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<!doctype html>
<html lang="en">
<head>
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<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
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<title>AIR Technical Research Applications Development | The Bridge Between Computer Science and Interdisciplinary Research</title>
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<a href="https://air.arizona.edu" target="_blank" rel="noopener noreferrer"><img class="img-fluid" src="images/airLogo-dark.png" alt="Arizona Institute for Resilience"></a>
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<h1 class="mb-2">AIR Technical Research Applications Development</h1>
<p class="lead">The Bridge Between Computer Science and Interdisciplinary Research</p>
</div>
<!-- Overview Section (not linked in menu) -->
<div class="col-12">
<h2 class="h1">Overview</h2>
<h3>Who we are</h3>
<p>The Technical Research Applications Development (TRAD) group is a group of technology research professionals and students who provide technical research services and collaborations to the <a href="https://air.arizona.edu/centers-programs" target="_blank" rel="noopener noreferrer">Centers and Programs</a> found within the <a href="https://air.arizona.edu" target="_blank" rel="noopener noreferrer">Arizona Institute for Resilience</a> (AIR). Our project collaborations span the globe and make meaningful impacts from the desert southwestern United States to the Middle East and places in between.</p>
<p>TRAD is led by <a href="https://github.com/reyg3" target="_blank" rel="noopener noreferrer">Rey Granillo</a> Director of Technology and Research & Development Engineering, <a href="https://github.com/lboeman" target="_blank" rel="noopener noreferrer">Leland Boeman</a> Research & Development Software Engineer, and <a href="https://github.com/tweissaz" target="_blank" rel="noopener noreferrer">Thomas Weiss</a> Research & Development Systems Engineer.</p>
<h3>Our proficiencies</h3>
<ul>
<li>Research & Development / Proof of Concept</li>
<ul>
<li>Research-focused software development</li>
<li>Hardware and sensor development</li>
</ul>
<li>Cloud computing infrastructure</li>
<ul>
<li>Amazon Web Services (AWS)</li>
<li>Google Cloud Platform (GCP)</li>
</ul>
<li>Data assimilation, processing, and presentation</li>
<li>Database architecture and development</li>
<li>Application Programming Interface (API) development</li>
<li>Remote sensing</li>
<ul>
<li>LoRaWAN</li>
<li>Microcontroller design and development</li>
</ul>
<li>3D printing</li>
<li>Machine Learning (ML)</li>
</ul>
<h3>How we do it</h3>
<h4>Development Process</h4>
<p>Using a collaborative model, TRAD members gain a broader understanding of the research questions being asked, and collaborators gain a better understanding of how cutting edge technology can be applied to their area of expertise. These collaborations have uncovered correlations in data that were not previously realized which have led to new research project ideas and proposals. We collaborate at each step of the research process from proposal development to publication. TRAD can provide a proof-of-concept application or tool showing what is technically feasible to add a competitive edge to proposals. Upon project completion, we aid with the technical writing of publications which outline our findings and technical solutions that were implemented.</p>
<h4>Student Support</h4>
<p>We rely on student support and participation across all research projects. Student participation ranges from software development, technical hardware implementation, database architecture design and implementation, ML/AI research, and publication writing. To support these efforts, AIR has established a student research computing working group that primarily meets during the Fall and Spring semesters. This working group is a forum where we discuss new research technologies, ask questions, and where students can report on their current research activities. The intention is to create research focused critical thinking processes that can generate new ideas and concepts across various projects and collaborations.</p>
<p>Our current student cohort includes:</p>
<ul>
<li><a href="https://github.com/rogerpalmenberg" target="_blank" rel="noopener noreferrer">Roger Palmenberg</a>
<ul>
<li>
Major: Statistics and Data Science
</li>
</ul>
</li>
<li><a href="https://github.com/xbeyllx" target="_blank" rel="noopener noreferrer">Minh Ngo</a>
<ul>
<li>
Major: Computer Science
</li>
</ul>
</li>
<li><a href="https://github.com/therbun" target="_blank" rel="noopener noreferrer">Therese Bunyi</a>
<ul>
<li>
Major: Computer Science and Math (Applied Emphasis), Minor: Physiology and Medical Sciences
</li>
</ul>
</li>
</ul>
</div>
</div>
<!-- Collaborators Section -->
<div class="row">
<div class="col-12">
<h2 id="collaborators">Research Collaborators</h2>
<p>In addition to our core research team, we've had a number of collaborators across AIR programs and affiliated groups across the University of Arizona.</p>
<div class="container">
<div class="row text-center">
<div class="col-6 col-md-4 col-lg-3">
