The Center for Resilient and Sustainable Communities

Groundwater Level Prediction with Machine Learning to Support Sustainable Irrigation in Water Scarcity Regions

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Wanru Li, Mekuanent Muluneh Finsa, Kathryn Blackmond Laskey, Paul Houser, and Rupert Douglas-Bate

Predicting groundwater levels is challenging, especially in regions of water scarcity where data availability is often limited. However, these regions have substantial water needs and require cost-effective groundwater utilization strategies. This study uses artificial intelligence to predict groundwater levels to provide guidance for drilling shallow boreholes for subsistence irrigation. The Bilate watershed, located 80 km north of Arba Minch in southern Ethiopia and covering just over 5250 km2, was selected as the study area. Bilate is typical of areas in Africa with high demand for water and limited availability of well data. Using a non-time series database of 75 boreholes, machine learning models, including multiple linear regression, multivariate adaptive regression splines, artificial neural networks, random forest regression, and gradient boosting regression (GBR), were constructed to predict the depth to the water table. The study considered 20 independent variables, including elevation, soil type, and seasonal data (spanning three seasons) for precipitation, specific humidity, wind speed, land surface temperature during day and night, and Normalized Difference Vegetation Index (NDVI). GBR performed the best of the approaches, with an average 0.77 R-squared value and a 19 m median absolute error on testing data. Finally, a map of predicted water levels in the Bilate watershed was created based on the best model, with water levels ranging from 1.6 to 245.9 m. With the limited set of borehole data, the results show a clear signal that can provide guidance for borehole drilling decisions for sustainable irrigation with additional implications for drinking water.

 

Click here to read the full research article.

Bridging the digital divide for Native American tribes: Roadblocks to broadband and community resilience

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Karina V. Korostelina, Jocelyn Barrett

Native American reservations are faced with a growing need for High-speed Internet and broadband access but face a variety of barriers to broadband infrastructure deployment. This paper discusses the difficulties tribal nations have faced in developing their education, economy, and access to healthcare and public safety due to the roadblocks in building this infrastructure within their tribes. Using case studies and interviews from over 30 Native American tribes, we reveal the shift of responsibility from federal institutions to Native American communities, stressing a tendency to downplay structural factors of exclusion and inequality affecting tribes’ resilience practices. Our article advances the understanding of external and internal factors of roadblocks that Native American tribes have not yet been able to overcome, stemming from limitations of support by state and government institutions, limited capacities and knowledge among tribal members, and complex terrain that also has a sacred value to tribes. Results indicate that resilient communities can produce and sustain practices to overcome internal obstacles, during which resilience practices continue expanding the community’s capacity to develop its digital systems and leadership.

Click here to read the full research article.

Summer 2023 GRF Seminar Recap

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Summer 2023 GRF Seminar Recap

Wei Dai: Offline Simulation Online Learning in Decision Support Analytic

The work is a continuation of the previous multi-fidelity framework, designed to enhance the efficiency of selecting the best design from a vast array of alternative decisions. This proposed framework is particularly relevant in scenarios where performance estimation relies on costly and time-consuming high-fidelity simulations. The integration of low-cost, cheaper low-fidelity information can efficiently aid in optimization.

The greatest novelty of the proposed algorithm, ‘Offline Simulation Online Learning (OSOL),’ lies in its online update feature. By utilizing partial data or information at hand, we gradually learn from the data, leading to more efficient and accurate decision-making processes, especially in complex designs. We have theoretically demonstrated that this update is expected to achieve the same effects as the previous framework. Meanwhile, by training low-fidelity models offline and updating high-fidelity models online, the computational intensity typically associated with high-fidelity simulations can be significantly mitigated. Another advantage of the proposed algorithm is that its online component allows for a more flexible and dynamic response to changing data. This timely update capability is crucial in rapidly changing environments or where continual improvement or monitoring of the model is necessary.

