Predicting complex system behavior using hybrid modeling and computational intelligence

“Modeling and prediction of complex systems is a challenging problem due to the sub-system interactions and dependencies. This research examines combining various computational intelligence algorithms and modeling techniques to provide insights into these complex processes and allow for better decision making. This hybrid methodology provided additional capabilities to analyze and predict the overall system behavior where a single model cannot be used to understand the complex problem. The systems analyzed here are flooding events and fetal health care. The impact of floods on road infrastructure is investigated using graph theory, agent-based traffic simulation, and Long Short-Term Memory deep learning to predict water level rise from river gauge height. Combined with existing infrastructure models, these techniques provide a 15-minute interval for making closure decisions rather than the current 6-hour interval. The second system explored is fetal monitoring, which is essential to diagnose severe fetal conditions such as acidosis. Support Vector Machine and Random Forest were compared to identify the best model for classification of fetal state. This model provided a more accurate classification than existing research on the CTG. A deep learning forecasting model was developed to predict the future values for fetal heart rate and uterine contractions. The forecasting and classification algorithms are then integrated to evaluate the future condition of the fetus. The final model can predict the fetal state 4 minutes ahead to help the obstetricians to plan necessary interventions for preventing acidosis and asphyxiation. In both cases, time series predictions using hybrid modeling provided superior results to existing methods to predict complex behaviors”--Abstract, page iv

Computational intelligence methods for predicting fetal outcomes from heart rate patterns

In this thesis, methods for evaluating the fetal state are compared to make predictions based on Cardiotocography (CTG) data. The first part of this research is the development of an algorithm to extract features from the CTG data. A feature extraction algorithm is presented that is capable of extracting most of the features in the SISPORTO software package as well as late and variable decelerations. The resulting features are used for classification based on both U.S. National Institutes of Health (NIH) categories and umbilical cord pH data. The first experiment uses the features to classify the results into three different categories suggested by the NIH and commonly being used in practice in hospitals across the United States. In addition, the algorithms developed here were used to predict cord pH levels, the actual condition that the three NIH categories are used to attempt to measure. This thesis demonstrates the importance of machine learning in Maternal and Fetal Medicine. It provides assistance for the obstetricians in assessing the state of the fetus better than the category methods, as only about 30% of the patients in the Pathological category suffer from acidosis, while the majority of acidotic babies were in the suspect category, which is considered lower risk. By predicting the direct indicator of acidosis, umbilical cord pH, this work demonstrates a methodology to achieve a more accurate prediction of fetal outcomes using Fetal Heartrate and Uterine Activity with accuracies of greater than 99.5% for predicting categories and greater than 70% for fetal acidosis based on pH values --Abstract, page iii

Flood Prediction and Uncertainty Estimation using Deep Learning

Floods are a complex phenomenon that are difficult to predict because of their non-linear and dynamic nature. Therefore, flood prediction has been a key research topic in the field of hydrology. Various researchers have approached this problem using different techniques ranging from physical models to image processing, but the accuracy and time steps are not sufficient for all applications. This study explores deep learning techniques for predicting gauge height and evaluating the associated uncertainty. Gauge height data for the Meramec River in Valley Park, Missouri was used to develop and validate the model. It was found that the deep learning model was more accurate than the physical and statistical models currently in use while providing information in 15 minute increments rather than six hour increments. It was also found that the use of data sub-selection for regularization in deep learning is preferred to dropout. These results make it possible to provide more accurate and timely flood prediction for a wide variety of applications, including transportation systems

Agent based Modeling for Flood Inundation Mapping and Rerouting

Natural disasters like earthquakes and floods can have a serious impact on road networks, which are critical to supply chain infrastructure and to provide connectivity. These extreme events can result in isolating people in the affected area from hospitals and emergency response. This paper presents an agent-based model for understanding flood propagation and developing inundation mapping. The results from the mapping are used to identify the roads prone to floods based on elevation data and flood simulation. A simulation environment was set up in SUMO, and the costs associated with the traffic disruption are evaluated. This paper discusses the integration of various techniques for a comprehensive flood prediction and rerouting system

Optimal Onsite Microgrid Design for Net-Zero Energy Operation in Manufacturing Industry

Developing an economic net-zero energy infrastructure for the manufacturing industry can play a critical role to achieve the goal of affordable, reliable, and sustainable clean energy paradigm for the next generation. However, it is quite challenging to develop such an infrastructure due to the uncertain demand of the manufacturing system, intermittent electricity generation from the renewable sources, time of use (TOU) pricing of electricity, and integrated operational planning for the long-term planning horizon. In this paper, a mixed-integer non-linear programming (MINLP) model is developed to economically design an onsite microgrid system considering the critical conditions and achieve a net-zero energy operation planning for the manufacturing industry. A linearization strategy is adopted to obtain the optimal design of the microgrid and the utilization of the resources. A numerical case study is conducted to evaluate the effectiveness of the model