<img src="https://github.com/ben-mcmahan.png" style="max-height: 188px;" class="img-fluid img-thumbnail" alt="Ben McMahan headshot">
<p class="mt-2"><span class="lead"><a href="https://climas.arizona.edu/about/people/ben-mcmahan" class="stretched-link" target="_blank" rel="noopener noreferrer">Ben McMahan</a></span><br>AIR, School of Anthropology</p>
</div>
<div class="col-6 col-md-4 col-lg-3">
<img src="images/headshots/Crimmins%20Mod.jpg" style="max-height: 188px;" class="img-fluid img-thumbnail" alt="Mike Crimmins headshot">
<p class="mt-2 align-text-bottom"><span class="lead"><a href="https://environmentalscience.cals.arizona.edu/person/michael-crimmins" class="stretched-link" target="_blank" rel="noopener noreferrer">Mike Crimmins</a></span><br>CALS - Environmental Sciences</p>
</div>
<div class="col-6 col-md-4 col-lg-3">
<img src="https://air.arizona.edu/sites/default/files/2022-12/ZackGuido.jpg" style="max-height: 188px;" class="img-fluid img-thumbnail" alt="Zack Guido headshot">
<p class="mt-2"><span class="lead"><a href="https://air.arizona.edu/person/zack-guido" class="stretched-link" target="_blank" rel="noopener noreferrer">Zack Guido</a></span><br>AIR International Programs</p>
</div>
<div class="col-6 col-md-4 col-lg-3">
<img src="images/headshots/tom%20evans.jpg" style="max-height: 188px;" class="img-fluid img-thumbnail" alt="Tom Evans headshot">
<p class="mt-2"><span class="lead"><a href="https://geography.arizona.edu/people/tom-evans" class="stretched-link" target="_blank" rel="noopener noreferrer">Tom Evans</a></span><br>SBS - School of Geography and Development</p>
</div>
<div class="col-6 col-md-4 col-lg-3">
<img src="https://air.arizona.edu/sites/default/files/2022-12/RileyDuren.png" style="max-height: 188px;" class="img-fluid img-thumbnail" alt="Riley Duren headshot">
<p class="mt-2"><span class="lead"><a href="https://air.arizona.edu/person/riley-duren" class="stretched-link" target="_blank" rel="noopener noreferrer">Riley Duren</a></span><br>CEO Carbon Mapper</p>
</div>
<div class="col-6 col-md-4 col-lg-3">
<img src="images/headshots/eller-Wesley-Howden.jpg" style="max-height: 188px;" class="img-fluid img-thumbnail" alt="Wes Howden headshot">
<p class="mt-2"><span class="lead"><a href="https://eller.arizona.edu/people/wesley-howden" class="stretched-link" target="_blank" rel="noopener noreferrer">Wes Howden</a></span><br>Eller College of Management</p>
</div>
<div class="col-6 col-md-4 col-lg-3">
<img src="images/headshots/Hara%20Crop.jpg" style="max-height: 188px;" class="img-fluid img-thumbnail" alt="Konan Hara headshot">
<p class="mt-2"><span class="lead"><a href="https://eller.arizona.edu/people/konan-hara" class="stretched-link" target="_blank" rel="noopener noreferrer">Konan Hara</a></span><br>Eller Doctoral Student Economics</p>
</div>
<div class="col-6 col-md-4 col-lg-3">
<img src="images/headshots/RosenbaumRachel.jpeg" style="max-height: 188px;" class="img-fluid img-thumbnail" alt="Rachel Rosenbaum headshot">
<p class="mt-2"><span class="lead"><a href="https://anthropology.arizona.edu/people/rachel-rosenbaum" class="stretched-link" target="_blank" rel="noopener noreferrer">Rachel Rosenbaum</a></span><br>School of Anthropology</p>
</div>
<div class="col-6 col-md-4 col-lg-3">
<img src="https://air.arizona.edu/sites/default/files/2022-12/NancyP.jpg" style="max-height: 188px;" class="img-fluid img-thumbnail" alt="Nancy Petersen headshot">
<p class="mt-2"><span class="lead"><a href="https://air.arizona.edu/person/nancy-petersen" class="stretched-link" target="_blank" rel="noopener noreferrer">Nancy Petersen</a></span><br>AIR Haury Program</p>
</div>
<div class="col-6 col-md-4 col-lg-3">
<img src="https://github.com/dharmahoy.png" style="max-height: 188px;" class="img-fluid img-thumbnail" alt="Dharma Hoy headshot">
<p class="mt-2"><span class="lead"><a href="https://github.com/dharmahoy" class="stretched-link" target="_blank" rel="noopener noreferrer">Dharma Hoy</a></span><br>Statistics Graduate Student Colorado State</p>
</div>
</div>
</div>
</div>
</div>
<!-- Proposals Section -->
<div class="row">
<div class="col-12">
<h2 id="proposals">Proposals</h2>
<div class="accordion" id="proposal_accordion">
<div class="card">
<div class="card-header" id="proposal_heading_1">
<button class="btn btn-link collapsed pr-5" type="button" data-toggle="collapse" data-target="#proposal_collapse_1" aria-expanded="false" aria-controls="proposal_collapse_1">
Integrating Data Science and ML/AI into Stakeholder Informed Assessments of Fire Weather Tools
</button>
</div>
<div id="proposal_collapse_1" class="collapse" aria-labelledby="proposal_heading_1" data-parent="#proposal_accordion">
<div class="card-body">
<p>Study/Project Aim(s) IDENTIFY fire weather forecast priorities and EXPAND the fire weather stakeholder network, DEVELOP experimental fire weather analysis and visualization prototypes, based on insights from data science, Ux testing, and ML/AI, CO-PRODUCE relevant, salient, and credible fire weather information collaboration with fire managers that ENHANCE fire weather impact decision support services (a NOAA/Npriority), and DEVELOP strategies to expand these products at scale for use by wider networks of fire managers, and EXPLORE opportunities for aggregation, management, visualization, and analysis of fire rand weather data</p>
