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.

Application of stacking hybrid machine learning algorithms in delineating multi-type flooding in Bangladesh

2021 ◽  
Vol 295 ◽  
pp. 113086
Author(s):  
Mahfuzur Rahman ◽  
Ningsheng Chen ◽  
Ahmed Elbeltagi ◽  
Md Monirul Islam ◽  
Mehtab Alam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Hybrid Machine

Robust hybrid machine learning algorithms for gas flow rates prediction through wellhead chokes in gas condensate fields

Fuel ◽  
2022 ◽  
Vol 308 ◽  
pp. 121872
Author(s):  
Abouzar Rajabi Behesht Abad ◽  
Hamzeh Ghorbani ◽  
Nima Mohamadian ◽  
Shadfar Davoodi ◽  
Mohammad Mehrad ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gas Flow ◽  
Learning Algorithms ◽  
Gas Condensate ◽  
Machine Learning Algorithms ◽  
Flow Rates ◽  
Hybrid Machine

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

Hybrid machine learning algorithms to predict condensate viscosity in the near wellbore regions of gas condensate reservoirs

2021 ◽  
pp. 104210
Author(s):  
Abouzar Rajabi Behesht Abad ◽  
Seyedmohammadvahid Mousavi ◽  
Nima Mohamadian ◽  
David A. Wood ◽  
Hamzeh Ghorbani ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Gas Condensate ◽  
Machine Learning Algorithms ◽  
Gas Condensate Reservoirs ◽  
Hybrid Machine

Hybrid machine learning algorithms to enhance lost-circulation prediction and management in the Marun oil field

2021 ◽  
Vol 198 ◽  
pp. 108125
Author(s):  
Mohammad Sabah ◽  
Mohammad Mehrad ◽  
Seyed Babak Ashrafi ◽  
David A. Wood ◽  
Shadi Fathi
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Oil Field ◽  
Machine Learning Algorithms ◽  
Lost Circulation ◽  
Hybrid Machine
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