The proposed algorithm has broad applications in fields where simulation plays a critical role in system design and optimization. Currently, the proposed algorithm performs well in several test cases we have experimented with.


Mohammadreza Torkjazi : A Resilience-Based Data-Driven Methodology For Analyzing System Of Autonomous Systems 

Artificial Intelligence and Machine Learning (AI/ML) are equipping constituent systems in System of Systems (SoS) with real-time learning and autonomous decision-making capabilities where the resulting system becomes a System of Autonomous Systems (SoAS). There are major Systems Engineering challenges for realization of SoAS, including evaluating the architecture and making decisions on the suitable Level of Autonomy (LoA) for each constituent system and understanding the SoAS-level emergent behaviors resulting from various LoAs and their interactions at the system level. In this project, Mr. Torkjazi and Dr. Raz  leverage innovative methods such as ML and Bayesian Networks (BN) and propose a data-driven methodology based on the resilience concept to:

  1. Identify the critical systems that are necessary to achieve the desired SoAS performance metrics ,
  2. Find the upper and lower limits of the performance measures of critical systems , and
  3. Visualize the causal relationships between critical performance metrics that are important for examining various scenarios in SoAS evaluation.

In conclusion, the level of autonomy in constituent systems is increasing because of AI/ML resulting in SoAS. There is a need to evaluate SoAS architecture and make decision on LoAs and understand the SoAS-level emergent behaviors in order to prevent them. This project proposed a data-driven methodology that leverages ML and BN to provide visualized analysis of SoAS while considering interactions between constituent systems.


Jocelyn Barrett: Enhancing Emergency Communications Resiliency: Effectiveness Through AI

The project is focused on three locations: George Mason University, Fairfax Country, Virginia, and Puerto Rico. At George Mason University, this included preparing to execute  Representative Connolly’s earmark for Enhancing Emergency Communications . In Fairfax County, this included collaboration with Fairfax County first responders to understand their perceptions and needs for AI, introduction of Particip.ai programs, and in Puerto Rico , this included the development of an operational community connectivity hub (CCH) and aims to enhance Internet resiliency through identifying power-comms interactions. Pulling from the discussion with Fairfax Emergency Responders, six critical areas were pinpointed for AI involvement that could bolster effectiveness both within their department and in the community. This included, digital inclusion, education, data management and analytics, management and optimization, multilingualism, and community engagement. In turn, the potential benefits were identified: improved 9-1-1 call handling, integrating real-time public feedback and improving cyber resiliency.

In conclusion, initial scoping, prototyping, testing and installation , training, maintenance and capacity building resulted in the ability to develop  transferable and transportable project capacity.


Ahmad Alghamdi: A Model-Based Systems Engineering Framework For Resilience Architecture in Complex Adaptive Systems 

The interoperability and complexity of today’s system make it more vulnerable to disruption. This study shows how systems architecture can withstand disruption and maintain an acceptable service level. Then a framework for system resilience according to defined attributes based on architecture design patterns is defined. This thesis examines the role of systems architecture in evaluating system resilience in complex adaptive systems and the approach uses Model-Based Systems Engineering (MBSE) to create system architecture and execute the model to ensure capturing emerging behavior in complex adaptive systems. The project examines the role of systems architecture in evaluating system resilience in complex adaptive systems, while the approach uses Model-Based Systems Engineering (MBSE) to create system architecture and execute the model to ensure capturing emerging behavior in complex adaptive systems. In turn, this will aid in creating a framework for evaluating resilience. The research will begin by creating a use case to examine the behavior aspect. As we cannot ignore the dynamic behavior of ACS.


Nischal Newar: Designing a Resilient Infrastructure Learning Game to Evaluate Maintenance Decisions 

Given the fact that  Critical Infrastructure systems (water, transportation, power) are vital for society , the challenges Rising natural disasters and cyber attacks are increasingly worrisome. In turn, this project aims to enhance decision-making through simulation-based educational games and improve human decision-making as players process new data over time. In essence, the goal is to  design a learning game to collect data, evaluate humans’ decisions on restoration/maintenance strategies, and compare these strategies to optimal solutions .