<p>PI: Rey Granillo<br>
Co-Applicants: Ben McMahan, Leland Boeman<br>
Original Proposal: $638,224<br>
Seed Funding (RII IT4IR 18th Mile): $63,052<br>
</p>
</div>
</div>
</div>
<div class="card">
<div class="card-header" id="proposal_heading_2">
<button class="btn btn-link collapsed pr-5" type="button" data-toggle="collapse" data-target="#proposal_collapse_2" aria-expanded="false" aria-controls="proposal_collapse_2">
High-Resolution multitemporal urban heat mapping: innovations in data aggregation, community engaged citizen science, and student experiential learning, to develop a scalable heat mapping platform in the Southwest
</button>
</div>
<div id="proposal_collapse_2" class="collapse" aria-labelledby="proposal_heading_2" data-parent="#proposal_accordion">
<div class="card-body">
<p>Proof of concept for data aggregation work, development of heat island data and visualization, laid foundation for student training in interdisciplinary and stakeholder engaged research at intersection of climate/social/computer science</p>
<p>PI: Ben McMahan & Rey Granillo<br>
$144,646</p>
</div>
</div>
</div>
<div class="card">
<div class="card-header" id="proposal_heading_3">
<button class="btn btn-link collapsed pr-5" type="button" data-toggle="collapse" data-target="#proposal_collapse_3" aria-expanded="false" aria-controls="proposal_collapse_3">
Can We Make a “RainLog for Heat Maps”? – Leveraging Data Aggregation and Citizen Science to Improve Temperature Maps and Enhance Outreach, Engagement and Education
</button>
</div>
<div id="proposal_collapse_3" class="collapse" aria-labelledby="proposal_heading_3" data-parent="#proposal_accordion">
<div class="card-body">
<p>Proof of concept for data aggregation work, would have helped lay foundation for future student collaborative work, and faculty led proposals</p>
<p>PI: Ben McMahan<br>
Co-PI: Mike Crimmins<br>
$40,554</p>
</div>
</div>
</div>
<div class="card">
<div class="card-header" id="proposal_heading_4">
<button class="btn btn-link collapsed pr-5" type="button" data-toggle="collapse" data-target="#proposal_collapse_4" aria-expanded="false" aria-controls="proposal_collapse_4">
Stakeholder Informed Assessments of Fire Weather Impact Decision Support Services
</button>
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<p>Heavy involvement on data aggregation and UX/visualization development. (why Rey was PI, as learning lab was heavily implicated in our proposed capacity)</p>
<p>PI: Ben McMahan<br>
Co-PI: Rey Granillo, Mike Crimmins, Will Holmgren<br>
$638,224</p>
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Anticipating Extreme Monsoon Rainfall: Integrating Machine Learning and Artificial Intelligence into Flood Risk Prediction Models
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<p>Use ML techniques to organize aggregated monsoon data to create improved flood decision support and data viz</p>
<p>PI: Ben McMahan & Rey Granillo<br>
$24,385</p>
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Adapting and Enhancing the Monsoon Threat Index: Iterative Development of Stakeholder Informed Climate Services and Weather Information
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<p>Included development of stakeholder focused data viz and tools - would have used the learning lab to help with project work and give students experience</p>
<p>PI: Ben McMahan<br>
Co-PI: Will Holmgren, Mike Crimmins<br>
$365,117</p>
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Public Engagement in Southwest Monsoon Forecasting: A Prototype Game to Build Climate Awareness and to Evaluate Community Forecast Skill
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<p>PI: Z. Guido<br>
Co-PI: McMahon, B., Crimmins, C., and Granillo, R.<br>
$13,958</p>
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Innovative Visualization & Analysis Tools for the North American Monsoon - Connecting Citizen Science Data and Observations for Research, Policy, and Decision Support
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<p>Initial foray into data aggregation.</p>
<p>PI: Ben McMahan<br>
Co-PI: Will Holmgren, Mike Crimmins<br>
$25,000</p>
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<h2 id="tech-transfer">Technology Transfer</h2>
<h3>National Science Foundation Innovation Corps (NSF I-Corps) Training Program</h3>
<p>The U.S. National Science Foundation’s Innovation Corps (I-Corps™) program is an immersive, entrepreneurial training program that facilitates the transformation of invention to impact. This immersive, seven-week experiential training program prepares scientists and engineers to extend their focus beyond the university laboratory — accelerating the economic and societal benefits of NSF-funded and other basic research projects that are ready to move toward commercialization. Visit the <a href="https://new.nsf.gov/funding/initiatives/i-corps/about-i-corps" target="_blank" rel="noopener noreferrer">NSF's I-Corps website</a> for more information.
