Big <scp>S</scp>hip <scp>D</scp>ata: Using vessel measurements to improve estimates of temperature and wind speed on the <scp>G</scp>reat <scp>L</scp>akes

Fries, Kevin; Kerkez, Branko

doi:10.1002/2016wr020084

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…GP have been used byFries et al to improve the accuracy of hydrological models[Fries and Kerkez 2017], and Troutman et al have used GP for identifying the contribution of wastewater flows in combined sewer systems[Troutman et al 2017]. …”

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confidence: 99%

Shaping Streamflow Using a Real-Time Stormwater Control Network

Mullapudi

Bartos

Wong

et al. 2018

Sensors

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“Smart” water systems are transforming the field of stormwater management by enabling real-time monitoring and control of previously static infrastructure. While the localized benefits of active control are well-established, the potential for system-scale control of watersheds is poorly understood. This study shows how a real-world smart stormwater system can be leveraged to shape streamflow within an urban watershed. Specifically, we coordinate releases from two internet-controlled stormwater basins to achieve desired control objectives downstream—such as maintaining the flow at a set-point, and generating interleaved waves. In the first part of the study, we describe the construction of the control network using a low-cost, open-source hardware stack and a cloud-based controller scheduling application. Next, we characterize the system’s control capabilities by determining the travel times, decay times, and magnitudes of various waves released from the upstream retention basins. With this characterization in hand, we use the system to generate two desired responses at a critical downstream junction. First, we generate a set-point hydrograph, in which flow is maintained at an approximately constant rate. Next, we generate a series of overlapping and interleaved waves using timed releases from both retention basins. We discuss how these control strategies can be used to stabilize flows, thereby mitigating streambed erosion and reducing contaminant loads into downstream waterbodies.

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confidence: 99%

Shaping Streamflow Using a Real-Time Stormwater Control Network

Mullapudi

Bartos

Wong

et al. 2018

Sensors

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“…This paper seeks to fill this gap. Our work echoes and draws from recent studies such as Fries and Kerkez (2017) and Mazzoleni et al (2015Mazzoleni et al ( , 2017Mazzoleni et al ( , 2018 on merging crowdsourced data with existing environmental monitoring or modeling data.…”

Section: Introductionmentioning

confidence: 73%

Fast Bayesian Regression Kriging Method for Real‐Time Merging of Radar, Rain Gauge, and Crowdsourced Rainfall Data

Yang

2019

Water Resources Research

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Crowdsourcing of rainfall measurements incorporating common citizens as a rich source of data is an emerging concept with huge potential to provide valuable high spatiotemporal resolution rainfall observations. Here we investigate 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 the FBRK method lies in its ability to capture the differences between rain gauge and crowdsourced measurement errors. Another lies in its fast yet reasonably accurate approximation of the Bayesian posterior, making it suitable to use in real time. We conduct synthetic 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 storm water flow forecasting model. In both aspects, we observe the FBRK method to lead to more accurate results and truer representations of the associated uncertainties. However, we also observe the performance of the FBRK method to be sensitive to the choice of the Bayesian prior under certain conditions. Finally, from the synthetic simulations, we find merging crowdsourced data with traditional data to lead to more accurate estimation of the ground truth rainfall field and, subsequently, more accurate flow forecasts (though only when an adequate merging method, e.g., the FBRK method, is used), and the results to be fairly robust to bias in the input crowdsourced data.

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“…Crowdsourcing in our study context is the obtaining of data through public sensors, social media, and the Internet of Things (e.g., smartphones, cameras, traffic lights, and moving cars). It has the ability to provide high spatio-temporal resolution data efficiently and economically and, hence, offers immense potential for addressing the problem of data availability in related areas of research and management as has been demonstrated by a number of studies (Fries & Kerkez, 2017;Rabiei et al, 2013;Yang & Ng, 2017). In recent years, crowdsourcing has been applied in many areas, including meteorology (Overeem et al, 2016), hydrology (Mazzoleni et al, 2015(Mazzoleni et al, , 2017, environment (Breuer et al, 2015;Zheng et al, 2017), and geography (Su et al, 2017;Zook, 2017).…”

Section: Introductionmentioning

confidence: 99%

Reward‐Based Participant Management for Crowdsourcing Rainfall Monitoring: An Agent‐Based Model Simulation

Yang

Cai

2019

Water Resources Research

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Crowdsourcing incorporates common citizens as rich sources of data and is promising for environmental monitoring. In this paper, we propose and test the idea of incorporating incentives to crowdsourcing management for rainfall monitoring. Specifically, we model the allocation of incentives (quantitatively measurable and limited rewards) among crowdsourcing participants for a theoretical rainfall monitoring case. For this purpose, we develop an integrated model comprising a reward allocation component to represent the decision‐making process of a central manager, an agent‐based model to simulate the interactions between the manager and participants, and a rainfall simulation model to evaluate the effectiveness of various reward allocation policies. We simulate six reward allocation policies of varying levels of administrative cost, and consideration of participant and rainfall spatial heterogeneities. The results suggest the performance of each policy to improve with the reward budget and their spatial uniformity. Among the six policies tested, we find that the participant density weighted maximum participation policy yields the most accurate estimation of rainfall intensity due to its more explicit consideration of the spatial distribution of participants; however, this policy associates with a high administrative cost. This highlights the trade‐off between performance and cost in designing effective reward allocation policies. This paper provides a physical and behavior simulation modeling tool to study the feasibility and complexity of reward‐based participant management for crowdsourcing rainfall monitoring. The proposed crowdsourcing method is beneficial for a wide range of applications that require rainfall data with fine resolution, such as storm water management and water availability and biomass assessment for food and energy crops.

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Big Ship Data: Using vessel measurements to improve estimates of temperature and wind speed on the Great Lakes

Cited by 5 publications

References 22 publications

Shaping Streamflow Using a Real-Time Stormwater Control Network

Shaping Streamflow Using a Real-Time Stormwater Control Network

Fast Bayesian Regression Kriging Method for Real‐Time Merging of Radar, Rain Gauge, and Crowdsourced Rainfall Data

Reward‐Based Participant Management for Crowdsourcing Rainfall Monitoring: An Agent‐Based Model Simulation

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