicting recreational water quality is key to protecting public health from exposure to wastewater-associated pathogens. It is not feasible to monitor recreational waters for all pathogens; therefore, monitoring programs use fecal indicator bacteria (FIB), such as enterococci, to identify wastewater pollution. Artificial neural networks (ANNs) were used to predict when culturable enterococci concentrations exceeded the U.S. Environmental Protection Agency (U.S. EPA) Recreational Water Quality Criteria (RWQC) at Escambron Beach, San Juan, Puerto Rico. Ten years of culturable enterococci data were analyzed together with satellite-derived sea surface temperature (SST), direct normal irradiance (DNI), turbidity, and dew point, along with local observations of precipitation and mean sea level (MSL). The factors identified as the most relevant for enterococci exceedance predictions based on the U.S. EPA RWQC were DNI, turbidity, cumulative 48 h precipitation, MSL, and SST; they predicted culturable enterococci exceedances with an accuracy of 75% and power greater than 60% based on the Receiving Operating Characteristic curve and F-Measure metrics. Results show the applicability of satellite-derived data and ANNs to predict recreational water quality at Escambron Beach. Future work should incorporate local sanitary survey data to predict risky recreational water conditions and protect human health.

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