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<h4>Desert and Pacific Regional Hub 2023 - Cohort 6 - Fire Data Dynamics</h4>
<p>AIR's technology team is working with <a href="https://techlaunch.arizona.edu/" target="_blank" rel="noopener noreferrer">Tech Launch Arizona</a> on licensing for our Fire Data Dynamics prototype to help make meaningful impacts in fire prediction, response, and analysis. As part of this licensing process, we participated in NSF's I-Corps where we learned about the Lean Startup Methodology with a strong focus on customer discovery and learning about the challenges being faced to ensure we are providing a solution with meaningful impact.</p>
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<h2 id="upcoming-projects">Upcoming Projects</h2>
<h3>Biosphere 2 Remote Sensing</h3>
<p>AIR's TRAD will be working closely with researchers at Biosphere 2 (B2) on the deployment of a LoRaWAN remote sensing gateway and the development and deployment of custom environmental remote sensing hardware within B2's biomes. This work will test sensor and enclosure designs in preparation for potential field deployment at research sites around the globe.
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<h4>ALERTArizona Fire Camera Network</h4>
<p>In collaboration with <a href="https://www.alertwest.org/" target="_blank" rel="noopener noreferrer">ALERTWest</a>, AIR's TRAD will facilitate and maintain a network of environmental monitoring equipment to include cameras designed to detect wildfires using an Artificial Intelligence (AI) model built by CAL FIRE and ALERTCalifornia. This early warning system is backed by a 24/7 operations center supporting users and provides human verification for every AI detection.
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<h2 id="projects">Projects</h2> <!-- Note: This is not linked in main navigation since it's a dropdown -->
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<h3 class="card-title h4" id="fire-data-dynamics">Fire Data Dynamics </h3>
<p class="card-text font-weight-normal">With data from the National Interagency Fire Center's (NIFC) Southwest Coordination Center (SWCC), and with funding from the University of Arizona's Technology and Research Initiative Fund (TRIF), we developed a fire data dashboard prototype designed to be used by and inform decision makers of environmental factors leading up to, during, and post fire events.</p>
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<h3 class="card-title h4" id="raws-machine-learning">Remote Automated Weather Stations (RAWS) Machine Learning</h3>
<p class="card-text font-weight-normal">Leveraging historical RAWS data from the past 20 years alongside popular machine learning technologies, we were able to build models to predict wind speed, temperature, and relative humidity conditions for 6-, 12-, and 24-hour time periods.</p>
<p><button class="stretched-link btn btn-outline-red" data-toggle="modal" data-target="#raws-machine-learning-modal" role="button">More Information</button></p>
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<h3 class="card-title h4" id="carbon-econ-plotter">Carbon-Econ Plotter</h3>
<p class="card-text font-weight-normal">We map methane plume emission rates measured by Carbon Mapper onto tract-level demographics from the U.S. Census Bureau to explore environmental justice issues associated with the methane emissions.</p>
<p><button class="stretched-link btn btn-outline-red" data-toggle="modal" data-target="#carbon-econ-plotter-modal" role="button">More Information</button></p>
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<h3 class="card-title h4" id="lorawan">LoRaWAN Remote Sensing Network</h3>
<p class="card-text font-weight-normal">AIR supports the Desert Laboratory on Tumamoc Hill which is an 860-acre preserve located west of downtown Tucson. AIR has a number of LoRaWAN Gateways and is using Tumamoc as our current testbed for future remote sensing projects.</p>
<p><button class="stretched-link btn btn-outline-red" data-toggle="modal" data-target="#lorawan-tumamoc" role="button">More Information</button></p>
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<h3 id="monsoon-game">Monsoon Game Repositories</h3>
<p>The following projects all pertain to various aspects of the AIR <a href="https://monsoonfantasy.arizona.edu/" target="_blank" rel="noopener noreferrer">Monsoon Fantasy Game</a>. These repositories include the detailed scoring method and a post game analysis.</p>
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<h4 class="card-title" id="monsoon-fantasy-game">Monsoon Fantasy Game</h4>
<p class="card-text font-weight-normal">In Monsoon Fantasy, players estimate the total monthly precipitation at each of the five major cities in the U.S. Southwest Monsoon region: Tucson, Phoenix, Flagstaff, Albuquerque, and El Paso.</p>
<p><button class="stretched-link btn btn-outline-red" data-toggle="modal" data-target="#monsoon-fantasy-game-modal" role="button">More Information</button></p>
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<h4 class="card-title" id="monsoon-game-scoring-method">Monsoon Game Scoring Method</h4>
<p class="card-text font-weight-normal">This repository contains a description of the scoring system used in the Monsoon Fantasy game and a simulation that was created to compare different scoring methods before the final scoring method was decided upon.</p>
<p><button class="stretched-link btn btn-outline-red" data-toggle="modal" data-target="#monsoon-game-scoring-method-modal" role="button">More Information</button></p>
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<h4 class="card-title" id="monsoon-post-game-analysis">Monsoon Post Game Analysis</h4>
