Predicting Urban Water Quality with Ubiquitous Data

TL;DR

This paper presents a data-driven approach to predict urban water quality using multi-source data and a multi-task multi-view learning model, improving prediction accuracy and understanding influential factors.

Contribution

It introduces a novel multi-task multi-view learning method that fuses diverse urban data sources for water quality prediction, demonstrating its effectiveness on real-world datasets.

Findings

The method outperforms baseline models in prediction accuracy.

Multiple data sources improve water quality forecasting.

Identified key factors influencing urban water quality.

Abstract

Urban water quality is of great importance to our daily lives. Prediction of urban water quality help control water pollution and protect human health. However, predicting the urban water quality is a challenging task since the water quality varies in urban spaces non-linearly and depends on multiple factors, such as meteorology, water usage patterns, and land uses. In this work, we forecast the water quality of a station over the next few hours from a data-driven perspective, using the water quality data and water hydraulic data reported by existing monitor stations and a variety of data sources we observed in the city, such as meteorology, pipe networks, structure of road networks, and point of interests (POIs). First, we identify the influential factors that affect the urban water quality via extensive experiments. Second, we present a multi-task multi-view learning method to fuse those multiple datasets from different domains into an unified learning model. We evaluate our method with real-world datasets, and the extensive experiments verify the advantages of our method over other baselines and demonstrate the effectiveness of our approach.