In conclusion,  the Effective Educational Approach: Game-based learning is impactful for students and decision makers, offering interactive and effective learning experiences. This includes:

  • Interconnected Infrastructures: The study highlights the importance of understanding interdependencies between infrastructure systems.
  • Resilience Insights: The study’s aim was to uncover the effects of restoration decisions on network resilience.
  • Awareness Enhancement: Feedback from focus group indicates the success of raising awareness about network resilience evolution post disruptions.
  • Comprehensive Role Play: Participants embraced dual roles as water and road managers, improving their grasp of component interplay.
  • Future Research: Building on the gained insights, the study progresses to a new phase – the Evacuation/Emergency Planning game.

Wanru Li: Modeling Optimal Drilling Location (Modl) Project Briefing 

In Ethiopia, small farmers comprise 95% of all farmers, which includes about 80% of the population. If needed, Ethiopian farmers could take micro-loans to construct wells for irrigation. It is important to note that affordable borehole depth are less than 30 meters. In this study, the research questions include:

  • What are the groundwater table depths and how can we predict them across the entire study area using limited data?
  • How can we quantify groundwater potential within the study area?
  • What factors can impact the groundwater recharge in the study area?
  • What methods can be employed to determine the most suitable locations for drilling?

The overall objectives include supporting better decision making for where to drill shallow boreholes for sustainable irrigation (<30 meters), by subsistence small farmers and to help save time and money, by increasing the rate of successful boreholes drilled. Using publicly available data from NASA, including surface runoff, subsurface runoff, and precipitation, the results estimated the groundwater recharge using surface and subsurface runoff in Rift Valley, Ethiopia.

 

 

STAR-TIDES & C-RASC 16th Annual Capabilities Demo

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The Center for Resilient and Sustainable Communities (C-RASC) is excited to be collaborating with Sharing To Accelerate Research–TransformativeInnovation for Development and Emergency Support (STAR-TIDES) on its 16th Annual Tech Demo at George Mason University in Fairfax, Virginia. The three-day conference is scheduled for April 17-19, 2023 and will feature a number of panels, speakers, and exhibits. The theme of this year’s program is Sustainable Resilience in the Face of Climate Change. Exhibits will include areas such as energy; housing & infrastructure, water, sanitation & hygiene; mobility & transportation; health care and public health. Additionally, companies and organizations that exhibit at George Mason University will be invited to participate in the TIDES follow-on event at the Pentagon Center Courtyard from April 20-21, 2023. For more information and registration for the free and public event, please click here.

 

Bridging the Digital Divide for Native American Tribes: Roadblocks to Broadband and Community Resilience

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C-RASC Member Dr. Karina V. Korostelina and C-RASC Graduate Research Fellow Jocelyn Barrett  have recently published their research article titled “Bridging the digital divide for Native American tribes: Roadblocks to broadband and community resilience.”

As the abstract notes, the paper “discusses the difficulties tribal nations have faced in developing their education, economy, and access to healthcare and public safety due to the roadblocks in building this infrastructure within their tribes.”

Click here to read their research.

Meet C-RASC’s Graduate Research Fellows: Ms. Jocelyn Barrett & Mr. Babak Aslani

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Meet C-RASC’s graduate research fellows: Ms. Jocelyn Barrett and Mr. Babak Aslani. 

Jocelyn Barrett is a doctoral candidate at the Carter School for Peace and Conflict Resolution at George Mason University, where she focuses on Conflict Analysis and Genocide Prevention Studies. Since 2021, Ms. Barrett has served as a graduate research fellow at GMU’s Center for Resilient and Sustainable Communities (C-RASC).