<p class="card-text font-weight-normal">When signing up to play Monsoon Fantasy, players had the option to fill out profile questions. These questions asked things such as: How many monsoon seasons have you experienced while living in the southwest?</p>
<p><button class="stretched-link btn btn-outline-red" data-toggle="modal" data-target="#monsoon-post-game-analysis-modal" role="button">More Information</button></p>
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<h3 id="monsoon-data-collection">Monsoon Data Collection and Processing</h3>
<p>The following projects are geared towards improving the availability of monsoon related meteorological data in Arizona by providing a centralized and persistent source for otherwise ephemeral observation data and demonstrating the value of that data through visualization and machine learning applications.</p>
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<h4 class="card-title" id="monsoon-scraper">Monsoon Scraper</h4>
<p class="card-text font-weight-normal">This project centralizes public data from several different Flood Control District (FCD) networks across the state of Arizona. This data is stored in a cloud based data warehouse and serves as the central data source for a number of monsoon related projects and research.</p>
<p><button class="stretched-link btn btn-outline-red" data-toggle="modal" data-target="#monsoon-scraper-modal" role="button">More Information</button></p>
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<h4 class="card-title" id="monsoon-data-api">Monsoon Data Application Programming Interface (API)</h4>
<p class="card-text font-weight-normal">Once data from the Monsoon Scraper project was gathered, we developed a REST API to programmatically query the dataset. The API contains a number of custom routes designed to query specific sets of data.</p>
<p><button class="stretched-link btn btn-outline-red" data-toggle="modal" data-target="#monsoon-data-api-modal" role="button">More Information</button></p>
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<h4 class="card-title" id="monsoon-plotter">Monsoon Plotter</h4>
<p class="card-text font-weight-normal">Monsoon Plotter is used to visually represent the data gathered via the Monsoon Scraper project which collects data from the state of Arizona flood control district (FCD) remote sensing networks. There are a handful of networks that can be plotted and more will be added as we expand our Monsoon Scraper project to gather more data.</p>
<p><button class="stretched-link btn btn-outline-red" data-toggle="modal" data-target="#monsoon-plotter-modal" role="button">More Information</button></p>
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<h4 class="card-title" id="monsoon-package">Monsoon API Package/CLI Tool</h4>
<p class="card-text font-weight-normal">This Python package serves as a wrapper to simplify REST API calls to the monsoon scraper data warehouse. The plotter allows a limited export of the data dependent on the date range and sensor network being plotted.</p>
<p><button class="stretched-link btn btn-outline-red" data-toggle="modal" data-target="#monsoon-package-modal" role="button">More Information</button></p>
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<h4 class="card-title" id="monsoon-machine-learning">Monsoon Machine Learning</h4>
<p class="card-text font-weight-normal">This repository takes historical weather data from the Tucson International Airport, precipitation data from our Monsoon API, and storm data from NOAA and applies these machine learning algorithms in an attempt to accurately predict flooding using historic data and a database of notable flood and rainfall events.</p>
<p><button class="stretched-link btn btn-outline-red" data-toggle="modal" data-target="#monsoon-machine-learning-modal" role="button">More Information</button></p>
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<h3 class="card-title h4" id="regenerate-lebanon">Regenerate Lebanon</h3>
<p class="card-text font-weight-normal">This project stemmed from a UN funding award through the Japanese Embassy to provide a mapping service of recycle centers across Lebanon. The project then branched out to include sustainable businesses and other environmentally friendly organizations.</p>
<p><button class="stretched-link btn btn-outline-red" data-toggle="modal" data-target="#regenerate-lebanon-modal" role="button">More Information</button></p>
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<h3 class="card-title h4" id="jamaica-coffee-leaf-rust">Jamaica Coffee Leaf Rust</h3>
<p class="card-text font-weight-normal">This was a proof-of-concept application that was built using Google's Vision AI to detect Coffee Leaf Rust which is a fungus that kills coffee plant crop fields. The idea was to detect this fungus via photos taken by farmers in Jamaica.</p>
<p><button class="stretched-link btn btn-outline-red" data-toggle="modal" data-target="#jamaica-coffee-leaf-rust-modal" role="button">More Information</button></p>
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<h3 class="card-title h4" id="project-visualizations">Project Visualizations</h3>
<p class="card-text font-weight-normal">This project was developed for the Agnese Nelms Haury Program in Environment and Social Justice. The idea was to use models and algorithms in Natural Language Processing (NLP) to find similarities or potential collaboration opportunities across abstract and proposal submissions.</p>
<p><button class="stretched-link btn btn-outline-red" data-toggle="modal" data-target="#project-visualizations-modal" role="button">More Information</button></p>
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<h3 class="card-title h4" id="zambia-invasive-species-tracking">Zambia Invasive Species Tracking</h3>
