Corrigendum to “Improving prediction of water quality indices using novel hybrid machine-learning algorithms” [Sci. Total Environ. 715 (2020) 136836] and “Enhancing nitrate and strontium concentration prediction in groundwater by using new data mining algorithm” [Sci. Total Environ. 721 (2020) 137612]

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.

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PM2.5 Concentration Prediction for Air Pollution using Machine Learning Algorithms

Menoufia Journal of Electronic Engineering Research ◽

10.21608/mjeer.2019.67375 ◽

2019 ◽

Vol 28 (1) ◽

pp. 349-354 ◽

Cited By ~ 1

Author(s):

Ahmed Samy Abd El Aziz Moursi ◽

Marwa Shouman ◽

Ezz El-din Hemdan ◽

Nawal El-Fishawy

Keyword(s):

Machine Learning ◽

Air Pollution ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Concentration Prediction ◽

Pm2.5 Concentration

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

Journal of Environmental Management ◽

10.1016/j.jenvman.2021.113086 ◽

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

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Toward a Progress Indicator for Machine Learning Model Building and Data Mining Algorithm Execution

ACM SIGKDD Explorations Newsletter ◽

10.1145/3166054.3166057 ◽

2017 ◽

Vol 19 (2) ◽

pp. 13-24 ◽

Cited By ~ 3

Author(s):

Gang Luo

Keyword(s):

Machine Learning ◽

Data Mining ◽

Model Building ◽

Learning Model ◽

Data Mining Algorithm ◽

Mining Algorithm ◽

Machine Learning Model

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

Fuel ◽

10.1016/j.fuel.2021.121872 ◽

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

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Predicting Student Failure in University Examination using Machine Learning Algorithms

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.e2643.039520 ◽

2020 ◽

Vol 9 (5) ◽

pp. 956-959

Keyword(s):

Machine Learning ◽

Data Mining ◽

Performance Management ◽

Student Performance ◽

Learning Algorithms ◽

Educational Data Mining ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Social Characteristics ◽

Student Failure

Student Performance Management is one of the key pillars of the higher education institutions since it directly impacts the student’s career prospects and college rankings. This paper follows the path of learning analytics and educational data mining by applying machine learning techniques in student data for identifying students who are at the more likely to fail in the university examinations and thus providing needed interventions for improved student performance. The Paper uses data mining approach with 10 fold cross validation to classify students based on predictors which are demographic and social characteristics of the students. This paper compares five popular machine learning algorithms Rep Tree, Jrip, Random Forest, Random Tree, Naive Bayes algorithms based on overall classifier accuracy as well as other class specific indicators i.e. precision, recall, f-measure. Results proved that Rep tree algorithm outperformed other machine learning algorithms in classifying students who are at more likely to fail in the examinations.

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