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 winddirections over a large time period in our applicationPrior-knowledgeÞ 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 distributionsELCOM-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

• Obtain a GP of wind for each season• Heuristic algorithm can be used to find optimal locations. (entropy)

• Transformation of Uniform Distribution• Sensor Placement for the Whole Year

• Sensitivity of water quality

Spatial Prediction

Deployment and evaluation