Working with Dr. Katherine Laskey and Dr. Karina Korostelina at C-RASC, Ms. Barrett’s research focuses on empowering Native American communities through access to broadband development. Her work is a result of the many roadblocks in broadband that these communities faced before and during the 2020 COVID-19 pandemic. For reference, the U.S. Government Accountability Office estimates that in 2020, 18% of people living on tribal lands couldn’t access broadband service, compared to 4% of people in non-tribal areas. In the future, Ms. Barrett hopes to expand her research to include additional underserved communities on a global scale.

Outside of her research at C-RASC, Ms. Barrett is the Visiting Scholars Program Coordinator at the United States Holocaust Memorial Museum in Washington, DC and loves to travel.

Babak Aslani is a doctoral candidate at the College of Engineering and Computing at George Mason University, where he focuses on Systems Engineering and Operations Research. His research interests include optimization, evolutionary algorithms, machine learning, and multi-criteria decision-making. Since 2020, Mr. Aslani has served as a graduate research fellow at GMU’s Center for Resilient and Sustainable Communities (C-RASC). 

Working with Dr. Shima Mohebbi at C-RASC, Mr. Aslani’s research is focused on protecting critical infrastructures, such as water and transportation, from natural disasters and cyber-attacks. In essence, his research helps infrastructure management restore the affected systems faster to standard service level. In addition, Mr. Aslani’s work aims to identify and empower vulnerable communities, which are severely impacted by disastrous situations, in efforts to prepare for future incidents.

Outside of his research, Mr. Aslani is a fan of soccer and movies. 

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Get To Know You Series: Dr. Lance Sherry

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Dr. Lance Sherry is a Co-Director at the Center for Resilient and Sustainable Communities and an Associate Professor in the Systems Engineering and Operations Research Department at the College of Engineering and Computing at George Mason University. 

Dr. Sherry brings over 30 years of experience in strategic planning, system engineering, project management, and product commercialization. In the aviation industry, Dr. Sherry held positions as flight-test engineer, flight control engineer, system engineer, lead system architect, program manager, strategic planning and business development. 

Dr., Sherry’s research focuses on climate-related financial risks, climate adaptation modeling, and cost/benefit analysis. Furthermore, Dr. Sherry’s team develops and applies agent-based models, big data analytics, and AI/ML to develop actionable plans and practical solutions. Topics include aircraft contrail mitigation, renewable energy, CO2 recovery, EV charging station planning, and city-wide digital-twins. 

Dr. Sherry has conducted award winning work, published over 100 papers and journal articles, and holds several patents. 

 

Get To Know You Series: Dr. Celso Ferreira

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Dr. Celso Ferreira is a Co-Director of the Center for Resilient and Sustainable Communities and an Associate Professor of Civil, Environmental and Infrastructure Engineering at the Volgenau School of Engineering at George Mason University.

Having experienced extreme weather events in Brazil, his work in investigating and developing solutions for flood related natural hazards has focused largely on coastal communities.

Dr. Ferreira’s work is concentrated on flood hazards from coastal, riverine, and urban environments and includes real time flood forecasting, monitoring storm surges, and supporting incorporation of natural systems into coastal flood defenses.

After several years at Mason, he recently joined the leadership team of C-RASC in efforts to continue and advance much needed research in this transdisciplinary field. As the global community faces the ramifications of climate change, Dr. Ferreira’s contributions on empowering coastal communities elevates the integral mission of C-RASC.

“My work with C-RASC is an opportunity to work in a diverse, interdisciplinary research center focusing on helping the coastal communities most affected by flooding and other extreme weather,” said Dr. Ferreira.

Outside of his professional work, Dr. Ferreira is an avid surfer and enjoys spending time on the beautiful hiking trails of Northern Virginia with his two young children.

 

Celso Ferreira is Assistant Professor, Civil, Environmental & Infrastructure Engineering. Photo by Evan Cantwell/Creative Services/George Mason University