Coupling centennial-scale shoreline change to sea-level rise and coastal morphology in the Gulf of Mexico using a Bayesian network

© The Author(s), 2016. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Earth's Future 4 (2016): 143–158, doi:10.1002/2015EF000331.Predictions of coastal evolution driven by episodic and persistent processes associated with storms and relative sea-level rise (SLR) are required to test our understanding, evaluate our predictive capability, and to provide guidance for coastal management decisions. Previous work demonstrated that the spatial variability of long-term shoreline change can be predicted using observed SLR rates, tide range, wave height, coastal slope, and a characterization of the geomorphic setting. The shoreline is not sufficient to indicate which processes are important in causing shoreline change, such as overwash that depends on coastal dune elevations. Predicting dune height is intrinsically important to assess future storm vulnerability. Here, we enhance shoreline-change predictions by including dune height as a variable in a statistical modeling approach. Dune height can also be used as an input variable, but it does not improve the shoreline-change prediction skill. Dune-height input does help to reduce prediction uncertainty. That is, by including dune height, the prediction is more precise but not more accurate. Comparing hindcast evaluations, better predictive skill was found when predicting dune height (0.8) compared with shoreline change (0.6). The skill depends on the level of detail of the model and we identify an optimized model that has high skill and minimal overfitting. The predictive model can be implemented with a range of forecast scenarios, and we illustrate the impacts of a higher future sea-level. This scenario shows that the shoreline change becomes increasingly erosional and more uncertain. Predicted dune heights are lower and the dune height uncertainty decreases.This work was supported by the USGS Coastal and Marine Geology Program and the USGS Southeast Regional Assessment Project

Spatial analysis of invasive alien plant distribution patterns and processes using Bayesian network-based data mining techniques

Invasive alien plants have widespread ecological and socioeconomic impacts throughout many parts of the world, including Swaziland where the government declared them a national disaster. Control of these species requires knowledge on the invasion ecology of each species including how they interact with the invaded environment. Species distribution models are vital for providing solutions to such problems including the prediction of their niche and distribution. Various modelling approaches are used for species distribution modelling albeit with limitations resulting from statistical assumptions, implementation and interpretation of outputs. This study explores the usefulness of Bayesian networks (BNs) due their ability to model stochastic, nonlinear inter-causal relationships and uncertainty. Data-driven BNs were used to explore patterns and processes influencing the spatial distribution of 16 priority invasive alien plants in Swaziland. Various BN structure learning algorithms were applied within the Weka software to build models from a set of 170 variables incorporating climatic, anthropogenic, topo-edaphic and landscape factors. While all the BN models produced accurate predictions of alien plant invasion, the globally scored networks, particularly the hill climbing algorithms, performed relatively well. However, when considering the probabilistic outputs, the constraint-based Inferred Causation algorithm which attempts to generate a causal BN structure, performed relatively better. The learned BNs reveal that the main pathways of alien plants into new areas are ruderal areas such as road verges and riverbanks whilst humans and human activity are key driving factors and the main dispersal mechanism. However, the distribution of most of the species is constrained by climate particularly tolerance to very low temperatures and precipitation seasonality. Biotic interactions and/or associations among the species are also prevalent. The findings suggest that most of the species will proliferate by extending their range resulting in the whole country being at risk of further invasion. The ability of BNs to express uncertain, rather complex conditional and probabilistic dependencies and to combine multisource data makes them an attractive technique for species distribution modeling, especially as joint invasive species distribution models (JiSDM). Suggestions for further research are provided including the need for rigorous invasive species monitoring, data stewardship and testing more BN learning algorithms.Environmental SciencesD. Phil. (Environmental Science

Using a Bayesian network to predict barrier island geomorphologic characteristics

© The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Journal of Geophysical Research: Earth Surface 120 (2015): 2452–2475, doi:10.1002/2015JF003671.Quantifying geomorphic variability of coastal environments is important for understanding and describing the vulnerability of coastal topography, infrastructure, and ecosystems to future storms and sea level rise. Here we use a Bayesian network (BN) to test the importance of multiple interactions between barrier island geomorphic variables. This approach models complex interactions and handles uncertainty, which is intrinsic to future sea level rise, storminess, or anthropogenic processes (e.g., beach nourishment and other forms of coastal management). The BN was developed and tested at Assateague Island, Maryland/Virginia, USA, a barrier island with sufficient geomorphic and temporal variability to evaluate our approach. We tested the ability to predict dune height, beach width, and beach height variables using inputs that included longer-term, larger-scale, or external variables (historical shoreline change rates, distances to inlets, barrier width, mean barrier elevation, and anthropogenic modification). Data sets from three different years spanning nearly a decade sampled substantial temporal variability and serve as a proxy for analysis of future conditions. We show that distinct geomorphic conditions are associated with different long-term shoreline change rates and that the most skillful predictions of dune height, beach width, and beach height depend on including multiple input variables simultaneously. The predictive relationships are robust to variations in the amount of input data and to variations in model complexity. The resulting model can be used to evaluate scenarios related to coastal management plans and/or future scenarios where shoreline change rates may differ from those observed historically.U.S. Geological Survey (USGS) Coastal and Marine Geology Program; U.S. Fish and Wildlife Servic

