Optimal Sensor Placement and Measurement of Wind for Water Quality Studies in Urban Reservoirs Wan Du, Zikun Xing, Mo Li, Bing sheng He, Lloyd Hock Chye Chua, Haiyan Miao IPSN 2014

Introduction A healthy aquatic ecosystem and water quality monitoring is essential for good understanding of the water resources and social security The distribution of wind stress on the surface of a lake can significantly impact water hydrodynamics and affects water quality. Existing limnological studies

Introduction A limited number of wind sensors =>the wind direction and speed =>derive the wind distribution over the entire Marina reservoir The accurate wind distribution is critical for studying and predicting the water quality.

Introduction Gaussian distribution & Gaussian Process ( 正态分布 ) entropy /mutual information Our study Wind directions do not follow Gaussian distribution over time. Existing approaches require prior knowledge to train GP. The water quality has sensitivity to the wind input at different locations.

Problem statement Divide Marina Reservoir into small grids of 20m*20m. V: all locations. (More than 5k) The observations at each location vi can be modeled as a random variable Xi. A: optimal sensor placement. Common approaches => GP

The GP assumption, however, does not hold for wind directions over a large time period in our application Prior-knowledge  more than 5k monitors Consider the water quality modeling

Approach overview Divide into two monsoon seasons and two intermonsoon seasons In each segment, select optimal sensor locations Combine the results CDF modeling => wind distributions ELCOM-CAEDYM =>quantify the sensitivity of water quality to wind input at different locations

Approach overview

Monsoon based time series segmentation Time series segmentation algorithm Maximum likelihood Result analysis

CFD Modeling Inputs: atmospheric flow topography information of the land surface Cannot provide instant wind distribution

CFD Modeling 16-point compass rose 10 gradually incoming speeds =>160 independent surface wind distributions Divide the historical wind data into 160 segments.

Sensor placement

Spatial Prediction

Deployment and evaluation