Anomaly Detection for Environmental Data Using Machine Learning Regression

6th Annual International Conference on Material Science and Environmental Engineering

材料科学;生态环境科学

Yuan, Fuqing^1 ; Lu, Jinmei^1

Department of Technology and Safety, UiT, Arctic University of Norway, Tromsø

9037, Norway^1

关键词: Environmental data;    Machine learning methods;    Observed data;    Popular supports;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/472/1/012089/pdf
DOI  :  10.1088/1757-899X/472/1/012089

来源: IOP

【 摘 要 】

Environmental data exhibits as huge amount and complex dependency. Utilizing these data to detect anomaly is beneficial to the disaster prevention. Big data approach using the machine learning method has the advantage not requiring the geophysical and geochemical knowledge to detect anomaly. This paper using the popular support vector regression (SVR ) to model the correlation between factors. From the residual of the regression, it develops a statistical method to quantify the extremity of some abnormal observed data. A case study is proposed to demonstrate the developed methods.