公开(公告)号：US11488069B2

公开(公告)日：2022-11-01

申请号：US16179993

申请日：2018-11-04

Applicant: National Chung-Shan Institute of Science and Technology

Inventor： Li-Yen Kuo ,  Chih-Lun Liao ,  Chun-Han Tai ,  Hao-Yu Kao

IPC: G06N20/20 ,  G06N7/00 ,  G08B21/12 ,  G06N3/04 ,  G06N20/00 ,  G06N3/08

Abstract: A method for predicting air quality with the aid of machine learning models includes: (A) providing air pollution data to perform an eXtreme Gradient Boosting (XGBoost) regression algorithm for obtaining a XGBoost prediction value; (B) providing the air pollution data to perform a Long Short-Term Memory (LSTM) algorithm for obtaining an LSTM prediction value; (C) combining the air pollution data, the XGBoost prediction value and the LSTM prediction value to generate air pollution combination data; (D) performing an XGBoost classification algorithm to obtain a suggestion for whether to issue an air pollution alert; and (E) performing the XGBoost regression algorithm on the air pollution combination data to obtain an air pollution prediction value. Two layers of machine learning models are built, and a situation where prediction results are too conservative when a single model does not have enough data can be improved.

公开(公告)号：US20190325334A1

公开(公告)日：2019-10-24

申请号：US16179993

申请日：2018-11-04

Applicant: National Chung-Shan Institute of Science and Technology

Inventor： Li-Yen Kuo ,  Chih-Lun Liao ,  Chun-Han Tai ,  Hao-Yu Kao

IPC: G06N7/00 ,  G06N3/04 ,  G08B21/12 ,  G06N99/00

Abstract: A method for predicting air quality with the aid of machine learning models includes: (A) providing air pollution data to perform an eXtreme Gradient Boosting (XGBoost) regression algorithm for obtaining a XGBoost prediction value; (B) providing the air pollution data to perform a Long Short-Term Memory (LSTM) algorithm for obtaining an LSTM prediction value; (C) combining the air pollution data, the XGBoost prediction value and the LSTM prediction value to generate air pollution combination data; (D) performing an XGBoost classification algorithm to obtain a suggestion for whether to issue an air pollution alert; and (E) performing the XGBoost regression algorithm on the air pollution combination data to obtain an air pollution prediction value. Two layers of machine learning models are built, and a situation where prediction results are too conservative when a single model does not have enough data can be improved.