Uncertainty quantification and integration of machine learning techniques for predicting acid rock drainage chemistry: a probability bounds approach.
Acid rock drainage (ARD) is a major pollution problem globally that has adversely impacted the environment. Identification and quantification of uncertainties are integral parts of ARD assessment and risk mitigation, however previous studies on predicting ARD drainage chemistry have not fully addressed issues of uncertainties. In this study, artificial neural networks (ANN) and support vector machine (SVM) are used for the prediction of ARD drainage chemistry and their predictive uncertainties are quantified using probability bounds analysis. Furthermore, the predictions of ANN and SVM are integrated using four aggregation methods to improve their individual predictions. The results of this study showed that ANN performed better than SVM in enveloping the observed concentrations. In addition, integrating the prediction of ANN and SVM using the aggregation methods improved the predictions of individual techniques.
School of Engineering, UBC, Kelowna, BC, Canada. Electronic address: getnet.betrie@ubc.ca.
Getnet D
GD
Betrie
School of Engineering, UBC, Kelowna, BC, Canada.
Rehan
R
Sadiq
Minesite Drainage Assessment Group, Surrey, BC, Canada.
Kevin A
KA
Morin
School of Engineering, UBC, Kelowna, BC, Canada.
Solomon
S
Tesfamariam
D000465:Algorithms; D001185:Artificial Intelligence; D004784:Environmental Monitoring / Q000379:methods*; D008956:Models, Chemical; D016571:Neural Networks, Computer; D011336:Probability; D035501:Uncertainty
Acid rock drainage; Artificial neural network; Machine learning; Support vector machine; Uncertainty analysis