<p class="card-text font-weight-normal">This invasive species tracking application was designed to plot the detection of the Fall Army Worm (FAW) across maize crops in Zambia. The result of this tracking helped governmental agriculture decision makers in focussing remediation efforts of FAW.</p>
<p><button class="stretched-link btn btn-outline-red" data-toggle="modal" data-target="#zambia-invasive-species-tracking-modal" role="button">More Information</button></p>
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<h2 id="publications">Publications</h2>
<p>Dharma H, Granillo R.L. III, Boeman L, McMahan B, Crimmins MA (2023) <a href="https://doi.org/10.3389/fclim.2023.1107363" target="_blank" rel="noopener noreferrer">Data Aggregation, ML ready Datasets, and an API: Leveraging diverse data to create enhanced characterizations of monsoon flood risk. Frontiers in Climate</a>. Front. Clim. doi: 10.3389/fclim.2023.1107363</p>
<p>Guido, Z., McMahan, B., Hoy, D., Larsen, C., Delgado, B., Granillo, R. L., III, & Crimmins, M. (2022). Public Engagement on Weather and Climate with a Monsoon Fantasy Forecasting Game, Bulletin of the American Meteorological Society. <a href="https://journals.ametsoc.org/view/journals/bams/aop/BAMS-D-22-0003.1/BAMS-D-22-0003.1.xml" class="d-inline-block text-truncate" style="max-width: 100%;" target="_blank" rel="noopener noreferrer">https://journals.ametsoc.org/view/journals/bams/aop/BAMS-D-22-0003.1/BAMS-D-22-0003.1.xml</a></p>
<p>Dharma Hoy, Calvin Larsen, Rey Granillo III, UA's Arizona Institute for Resilient Environments and Societies. (2022). uaenvironment/monsoon-game-scoring-method: v1.0.1 (v1.0.1). Zenodo. <a href="https://doi.org/10.5281/zenodo.6878318" target="_blank" rel="noopener noreferrer">https://doi.org/10.5281/zenodo.6878318</a></p>
<p>McMahan B, Granillo R.L. III, Delgado B, Herrera M and Crimmins MA (2021) <a href="https://www.frontiersin.org/articles/10.3389/fclim.2021.602573/full" target="_blank" rel="noopener noreferrer">Curating and Visualizing Dense Networks of Monsoon Precipitation Data: Integrating Computer Science Into Forward Looking Climate Services Development</a>. Front. Clim. 3:602573. doi: 10.3389/fclim.2021.602573</p>
<h3>Works in Progress</h3>
<p>Improving heat mapping using IoT weather station devices (Thomas Weiss, Rey Granillo, Leland Boeman, Lily McMullen, Mike Crimmins)</p>
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<h2 id="students">Student Contributors</h2>
<h3>Current</h3>
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<img src="https://github.com/rogerpalmenberg.png" style="max-height: 188px;" class="img-fluid img-thumbnail" alt="Roger Palmenberg headshot">
<p class="mt-2 align-text-bottom"><span class="lead"><a href="https://github.com/rogerpalmenberg" class="stretched-link" target="_blank" rel="noopener noreferrer">Roger Palmenberg</a></span></p>
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<img src="https://github.com/xbeyllx.png" style="max-height: 188px;" class="img-fluid img-thumbnail" alt="Minh Ngo headshot">
<p class="mt-2 align-text-bottom"><span class="lead"><a href="https://github.com/xbeyllx" class="stretched-link" target="_blank" rel="noopener noreferrer">Minh Ngo</a></span></p>
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<img src="https://github.com/therbun.png" style="max-height: 188px;" class="img-fluid img-thumbnail" alt="Therese Bunyi headshot">
<p class="mt-2 align-text-bottom"><span class="lead"><a href="https://github.com/therbun" class="stretched-link" target="_blank" rel="noopener noreferrer">Therese Bunyi</a></span></p>
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<h3>Former</h3>
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<img src="https://github.com/joeybish.png" style="max-height: 188px;" class="img-fluid img-thumbnail" alt="Joseph Bishop headshot">
<p class="mt-2 align-text-bottom"><span class="lead"><a href="https://github.com/joeybish" class="stretched-link" target="_blank" rel="noopener noreferrer">Joseph Bishop</a></span></p>
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<img src="https://github.com/lilymcmullen.png" style="max-height: 188px;" class="img-fluid img-thumbnail" alt="Lily McMullen headshot">
<p class="mt-2 align-text-bottom"><span class="lead"><a href="https://github.com/lilymcmullen" class="stretched-link" target="_blank" rel="noopener noreferrer">Lily McMullen</a></span></p>
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<img src="https://github.com/dharmahoy.png" style="max-height: 188px;" class="img-fluid img-thumbnail" alt="Dharma Hoy headshot">
<p class="mt-2"><span class="lead"><a href="https://github.com/dharmahoy" class="stretched-link" target="_blank" rel="noopener noreferrer">Dharma Hoy</a></span></p>
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<img src="https://github.com/rose-prendergast.png" style="max-height: 188px;" class="img-fluid img-thumbnail" alt="Rose Prendergast headshot">
<p class="mt-2"><span class="lead"><a href="https://github.com/rose-prendergast" class="stretched-link" target="_blank" rel="noopener noreferrer">Rose Prendergast</a></span></p>
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<img src="https://github.com/shyambhavi3.png" style="max-height: 188px;" class="img-fluid img-thumbnail" alt="Shyambhavi Shyambhavi headshot">
<p class="mt-2 align-text-bottom"><span class="lead"><a href="https://github.com/shyambhavi3" class="stretched-link" target="_blank" rel="noopener noreferrer">Shyambhavi Shyambhavi</a></span></p>
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<img src="https://github.com/francesslater.png" style="max-height: 188px;" class="img-fluid img-thumbnail" alt="Franny Slater headshot">
