Jose Tupayachi

This is a user's guide for the UI developed within the NSRD project.

Accurate prediction of extreme weather events is essential for operational preparedness at research facilities and critical infrastructure. This study presents an explainable machine learning framework for multi-event temporal forecasting using 15-minute resolution meteorological observations from six instrumented towers across the Oak Ridge Reservation. We address five operationally extreme events: temperature--moisture hazard, absolute humidity, wind chill, freezing temperatures, high wind, and atmospheric stagnation. Our data fusion strategy enables each model to learn cross-tower spatial patterns. We evaluate six models, including LightGBM, XGBoost, LSTM-FCN, TST, and the convolutional neural networks ResNet+ and XceptionTime. Experimental configurations span event-specific forecast horizons from 30~minutes to 6~hours. An ablation study quantifies the contribution of seven ORR Variables background meteorological features including mixing height, dew point, solar radiation, atmospheric stability, and surface pressure gradients. Boosting methods achieve the highest overall performance, with a mean Matthews Correlation Coefficient (MCC) of 0.829. Event-specific accuracy shows strong results for temperature–moisture events (MCC = 0.964), wind chill (0.963), and low temperature (0.937), while high wind remains more challenging (0.764) due to its rapid-onset dynamics. Feature importance analysis reveals that tower-local features such as rolling temperature minima, peak wind speed, and absolute humidity dominate prediction, with ORR Variables physics-based gradient features contributing at extended forecast horizons. This work provides a reproducible, operationally deployable framework for extreme event prediction applicable to critical facilities.

According to the Immunization Status Survey conducted by the Tennessee Department of Health in 2023, Tennessee ranks in the bottom 25th percentile among states for vaccination rates by the age of 24 months, based on the full series of recommended vaccines. To tackle this issue, the SmartSHOTS mobile application was developed to reduce barriers to vaccinations for children aged 0-24 months. The app includes features such as vaccine information, the ability to add and calculate vaccine due dates, and the ability to locate health departments and transportation services based on zip codes. The app was developed and usability tested using an iterative design process, based on a needs assessment conducted across regions of Tennessee with community members who served on the state’s county health council. These community members also reviewed the app wireframes. Parents or guardians of children aged 0-24 months are currently evaluating the app (preliminary findings reported here). SmartSHOTS will then be available on Google Play and the Apple Store.

We present a pilot study exploring the potential of Large Language Models (LLMs) to interface with application programming interfaces through logical instructions, specifically within the domain of Geographic Question Answering for route optimization. This study employs a Continuous Retrieval-Augmented Generation approach combined with fine-tuned LLMs, featuring customized node-based storage and vector search retrieval. We also provide a comparative analysis of the method's effectiveness and adaptability in handling diverse textual queries.

Bibliometric analysis is essential for understanding research trends, scope, and impact in urban science, especially in high-impact journals, such Nature Portfolios. However, traditional methods, relying on keyword searches and basic NLP techniques, often fail to uncover valuable insights not explicitly stated in article titles or keywords. These approaches are unable to perform semantic searches and contextual understanding, limiting their effectiveness in classifying topics and characterizing studies. In this paper, we address these limitations by leveraging Generative AI models, specifically transformers and Retrieval-Augmented Generation (RAG), to automate and enhance bibliometric analysis. We developed a technical workflow that integrates a vector database, Sentence Transformers, a Gaussian Mixture Model (GMM), Retrieval Agent, and Large Language Models (LLMs) to enable contextual search, topic ranking, and characterization of research using customized prompt templates. A pilot study analyzing 223 urban science-related articles published in Nature Communications over the past decade highlights the effectiveness of our approach in generating insightful summary statistics on the quality, scope, and characteristics of papers in high-impact journals. This study introduces a new paradigm for enhancing bibliometric analysis and knowledge retrieval in urban research, positioning an AI agent as a powerful tool for advancing research evaluation and understanding.

The incorporation of Artificial Intelligence (AI) models into various optimization systems is on the rise. However, addressing complex urban and environmental management challenges often demands deep expertise in domain science and informatics. This expertise is essential for deriving data and simulation-driven insights that support informed decision-making. In this context, we investigate the potential of leveraging the pre-trained Large Language Models (LLMs) to create knowledge representations for supporting operations research. By adopting ChatGPT-4 API as the reasoning core, we outline an applied workflow that encompasses natural language processing, Methontology-based prompt tuning, and Generative Pre-trained Transformer (GPT), to automate the construction of scenario-based ontologies using existing research articles and technical manuals of urban datasets and simulations. From these ontologies, knowledge graphs can be derived using widely adopted formats and protocols, guiding various tasks towards data-informed decision support. The performance of our methodology is evaluated through a comparative analysis that contrasts our AI-generated ontology with the widely recognized pizza ontology, commonly used in tutorials for popular ontology software. We conclude with a real-world case study on optimizing the complex system of multi-modal freight transportation. Our approach advances urban decision support systems by enhancing data and metadata modeling, improving data integration and simulation coupling, and guiding the development of decision support strategies and essential software components.

Wildfire incidents are catastrophic events with a high level of uncertainty. Therefore, resource allocation towards its suppression is a challenging task. Our research presents a method for wildfire management that combines digital twins with a real-time deep reinforcement learning (DRL) technique. The detection of wildfires is supported by the use of real-time satellite imagery. The developed digital twin operates under two stages. Initially, a discrete simulation approach is employed to model the behavior of fires, their immediate impact on the population, and the response times of firefighting resources. Then, an Advantage Actor-Critic (A2C) DRL policy optimizes the response under different scenarios accounting for a time-based environment to adjust agent actions. The holistic approach presented in this study strives to synergize and minimize the effect of wildfires over population to create an adaptive and effective wildfire response system.

The use of drones for package delivery, commonly known as drone delivery or unmanned aerial vehicle (UAV) delivery, has gained significant attention from academia and industries. Compared to traditional delivery methods, it provides greater flexibility, improved accessibility, increased speed and efficiency, enhanced safety, and even some environmental benefits. With the increasing interest in this technology, it is crucial for researchers and practitioners to understand the current state of the art in drone delivery. This paper aims to review the current literature on drone delivery and identify research trends, challenges, and future research directions. Specifically, the relevant literature is identified and selected using a systematic literature review approach. We then categorize the literature according to the characteristics and objectives of the problems and thoroughly analyze them based on mathematical formulations and solution techniques. We summarize key challenges and limitations associated with drone delivery from technological, safety, societal, and environmental aspects. Finally, potential research directions are identified.