Accurate rainfall measurement at high spatial and temporal resolutions is critical for
both research and urban management. With the recent advances in technology, there
emerges a new crowd-sourcing approach, where common citizens use smartphones,
surveillance cameras, and other devices as sensors to measure rainfall intensity. The
crowd-sourcing approach is promising as it has the potential to provide high-density
measurements, but it also associates with relatively large individual errors and
relatively unreliable participants. In this thesis we develop a framework for modeling
the crowd-sourcing approach in rainfall monitoring, and use this modeling framework
to tap into the following issues: (i) the proof of concept of the crowd-sourcing
approach, (ii) the integration of cro...[
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Accurate rainfall measurement at high spatial and temporal resolutions is critical for
both research and urban management. With the recent advances in technology, there
emerges a new crowd-sourcing approach, where common citizens use smartphones,
surveillance cameras, and other devices as sensors to measure rainfall intensity. The
crowd-sourcing approach is promising as it has the potential to provide high-density
measurements, but it also associates with relatively large individual errors and
relatively unreliable participants. In this thesis we develop a framework for modeling
the crowd-sourcing approach in rainfall monitoring, and use this modeling framework
to tap into the following issues: (i) the proof of concept of the crowd-sourcing
approach, (ii) the integration of crowdsourced data with the existing radar and rain
gauge data, and (iii) the optimal management of crowd-sourcing participants.
The main body of this thesis consists of three parts. The first part develops a new
rainfall data crowd-sourcing model, which involves two data types: (i) scattered
observations made by individuals and (ii) fixed-point observations made using fixed
sensors. Using this model, we explore the potential of the crowd-sourcing approach for
urban rainfall monitoring and the subsequent implications for stormwater modeling
through a series of synthetic but realistic simulation experiments. The results show that
even under conservative assumptions, crowdsourced rainfall data lead to more accurate
modeling of stormwater flows as compared to rain gauge data. We also observe the
relative superiority of the crowd-sourcing approach to vary depending on crowd
participation rate, measurement accuracy, drainage area, choice of performance
statistic, and crowdsourced observation type.
The second part investigates the merging of crowdsourced rainfall data with traditional
radar and rain gauge data to maximize their utility. For this purpose, we develop a
tailored Fast Bayesian Regression Kriging (FBRK) method combining regression
kriging and Laplace approximation in a Bayesian framework. A strength of this method
lies is its ability to capture the differences in the errors of the three data types. Another
lies in its fast yet reasonably accurate approximation of the Bayesian posterior, making
it suitable for use in real-time. We conduct computer simulations to evaluate the FBRK
method alongside three other merging methods. In the simulations, we compare the
accuracies of their resulting rainfall estimates, as well as the skill of those estimates as
input to a stormwater flow forecasting model. In both aspects, we observe the FBRK
method to lead to more exact results and truer representations of the associated
uncertainties. We observe also, however, the performance of the FBRK method to be
sensitive to the choice of the Bayesian prior under certain conditions. Finally, we find
merging crowdsourced data with traditional data leads to more accurate estimation of
the ground truth rainfall field and subsequently, more accurate flow forecasts (though
only when an adequate merging method, such as the FBRK method is used), and the
results to be fairly robust to bias in the input crowdsourced data.
The third part proposes and tests the idea of incentive allocation (IA) among crowd-sourcing
participants, where IA refers to the allocation of quantitatively measurable
and limited rewards. We modify our model in the first part to develop an integrated
model for this purpose. The new model consists of a real-time IA component for the
manager, an agent-based model to simulate interactions between the manager and
participants, and a rainfall simulation model to evaluate the performances of different
IA policies. In the model, we generate six different IA policies, and each follows a
specific design principle and associates with a level of administrative cost (AC). These
policies and the integrated model is tested through a theoretical but realistic case study.
Results suggest the performance with each policy to be positively affected by the
incentive budget, the level of environmental education and awareness, and the spatial
uniformity of participants, and the impact of budgets is less significant with the
increase of spatial uniformity of participants. The participant density weighted
maximum participation policy (MDPP) which considers the spatial distribution of
participants performs better than all other policies under almost all investigated
conditions. However, MDPP associates with a high level of AC, which makes the
choice of optimal IA policy a trade-off between performance and AC.
To sum up, this thesis provides direct answers to concerns regarding the feasibility,
data processing, and participant’s reliability of the crowd-sourcing approach for urban
rainfall monitoring. It also offers more quantitative understandings regarding this new
approach, and the models developed here could be used as a framework for future
studies on the application of the crowd-sourcing approach in other research areas.
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