<p class="mt-2 align-text-bottom"><span class="lead"><a href="https://github.com/francesslater" class="stretched-link" target="_blank" rel="noopener noreferrer">Franny Slater</a></span></p>
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<img src="https://github.com/calvinlarsen.png" style="max-height: 188px;" class="img-fluid img-thumbnail" alt="CJ Larsen headshot">
<p class="mt-2"><span class="lead"><a href="https://github.com/calvinlarsen" class="stretched-link" target="_blank" rel="noopener noreferrer">CJ Larsen</a></span></p>
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<img src="https://github.com/gbdelgado.png" style="max-height: 188px;" class="img-fluid img-thumbnail" alt="Benni Delgado headshot">
<p class="mt-2 align-text-bottom"><span class="lead"><a href="https://github.com/gbdelgado" class="stretched-link" target="_blank" rel="noopener noreferrer">Benni Delgado</a></span></p>
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<img src="https://github.com/mauherrerag.png" style="max-height: 188px;" class="img-fluid img-thumbnail" alt="Mau Herrera headshot">
<p class="mt-2 align-text-bottom"><span class="lead"><a href="https://github.com/mauherrerag" class="stretched-link" target="_blank" rel="noopener noreferrer">Mau Herrera</a></span></p>
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<div class="col-6 col-md-4 col-lg-3">
<img src="https://media.licdn.com/dms/image/D5603AQETyylnoKGcew/profile-displayphoto-shrink_200_200/0/1698089684153?e=2147483647&v=beta&t=svZYIFzQgnFQHJ8DUoU6tvaZu0L9SXqzn9LxK1nrqo8" style="max-height: 188px;" class="img-fluid img-thumbnail" alt="Lauren Tran headshot">
<p class="mt-2 align-text-bottom"><span class="lead"><a href="https://www.linkedin.com/in/laurenktran" class="stretched-link" target="_blank" rel="noopener noreferrer">Lauren Tran</a></span></p>
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<p>In Monsoon Fantasy, players estimate the total monthly precipitation at each of the five major cities in the U.S. Southwest Monsoon region: Tucson, Phoenix, Flagstaff, Albuquerque, and El Paso. Points are awarded each month depending on the accuracy of the estimate compared to the actual observed rainfall. The goal is to accumulate the most points over the July, August, September period.</p>
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<p>This repository contains a description of the scoring system used in the Monsoon Fantasy game and a simulation that was created to compare different scoring methods before the final scoring method was decided upon. The final scoring method takes into account both the risk and accuracy of a players guess. First, a potential maximum points value is determined for a guess. This value is higher the further a guess is from the historical rainfall average. Then, the player gets a percentage of their potential maximum points value depending on how close their guess is to the actual rainfall. Visit our <a href="https://github.com/uaenvironment/monsoon-game-scoring-method" target="_blank" rel="noopener noreferrer">public GitHub repository</a> for more detailed information.</p>
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<p>When signing up to play Monsoon Fantasy, players had the option to fill out profile questions. These questions asked things such as</p>
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<li>How many monsoon seasons have you experienced while living in the southwest?</li>
<li>How would you rate your understanding of the monsoon system?</li>
<li>During monsoon season how often do you consult different types of weather forecasts?</li>
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<p>All of the questions were optional and the responses were made anonymous before analysis. This data combined with the users' forecasts and points earned were used in this analysis which will be performed and the end of every monsoon season.</p>
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<p>This project centralizes public data from several different Flood Control District (FCD) networks across the state of Arizona. This data is stored in a cloud based data warehouse and serves as the central data source for a number of monsoon related projects and research. To gather this data we have written a number of applications that run at different intervals dependent on the different FCD network implementations. These applications run on 15 minute to 1 hour intervals. These time intervals are required in order to obtain incremental precipitation data readings which are not available if gathering data on an hour or day interval. In addition to precipitation data, some FCD sensors also report temperature, pressure, humidity, and stream flow intensity in washes.</p>
<p>This dataset consists of the following remote sensing precipitation networks along with their API programmatic names. Additional networks will be added as they are implemented.</p>
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<li>Pima County FCD - pima_fcd</li>
<li>Maricopa County FCD - maricopa_fcd</li>
<li>RainLog.org - rainlog</li>
<li>MesoWest - mesowest</li>
<li>Mohave County FCD - mohave_fcd (data beginning 2021)</li>
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<p>Data from the networks above are updated at different frequencies and in some cases multiple times per day or hour. This is variable across networks based on their configuration and if precipitation sensors are experiencing rainfall.</p>
<p>The result of this work can be found in a Frontiers in Climate publication titled <a href="https://www.frontiersin.org/articles/10.3389/fclim.2021.602573/full" target="_blank" rel="noopener noreferrer">Curating and Visualizing Dense Networks of Monsoon Precipitation Data: Integrating Computer Science Into Forward Looking Climate Services Development</a>.</p>