The Design of Resilient Engineering Infrastructure Systems

The concept of resilience has emerged from a number of domains to address how systems, people as well as organisations can handle uncertainty and thereby not only survive hardship, but also thrive and prosper. This is of particular importance for engineering infrastructure systems which, due to the inherently long lifecycles giving rise to many unknowns, need to be designed for resilience such that it not only maintains operations in the face of day-to-day demands, but also continue to be able to evolve for the future. While there has been substantial interest in resilience from both academia and industry, exactly how such systems may be endowed with resilience to address these concerns from an engineering design perspective is less clear. To this end, a literature review was first conducted to compile the definitions and characteristics of resilience across the domains of engineering, organisational management and ecology. The characteristics were found to comprise: absorbing disturbances, adapting for change and thriving for the future. These were then mapped to the engineering design ilities of robustness, adaptability and flexibility before being brought together in a conceptual model to form a strategic view for resilience. Further methods from resilience and engineering design literature were then consulted to understand how this particular view could be modelled and evaluated. This led to the development of a preliminary model using the Least Squares Monte Carlo method adapted for a telecommunications case study. The insights gained from these explorations were then used to drive the synthesis of a novel support method whereby the design for flexibility framework was adapted to include decision modelling with Bayesian Networks and for resilience analysis. Here, resilience is taken to be the maximisation of the system economic lifecycle value under uncertainty, as measured by Expected Net Present Value, through robust and flexible strategies. This was applied to two case studies involving infrastructure systems: the first built upon existing work based on a Waste-to-Energy system in Singapore to verify the new method while the second applied the support method with BT, a multinational telecommunications company based in the UK, to gauge reception of this approach in industry. In both cases, the initial capacity and maximum number of upgrades served as proxies for robustness and flexibility respectively. Results demonstrate that Bayesian Networks are able to model decision rules for flexibility by selecting technology options over time given observations on the system and are also useful for extracting expert domain knowledge. While the construction of Bayesian Networks are subjective, they present an intuitive visualisation of the dependencies in a system and as such, engaged stakeholder interest. Resilience analysis examined the effect of volatility and drift of demand on the design strategies and indeed, there existed a trade-off between robust and flexible strategies. Furthermore, the greater utility of the support method lies in aiding decision makers in exploring the solution space and prompting discussions for what-if scenarios for the organisation.BT Grou

Predicted sea-level rise-driven biogeomorphological changes on Fire Island, New York: implications for people and plovers

© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Zeigler, S. L., Gutierrez, B. T., Lentz, E. E., Plant, N. G., Sturdivant, E. J., & Doran, K. S. Predicted sea-level rise-driven biogeomorphological changes on Fire Island, New York: implications for people and plovers. Earth’s Future, 10(4), (2022): e2021EF002436, https://doi.org/10.1029/2021EF002436.Forecasting biogeomorphological conditions for barrier islands is critical for informing sea-level rise (SLR) planning, including management of coastal development and ecosystems. We combined five probabilistic models to predict SLR-driven changes and their implications on Fire Island, New York, by 2050. We predicted barrier island biogeomorphological conditions, dynamic landcover response, piping plover (Charadrius melodus) habitat availability, and probability of storm overwash under three scenarios of shoreline change (SLC) and compared results to observed 2014/2015 conditions. Scenarios assumed increasing rates of mean SLC from 0 to 4.71 m erosion per year. We observed uncertainty in several morphological predictions (e.g., beach width, dune height), suggesting decreasing confidence that Fire Island will evolve in response to SLR as it has in the past. Where most likely conditions could be determined, models predicted that Fire Island would become flatter, narrower, and more overwash-prone with increasing rates of SLC. Beach ecosystems were predicted to respond dynamically to SLR and migrate with the shoreline, while marshes lost the most area of any landcover type compared to 2014/2015 conditions. Such morphological changes may lead to increased flooding or breaching with coastal storms. However—although modest declines in piping plover habitat were observed with SLC—the dynamic response of beaches, flatter topography, and increased likelihood of overwash suggest storms could promote suitable conditions for nesting piping plovers above what our geomorphology models predict. Therefore, Fire Island may offer a conservation opportunity for coastal species that rely on early successional beach environments if natural overwash processes are encouraged.Funding for this work was provided by the U.S. Geological Survey's Coastal and Marine Hazards and Resources Program, with supplemental funding through the Disaster Relief Act

