Use of artificial neural network for predicting effluent quality parameters and enabling wastewater reuse for climate change resilience – A case from Jordan

2021 ◽  
Vol 44 ◽  
pp. 102423
Author(s):  
Ziad Al-Ghazawi ◽  
Rami Alawneh
Keyword(s):  
Neural Network ◽  
Climate Change ◽  
Artificial Neural Network ◽  
Wastewater Reuse ◽  
Quality Parameters ◽  
Effluent Quality ◽  
Artificial Neural ◽  
Climate Change Resilience

Water Quality Forecast Based on BP-Artificial Neural Network Model in Qiantang River

2014 ◽  
Vol 668-669 ◽  
pp. 994-998
Author(s):  
Jin Ting Ding ◽  
Jie He
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Ph Value ◽  
Back Propagation ◽  
Ammonia Nitrogen ◽  
Quality Parameters ◽  
Ann Model ◽  
Qiantang River ◽  
Artificial Neural

This study aims at providing a back propagation-artificial neural network (BP-ANN) model on forecasting the water quality change trend of Qiantang River basin. To achieve this goal, a three-layer (one input layer, one hidden layer, and one output layer) BP-ANN with the LM regularization training algorithm was used. Water quality variables such as pH value, dissolved oxygen, permanganate index and ammonia-nitrogen was selected as the input data to obtain the output of the neural network. The ANN structure with 17 hidden neurons obtained the best selection. The comparison between the original measured and forecast values of the ANN model shows that the relative errors, with a few exceptions, were lower than 9%. The results indicated that the BP neural network can be satisfactorily applied to forecast precise water quality parameters and is suitable for pre-alarm of water quality trend.

scholarly journals The effect of climate change on cholera disease: The road ahead using artificial neural network

PLoS ONE ◽  
2019 ◽  
Vol 14 (11) ◽  
pp. e0224813 ◽  
Author(s):  
Zahra Asadgol ◽  
Hamed Mohammadi ◽  
Majid Kermani ◽  
Alireza Badirzadeh ◽  
Mitra Gholami
Keyword(s):  
Neural Network ◽  
Climate Change ◽  
Artificial Neural Network ◽  
The Road ◽  
Artificial Neural

scholarly journals Prediction of Groundwater Quality Using Artificial Neural Network

2021 ◽  
Author(s):  
Rita Maria Joseph ◽  
Alna D Manjaly ◽  
Sreeram Unni ◽  
Able E C ◽  
Vinitha Sharon
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Groundwater Quality ◽  
Quality Criterion ◽  
Quality Parameters ◽  
Total Hardness ◽  
Target Values ◽  
Input Parameters ◽  
Artificial Neural

Assessment and prediction of water quality is a vital tool for the management of water resources systems. It is necessitous for human existence, agriculture and industry. This project delves into the prediction of groundwater quality parameters and groundwater quality criterion based on the Artificial Neural Network Modelling with the study area as Kerala, a state of India. Two models were developed. The first model employs the water quality parameters such as pH, electrical conductivity, total hardness as the input parameters and calcium, magnesium, chloride, fluoride, nitrate concentration as the output parameters. The second model was designed by giving input as, input values and the predicted output values of the first model, and groundwater quality criterion corresponding to each location as the target values. The output qualitative parameters were estimated and compared with the measured values, to evaluate the influence of key input parameters. The number of neurons to be given in the hidden layer was decided by the trial-and-error method. Data of 506 water samples from all over Kerala were collected for modelling.

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