Urban Water Quality Prediction based on Multi-task Multi-view Learning Presented by: Tyler Pietri, Dennis Silva, Michelle Lin

Content Background: Problem & Motivation Methodology Evaluation Conclusion

Background: Problem & Motivation

Background Urban water quality consists of: Physical (debris) Chemical (residual chlorine, turbidity, pH Level) Biological (bacteria, algae) Radiological (iodine-131) Typically, water quality is determined by comparing the physical and chemical characteristics of a water sample with water quality guidelines or standards. Turbidity is the cloudiness or haziness of a fluid caused by large numbers of individual particles that are generally invisible to the naked eye, similar to smoke in air Nuclear power plant meltdown in Japan a few years ago.

Background Water quality issues related to urban development: Urban Runoff (Nitrogen & Phosphorus) Sediment Buildup Population Growth Sewage Overflows Waterborne Pathogens Population Growth: Need more facilities (housing, developments, roads, shopping centers, etc). Disturb land stress water sources Nitrogen and phosphorus can come from fertilizers in heavy rains or storms, can increase aquatic plants/algae growth Storm sewers (storm runoff), sanitary sewers (raw sewage), combined sewers (combination raw and stormwater)

USA equivalent water is about $0.02 on average, although can vary

Background Contaminated water = significant issue 131M people drink contaminated water in USA

Motivation Existing Solutions Fail to address complexity Localized predictions Lack of global applicability Healthy populace Informed policy Urban projects Personal motivation and existing solutions that are problematic

Existing Solutions Can you more accurately predict water quality by assuming water quality is correlated?

Methodology

Data Spatial Heterogeneous Road network Multiple sources Temporal Shenzhen, China Spatial Road network Pipeline network POI Temporal Water quality Weather

Framework

Temporal View - a single feature vector The latest 12 hours temporal data in a station. Time Series Signal. Water quality (RC, Turbidity, pH) Water hydraulic data (flow, pressure) Meteorological features: temperature, humidity, barometer pressure, wind speed, weather

Spatial View Water pipe network structures (length, diameter and age) Road network structures (road segment density, road length) POIs (distribution) k nearest neighbors (water quality and hydraulic characteristics) i.e. Neighbors’ temporal features via the geographical similarity (the sum of top-k shortest paths between stations)

Prediction Model Spatial prediction Temporal prediction W is the linear mapping function for station l Assume both contribute equally, the prediction model

Objective function Considering the least-squares loss function Inherent characteristics of the same node from various aspects This penalty enforces the agreement on the prediction results

Objective function - includes the global impact on a station This Graph Laplacian penalty ensures a small deviation between two nodes that are near in the pipeline system. Sl,m measures the spatial autocorrelation between l and m. If it is large, the penalty will force similarity between l and m.

L2,1-norm of W(the weight matrix over nodes) D: # of features M: # of nodes Group Lasso penalty. It encourages all tasks to select a common set of features and thereby plays the role of group feature selection.

Optimization The objective function can be written as h(W) is smooth, g(W) is non-smooth. Use the Fast Iterative Shrinkage Thresholding Algorithm (FISTA) or Accelerated Gradient Descent.

Model

Evaluation

Evaluation Focus Model Performance RSME Varied prediction intervals Assumption Soundness Single task Multitask

stMTMV vs. Alternatives Model Performance stMTMV = ↓ Error ↑ Interval = ↑ Error Close approximate of known values stMTMV vs. Alternatives stMTMV vs. True Output

stMTMV vs. stMTMV Varients Assumption Validity Varied inputs Inclusive model = best model Proved water quality = interconnected Spatial-temporal element stMTMV vs. stMTMV Varients

Assumption Validity Varied Views Temporal (t) Spatial (s) t+s without alignment t+s with alignment Proved Spatial-temporal element Alignment is necessary stMTMV vs. views

Future Work

Conclusions and Future Work Explore stMTVT model using different prediction functions Nonlinear, convex, etc. Apply model to varied problem (traffic)

Q&A

References Liu, Ye, et al. "Urban water quality prediction based on multi-task multi-view learning." Proceedings of the International Joint Conference on Artificial Intelligence. 2016. "Safe Water Is a Scarce Commodity Worldwide." Statista. N.p., n.d. Web. <https://www.statista.com.ezproxy.wpi.edu/chart/4591/drinking-water-world-water-day/> Wright, Paul. "5 Infographics That Show The Need For Drinking Water Testing." SCIEX. N.p., n.d. Web. <https://sciex.com/community/blogs/blogs/5-infographics-that-show-the-need-for-drinking-water-testing>. Survey, U.S. Geological. "Water Resources of the United States." Water Resources of the United States: U.S. Geological Survey. N.p., n.d.. <https://www2.usgs.gov/water/>.