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Urban Water Quality Prediction using Multi-View Learning

Predicting urban water quality is crucial for public health. This study explores multi-task, multi-view learning to predict water quality using various data sources such as pH, chlorine, and more. The methodology involves capturing global and local perspectives for accurate predictions.

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Urban Water Quality Prediction using Multi-View Learning

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  1. Urban Water Quality Prediction based on Multi-task Multi-view Learning Ye Liu, Yu Zheng, Yuxuan Liang, Shuming Liu, David S. Rosenblum https://www.microsoft.com/en-us/research/publication/urban-water-quality-prediction-based-multi-task-multi-view-learning-2/

  2. Urban Water Quality • Urban Water quality is crucial to our life • quality index • Residual Chlorine (RC) • Turbidity • pH • Predicting urban water quality is of great importance to us • Applications Suggestions for replacements Real-time monitoring water pollution alarming

  3. Urban Water Quality • Predicting the Urban Water Quality from Multi-sources Urban Data • Water quality data • pH • residual chlorine • turbidity • Meteorology • Traffic • Hydraulic condition data • flow • pressure • Map data • CAD • GIS • POIs • Road networks • Pipeline attribute data • length • material • pipe age

  4. Challenges • Unknown influential factors that affect water quality • Turbidity • Flow • POIs • ……. • Water quality various over time and location non-linearly • RC - POIs • RC - Turbidity

  5. Solutions • Identifying influencing factors • Flow, turbidity, pH, etc. • Approaching from spatial and temporal perspectives • Multi-views: each station has two views: • spatial view and temporal view • Capturing local information of each station • Approaching from local and global perspectives • Multi-tasks: water quality prediction at each station • Capturing the global correlations among stations

  6. Insight • The urban water is influenced by various factors • direct factors, e.g., usage patterns, pipe structures • indirect factors, e.g., POIs, time • RC - POIs • RC - Turbidity

  7. Overview • The system consists of: • Feature extraction and view construction • Multi-view based prediction (for each station) • Multi-task based prediction (all stations)

  8. Methodology • Multi-task Multi-view Learning • Multi-Views: For each station, there are two views • Spatial view: predictions based on its neighbors • Temporal view: predictions based on its own history • Alignment between two views • Multi-Tasks: • The prediction at each station is a task • All stations do the co-prediction • Alignments among multiple tasks • Formulations:

  9. Evaluations Code Released • Performance comparison among various approaches • Predictive Performance • Model components comparison • Views comparison

  10. Search for “Urban Computing” 搜索“城市计算” Thanks! Yu Zheng yuzheng@microsoft.com Download Urban Air Apps Homepage • Zheng, Y., et al. Urban Computing: concepts, methodologies, and applications. ACM transactions on Intelligent Systems and Technology. • 郑宇. 城市计算概述，武汉大学学报. 2015年1月，40卷第一期 Yu Zheng. Methodologies for Cross-Domain Data Fusion: An Overview. IEEE Transactions on Big Data, 1, 1, 2015.

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