Proceedings Abstracts of the Twenty-Fifth International Joint Conference on Artificial Intelligence

Urban Water Quality Prediction Based on Multi-Task Multi-View Learning / 2576
Ye Liu, Yu Zheng, Yuxuan Liang, Shuming Liu, David S. Rosenblum

Urban water quality is of great importance to our daily lives. Prediction of urban water quality help control water pollution and protect human health. In this work, we forecast the water quality of a station over the next few hours, using a multi-task multi-view learning method to fuse multiple datasets from different domains. In particular, our learning model comprises two alignments. The first alignment is the spaio-temporal view alignment, which combines local spatial and temporal information of each station. The second alignment is the prediction alignment among stations, which captures their spatial correlations and performs co-predictions by incorporating these correlations. Extensive experiments on real-world datasets demonstrate the effectiveness of our approach.