Implementing Machine Learning Algorithms for Water Quality Event Detection: Theory and Practice

2013 ◽  
pp. 107-122 ◽  
Author(s):  
Eyal Brill
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Event Detection ◽  
Learning Algorithms ◽  
Detection Theory ◽  
Machine Learning Algorithms ◽  
Theory And Practice

Prediction on water quality of a lake in Chennai, India using machine learning algorithms

2021 ◽  
Vol 218 ◽  
pp. 44-51
Author(s):  
D. Venkata Vara Prasad ◽  
Lokeswari Y. Venkataramana ◽  
P. Senthil Kumar ◽  
G. Prasannamedha ◽  
K. Soumya ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Learning Algorithms ◽  
Machine Learning Algorithms

Improving prediction of water quality indices using novel hybrid machine-learning algorithms

2020 ◽  
Vol 721 ◽  
pp. 137612 ◽  
Author(s):  
Duie Tien Bui ◽  
Khabat Khosravi ◽  
John Tiefenbacher ◽  
Hoang Nguyen ◽  
Nerantzis Kazakis
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Quality Indices ◽  
Water Quality Indices ◽  
Hybrid Machine

scholarly journals Prediction of groundwater quality indices using machine learning algorithms

2021 ◽  
Author(s):  
Hemant Raheja ◽  
Arun Goel ◽  
Mahesh Pal
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Water Quality Index ◽  
Quality Index ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Quality Indices ◽  
Extreme Gradient Boosting ◽  
Analysis Of Results

Abstract The present paper deals with performance evaluation of application of three machine learning algorithms such as Deep neural network (DNN), Gradient boosting machine (GBM) and Extreme gradient boosting (XGBoost) to evaluate the ground water indices over a study area of Haryana state (India). To investigate the applicability of these models, two water quality indices namely Entropy Water Quality Index (EWQI) and Water Quality Index (WQI) are employed in the present study. Analysis of results demonstrated that DNN has exhibited comparatively lower error values and it performed better in the prediction of both indices i.e. EWQI and WQI. The values of Correlation Coefficient (CC = 0.989), Root Mean Square Error (RMSE = 0.037), Nash–Sutcliffe efficiency (NSE = 0.995), Index of agreement (d = 0.999) for EWQI and CC = 0.975, RMSE = 0.055, NSE = 0.991, d = 0.998 for WQI have been obtained. From variable importance of input parameters, the Electrical conductivity (EC) was observed to be most significant and ‘pH’ was least significant parameter in predictions of EWQI and WQI using these three models. It is envisaged that the results of study can be used to righteously predict EWQI and WQI of groundwater to decide its potability.

scholarly journals Machine learning algorithms for event detection

2010 ◽  
Vol 79 (3) ◽  
pp. 257-259 ◽  
Author(s):  
Dragos Margineantu ◽  
Weng-Keen Wong ◽  
Denver Dash
Keyword(s):  
Machine Learning ◽  
Event Detection ◽  
Learning Algorithms ◽  
Machine Learning Algorithms

scholarly journals Efficient Prediction of Water Quality Index (WQI) Using Machine Learning Algorithms

2021 ◽  
Author(s):  
Md. Mehedi Hassan ◽  
Md. Mahedi Hassan ◽  
Laboni Akter ◽  
Md. Mushfiqur Rahman ◽  
Sadika Zaman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Water Quality Index ◽  
Quality Index ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Efficient Prediction
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