Jiangnan University

Xin Zhang

Wei Fang

Jun Sun

Xiaojun Wu

Hengyang Lu

Shitong Wang

Jian Yao

Runsu Zhu

Yi Gu

Zhihuang Wang

Yizhang Jiang

Aiguo Chen

Chao Fan

Pengjiang Qian

The invention provides a method for constructing a target prediction model in a multicenter small sample scenario and a prediction method. In combination with the idea of transfer learning, a training set of a new node is predicted by directly using knowledge of a trained node, and a prediction error sample is used to reflect a difference between the new node and the trained node. The difference is used as supplementary knowledge. Model knowledge of the new node is quickly acquired, to avoid training of a new node from scratch each time. Finally, parallel integration of incremental subclassifiers is implemented by using a ridge regression method, so that the deployment time and costs are greatly reduced. The generalization of models is ensured through sharing of historical knowledge and a knowledge discarding mechanism, good classification effect can also be achieved for a node with a small sample size.

Method for constructing target prediction model in multicenter small sample scenario and prediction method

The present disclosure discloses a deep spatial-temporal similarity method for air quality prediction, and belongs to the technical field of environmental protection. When the method predicts air quality-related indexes of a target site, a temporal change of air pollution and a spatial diffusion relationship are effectively combined, and then spatial-temporal similarity sites of the target site are selected; air quality monitoring data collected by the target site, the spatial-temporal similarity site of the target site and geographical neighbour sites of the target site and meteorological data are respectively taken as inputs of a long short term memory network (LSTM) model to obtain uncorrelated output results, and then predicted values of air quality-related index data of the target site are obtained in a mode of support vector regression (SVR) integration. The present disclosure effectively combines the temporal change of air pollution with the spatial diffusion relationship, and namely proposes a more efficient way to select more highly relevant data to predict air quality so that a prediction result is more accurate.

Deep spatial-temporal similarity method for air quality prediction

Growing community of inventors

Wuxi, China

Xin Zhang