Prof. Huichun Zhang
Case Western Reserve University, USA
Abstract: Machine learning (ML) has revolutionized the field of environmental systems modeling by delivering enhanced performance and leveraging diverse input features. However, to ensure the development of robust and meaningful ML models, it is crucial to integrate expert knowledge into the process, particularly in feature selection and post-model interpretation. This presentation aims to illustrate these principles through two compelling examples. In the first case study, we conducted an extensive literature review and compiled a large dataset encompassing the chlorophyll-a index as the output variable. By employing a novel combination of riverine and meteorological features as inputs, we constructed machine learning-based classification and regression models to predict bloom occurrences in Lake Erie. In the second example, we concentrated on developing predictive models for the abiotic reduction of various organic compounds with diverse reducible functional groups, along with the ten most common inorganic compounds, using different Fe(II)-based reductants. To validate these models, we compared the predictions across different chemical groups, reductant identities, and reaction conditions with the known reduction mechanisms. This rigorous evaluation process allowed us to demonstrate the efficacy of our models and their alignment with established scientific principles. In summary, our approach combines expert knowledge, meticulous feature selection, and thorough model interpretation to advance ML modeling in the environmental field.
Host：Prof. Cheng Gu
Executive Editor
Nanjing University
Time：09:00am August 17, 2023 (Beijing time)
Zoom ID: 816 9975 7155
Bilibili: 25002335