Using Trend Extraction and Spatial Trends to Improve Flood Modeling and Control

Effective management of flood events depends on a thorough understanding of regional geospatial characteristics, yet data visualization is rarely effectively integrated into the planning tools used by decision makers. This chapter considers publicly available data sets and data visualization techniques that can be adapted for use by all community planners and decision makers. A long short-term memory (LSTM) network is created to develop a univariate time series value for river stage prediction that improves the temporal resolution and accuracy of forecasts. This prediction is then tied to a corresponding spatial flood inundation profile in a geographic information system (GIS) setting. The intersection of flood profile and affected road segments can be easily visualized and extracted. Traffic decision makers can use these findings to proactively deploy re-routing measures and warnings to motorists to decrease travel-miles and risks such as loss of property or life

Migration US dataset

The dataset combines migration data from Redfin for 132 U.S. cities (2019-2021) with health information from NYU Langone Health's City Health Dashboard. </p

Integrated Deep Learning and Supervised Machine Learning Model for Predictive Fetal Monitoring

Asphyxiation associated with metabolic acidosis is one of the common causes of fetal deaths. The paper aims to develop a feature extraction and prediction algorithm capable of identifying most of the features in the SISPORTO software package and late and variable decelerations. The resulting features were used for classification based on umbilical cord pH data. The algorithms developed here were used to predict cord pH levels. The prediction system assists the obstetricians in assessing the state of the fetus better than the category methods, as only about 30% of the patients in the pathological category suffer from acidosis, while the majority of acidotic babies were in the suspect category, which is considered lower risk. By predicting the direct indicator of acidosis, umbilical cord pH, this work demonstrates a methodology, which uses fetal heart rate and uterine activity, to identify acidosis. This paper introduces a forecasting model based on deep learning to predict heart rate and uterine contractions, integrated with the classification algorithm, resulting in a robust tool for predictive fetal monitoring. The hybrid algorithm resulted in a model capable of providing future conditions of the fetus, which obstetricians can use for diagnosis and planning interventions. The ensemble classification algorithm had a test accuracy of 85% (n = 24) in predicting fetal acidosis on the features extracted from the cardiotocography data. When integrated with the classification model, the results from the prediction model (long short-term memory network) can effectively identify fetal acidosis 2 or 4 min in the future

SoS Explorer Application with Fuzzy-Genetic Algorithms to Assess an Enterprise Architecture -- A Healthcare Case Study

Kevin Dooley (1997), defined Complex Adaptive System (CAS) as a group of semi-autonomous agents who interact in interdependent ways to produce system-wide patterns, such that those patterns then influence behavior of the agents. A healthcare system is considered as a Complex Adaptive System of system (SoS) with agents composed of strategies, people, process, and technology. Healthcare systems are fragmented with independent systems and information. The enterprise architecture (EA) aims to address these fragmentations by creating boundaries around the business strategy and key performance attributes that drive integration across multiple systems of processes, people, and technology. This paper uses a SoS Explorer to select an optimal architecture that provide the necessary capabilities to meet key performance attributes (KPAs) in a dynamic, complex healthcare business environment. The SoS Explorer produced an optimal meta-architecture where all but two systems (disease and facility processes) participated with many of the systems having at least four interfaces. The healthcare meta-architecture produced in this study is not a solution to address the challenges of the healthcare enterprise architecture but provides insight on the areas - systems, capabilities, characteristics, and interfaces - to pay attention to where agility is an important attribute and not to be severely compromised

Evaluation of Support Vector Machines and Random Forest Classifiers in a Real-Time Fetal Monitoring System based on Cardiotocography Data

In this paper, we compare methods for evaluating the fetal state prediction based on Cardiotocography (CTG) data. Antepartum Fetal Monitoring provides information that can be used to predict the state of the fetus during labor to indicate the risk of a fetal acidosis (low blood pH from low oxygen levels). The effectiveness of these predictions is evaluated in a real-time clinical decision support system and extracts other features that can provide further information regarding the fetal state. This research differs from previous work in that all three fetal states (normal, suspect and pathological) are considered. The paper discusses the importance of machine learning in providing assistance for the obstetricians in \u27suspect\u27 cases. Results show that both Support Vector Machines and Random Forests had over 96% accuracy when predicting fetal outcomes, with SVM performing slightly better for suspect cases