Bayesian Networks with Expert Elicitation as Applicable to Student Retention in Institutional Research

The application of Bayesian networks within the field of institutional research is explored through the development of a Bayesian network used to predict first- to second-year retention of undergraduates. A hybrid approach to model development is employed, in which formal elicitation of subject-matter expertise is combined with machine learning in designing model structure and specification of model parameters. Subject-matter experts include two academic advisors at a small, private liberal arts college in the southeast, and the data used in machine learning include six years of historical student-related information (i.e., demographic, admissions, academic, and financial) on 1,438 first-year students. Netica 5.12, a software package designed for constructing Bayesian networks, is used for building and validating the model. Evaluation of the resulting model’s predictive capabilities is examined, as well as analyses of sensitivity, internal validity, and model complexity. Additionally, the utility of using Bayesian networks within institutional research and higher education is discussed. The importance of comprehensive evaluation is highlighted, due to the study’s inclusion of an unbalanced data set. Best practices and experiences with expert elicitation are also noted, including recommendations for use of formal elicitation frameworks and careful consideration of operating definitions. Academic preparation and financial need risk profile are identified as key variables related to retention, and the need for enhanced data collection surrounding such variables is also revealed. For example, the experts emphasize study skills as an important predictor of retention while noting the absence of collection of quantitative data related to measuring students’ study skills. Finally, the importance and value of the model development process is stressed, as stakeholders are required to articulate, define, discuss, and evaluate model components, assumptions, and results

Explainable AI models for predicting drop coalescence in microfluidics device

In the field of chemical engineering, understanding the dynamics and probability of drop coalescence is not just an academic pursuit, but a critical requirement for advancing process design by applying energy only where it is needed to build necessary interfacial structures, increasing efficiency towards Net Zero manufacture. This research applies machine learning predictive models to unravel the sophisticated relationships embedded in the experimental data on drop coalescence in a microfluidics device. Through the deployment of SHapley Additive exPlanations values, critical features relevant to coalescence processes are consistently identified. Comprehensive feature ablation tests further delineate the robustness and susceptibility of each model. Furthermore, the incorporation of Local Interpretable Model-agnostic Explanations for local interpretability offers an elucidative perspective, clarifying the intricate decision-making mechanisms inherent to each model’s predictions. As a result, this research provides the relative importance of the features for the outcome of drop interactions. It also underscores the pivotal role of model interpretability in reinforcing confidence in machine learning predictions of complex physical phenomena that are central to chemical engineering applications

Predictive modeling of riverine constituent concentrations and loads using historic and imposed hydrologic conditions

This research was principally concerned with the task of quantifying dissolved and suspended constituents carried in river water when direct measurements are not available. This is a question of scientific and societal relevance, and one with a long history of study and a great deal of remaining difficulty. The traditional approach to estimating these quantities, linear regression models (LMs), suffers from poor flexibility and high subsequent bias in many applications. This research applied semiparametric generalized additive models (GAMs), a more flexible class of regression models, evaluated their performance in various locations and conditions, and applied them in a proactive modeling effort in a major water-supply reservoir. Chapter 1 compared GAMs to LMs for estimating nutrient and organic carbon loads in three major tributaries of the Wachusett Reservoir in central Massachusetts. The relative performance of each model was determined using cross-validation. GAMs outperformed LMs in most cases, explaining an additional 2% of load variance and 5% of concentration variance in validation data on average. Relative differences between the two modeling approaches exceeded 100% depending on the time interval of the load estimate. Chapter 2 assessed the applicability of GAMs to the prediction of riverine solute concentrations during extreme high-flow events when such events are absent from the models\u27 calibration data. The models tended to overpredict extreme-event concentrations, with increasing bias and variance for increasingly extreme hydrologic conditions. Despite an overall increase in uncertainty for extreme-event concentration estimates, estimates under extreme hydrologic conditions could be improved by taking into account the observed bias in the aggregated regional database. Chapter 3 developed and applied a methodology to generate reservoir tributary discharge and constituent concentration time-series for an imposed extreme-event scenario. A multivariate probability model was developed for constituent concentration in an arbitrary number of tributaries and water-quality constituents, conditional on time and hydrologic condition. Two separate historical storm events were modified using 3 extreme precipitation depths on tributaries of the Wachusett Reservoir Watershed in Massachusetts, U.S. Quasi-Monte Carlo was used to propagate this uncertainty to a process-based model of the receiving water body