Smart Cities Solutions for More Flood Resilient Communities

Carlson, Katie; Chowdhury, Ashif; Kepley, Andrew; Somerville, Ethan; Warshaw, Kevin; Goodall, Jonathan L.

doi:10.1109/sieds.2019.8735625

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…The Measurements entity has a one-to-many relationship with the three other entities, meaning that each value data point can only have one device, variable, and location, while the remaining entities can have many values for each data point in their tables. This ERD was developed by advancing an approach from previous related research [38]. This design of the database allows for easy further advancement and change as additional devices and variables can be more easily incorporated.…”

Section: Relational Database Design and Implementationmentioning

confidence: 99%

A Cloud-Based Data Storage and Visualization Tool for Smart City IoT: Flood Warning as an Example Application

Leal Sobral,

Nelson,

Asmare

et al. 2023

Smart Cities

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Collecting, storing, and providing access to Internet of Things (IoT) data are fundamental tasks to many smart city projects. However, developing and integrating IoT systems is still a significant barrier to entry. In this work, we share insights on the development of cloud data storage and visualization tools for IoT smart city applications using flood warning as an example application. The developed system incorporates scalable, autonomous, and inexpensive features that allow users to monitor real-time environmental conditions, and to create threshold-based alert notifications. Built in Amazon Web Services (AWS), the system leverages serverless technology for sensor data backup, a relational database for data management, and a graphical user interface (GUI) for data visualizations and alerts. A RESTful API allows for easy integration with web-based development environments, such as Jupyter notebooks, for advanced data analysis. The system can ingest data from LoRaWAN sensors deployed using The Things Network (TTN). A cost analysis can support users’ planning and decision-making when deploying the system for different use cases. A proof-of-concept demonstration of the system was built with river and weather sensors deployed in a flood prone suburban watershed in the city of Charlottesville, Virginia.

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Section: Relational Database Design and Implementationmentioning

confidence: 99%

A Cloud-Based Data Storage and Visualization Tool for Smart City IoT: Flood Warning as an Example Application

Leal Sobral,

Nelson,

Asmare

et al. 2023

Smart Cities

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show abstract

“…IoT devices can offer high-resolution temporal and spatial data for climate mitigation and adaptation monitoring. For example, in Norfolk, Virginia, where flooding events are an increasing occurrence, ultrasonic sensors can detect minute changes in the distance between water level and ground to provide real-time flood analysis (Carlson et al, 2019 ). In the electricity sector, networks and sensors can be easily implemented to collect energy readings in situ from PV or wind power systems, sending that data to remote IoT gateways for preprocessing, storage, and transmission to the cloud for monitoring and analysis.…”

Section: Potential Digital Data Collection Solutionsmentioning

confidence: 99%

Next-Generation Digital Ecosystem for Climate Data Mining and Knowledge Discovery: A Review of Digital Data Collection Technologies

et al. 2020

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Climate change has been called "the defining challenge of our age" and yet the global community lacks adequate information to understand whether actions to address it are succeeding or failing to mitigate it. The emergence of technologies such as earth observation (EO) and Internet-of-Things (IoT) promises to provide new advances in data collection for monitoring climate change mitigation, particularly where traditional means of data exploration and analysis, such as government-led statistical census efforts, are costly and time consuming. In this review article, we examine the extent to which digital data technologies, such as EO (e.g., remote sensing satellites, unmanned aerial vehicles or UAVs, generally from space) and IoT (e.g., smart meters, sensors, and actuators, generally from the ground) can address existing gaps that impede efforts to evaluate progress toward global climate change mitigation. We argue that there is underexplored potential for EO and IoT to advance large-scale data generation that can be translated to improve climate change data collection. Finally, we discuss how a system employing digital data collection technologies could leverage advances in distributed ledger technologies to address concerns of transparency, privacy, and data governance.

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“…Flood risk is becoming a major urban challenge. In particular, coastal cities are much more easily affected by the potential impact of climate change, and floods are becoming increasingly frequent and serious [7]. Coastal cities are close to the ocean and are on low-lying terrain.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Learning Technology for Coastal Flood Forecasting in Internet-of-Things-Enabled Smart City

Dai¹,

Tang²,

Zhang

et al. 2021

Int J Comput Intell Syst

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Flooding is becoming a prominent issue in coastal cities, flood forecasting is the key to solving this problem. However, the lack and imbalance of research data and the insufficient performance of the model have led to the complexity and uncontrollability of flood forecasting. To forecast coastal floods accurately and reliably, the Internet of Things technology is used to collect data on floods and flood factors in smart cities. An ensemble learning method based on Bayesian model combination (BMC-EL) is designed to predict flood depth. First, flood intensity classification and K-fold cross-validation are introduced to generate multiple training subsets from the training set to realize uniform sampling and increase the diversity of subsets. Second, the backpropagation neural network (BPNN) and random forest (RF) are used as the base learners to build the prediction model and then import it into training subsets for training purposes. Finally, based on the prediction performance of the base learner in the validation sets, the Bayesian model combination strategy is formulated to integrate and output predicted values. We describe experiments conducted to forecast flood depth 1 h in advance that several machine learning models were trained and tested using real flood data taken from Macao, China. The models include linear regression, support vector machine, BPNN, RF and BMC-EL models. Results prove the accuracy and reliability of the BMC-EL method in flood forecasting for coastal cities.

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Smart Cities Solutions for More Flood Resilient Communities

Cited by 4 publications

References 4 publications

A Cloud-Based Data Storage and Visualization Tool for Smart City IoT: Flood Warning as an Example Application

A Cloud-Based Data Storage and Visualization Tool for Smart City IoT: Flood Warning as an Example Application

Next-Generation Digital Ecosystem for Climate Data Mining and Knowledge Discovery: A Review of Digital Data Collection Technologies

Ensemble Learning Technology for Coastal Flood Forecasting in Internet-of-Things-Enabled Smart City

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