Predicting stream water quality under different urban development pattern scenarios with an interpretable machine learning approach

2021 ◽  
Vol 761 ◽  
pp. 144057
Author(s):  
Runzi Wang ◽  
Jun-Hyun Kim ◽  
Ming-Han Li
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Urban Development ◽  
Stream Water ◽  
Development Pattern ◽  
Learning Approach ◽  
Stream Water Quality ◽  
Interpretable Machine Learning ◽  
Machine Learning Approach

Machine learning approach to investigate the influence of water quality on aquatic livestock in freshwater ponds

2021 ◽  
Vol 208 ◽  
pp. 164-175
Author(s):  
Mashud Rana ◽  
Ashfaqur Rahman ◽  
Joel Dabrowski ◽  
Stuart Arnold ◽  
John McCulloch ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Learning Approach ◽  
Freshwater Ponds ◽  
Machine Learning Approach ◽  
Influence Of Water

Interpretable Machine Learning for COVID-19 Diagnosis Through Clinical Variables

2020 ◽  
Author(s):  
Lucas M. Thimoteo ◽  
Marley M. Vellasco ◽  
Jorge M. do Amaral ◽  
Karla Figueiredo ◽  
Cátia Lie Yokoyama ◽  
...  
Keyword(s):  
Machine Learning ◽  
Linear Model ◽  
Linear Models ◽  
Learning Approach ◽  
Clinical Variables ◽  
Interpretable Machine Learning ◽  
Machine Learning Approach ◽  
Feature Importance ◽  
Non Linear ◽  
The Difference

This work proposes an interpretable machine learning approach to diagnosesuspected COVID-19 cases based on clinical variables. Results obtained for the proposed models have F-2 measure superior to 0.80 and accuracy superior to 0.85. Interpretation of the linear model feature importance brought insights about the most relevant features. Shapley Additive Explanations were used in the non-linear models. They were able to show the difference between positive and negative patients as well as offer a global interpretability sense of the models.

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