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<p>Once data from the Monsoon Scraper project was gathered, we developed a REST API to programmatically query the dataset. The API contains a number of custom routes designed to query specific sets of data. Some of these routes include our monsoon route which returns precipitation totals from specified networks or sensors between June 15th - September 30th of provided years, a flood route which returns data from flood gauge sensors typically found in washes, a sensors metadata route which returns metadata of specific sensors, and a readings route which queries specific sensors or networks using a provided date range.</p>
<p>Currently, API keys are only issued to researchers working with this dataset. There are plans to expand this audience in the future.</p>
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<p>Monsoon Plotter is used to visually represent the data gathered via the Monsoon Scraper project which collects data from the state of Arizona flood control district (FCD) remote sensing networks. There are a handful of networks that can be plotted and more will be added as we expand our Monsoon Scraper project to gather more data. There is also a limited CSV export feature available of the specific data points chosen to be plotted. For full exports of data an API key is required to make programmatic calls to the Monsoon API.</p>
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<p>This Python package serves as a wrapper to simplify REST API calls to the monsoon scraper data warehouse. This is the same dataset that is visually represented in our monsoon plotter found at <a href="https://monsoon.environment.arizona.edu/" target="_blank" rel="noopener noreferrer">monsoon.environment.arizona.edu</a>. The plotter allows a limited export of the data dependent on the date range and sensor network being plotted. This package allows you to incorporate our monsoon dataset into a local codebase for processing.</p>
<p>This package also contains a Command Line Interface (CLI) tool for those who prefer to work within a CLI instead of the Python package.</p>
<p>This repository is in the process of being published to The Python Package Index (PyPi) which serves as a repository of Python software. Instructions will be added here to install the package once published.</p>
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<h4>RAWS Data</h4>
<p>20 years of weather data was pulled for specific stations in AZ during this prototype phase. The data was then cleaned and scaled for use with the intended machine learning technologies.</p>
<h4>Python Package</h4>
<p>A custom RAWSTraining Python package was developed to easily clean and prepare data for use with Tensorflow and Keras to build multi-step forecasting models. With custom classes used for Training and making Predictions, training and testing took only a couple of hours for a single station to be deployed (with non-GPU enabled hardware).</p>
<h4>Web Application</h4>
<p>An application built with Vue3, Firebase, and Firestore was launched to show the predictions and their respective stations. This application featured flagging for potential fireweather conditions, and also assessed the models in realtime to showcase the validity of the predictions being made.</p>
<p>Future implementations of this project will exist within the Fire Data Dynamics prototype.</p>
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<img src="images/fwmachinelearning-map.png" class="img-fluid" alt="Screenshot of the web application showing a station and the generated flags.">
<figcaption class="figure-caption">A screenshot showing the RINCON station and the application's flagging functionality.</figcaption>
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<img src="images/fwmachinelearning-graph.png" class="img-fluid" alt="Graph showing a comparison between the predictive model and real, measured data.">
<figcaption class="figure-caption">An graph showing a trained model's predictions against real, measured data.</figcaption>
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<p>Using data from past fire events in Arizona and New Mexico, our dashboard displays fire occurrences and their locations, fire burn perimeters (when available), and relevant data from Remote Automated Weather Stations (RAWS) during those fire events. The current prototype also allows stakeholders to export this historical data for use with their own analysis. In addition, new features are being developed to incorporate a live fire viewer, perform predictive machine learning analysis for determining fire weather and occurrence likelihood, and incorporating Weather Research & Forecasting Model (WRF) to inform decision makers of the impact weather could have during fire events.</p>
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<p>This repository contains code in R and Python that demonstrates how to create basic machine learning algorithms. It then takes historical weather data from the Tucson International Airport, precipitation data from our Monsoon API, and storm data from NOAA and applies these machine learning algorithms in an attempt to accurately predict flooding using historic data and a database of notable flood and rainfall events.</p>
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<p>Methane emissions have an outsized impact on climate, and are increasingly receiving attention from policymakers and the scientific community. We map methane plume emission rates measured by <a href="https://carbonmapper.org/" target="_blank" rel="noopener noreferrer">Carbon Mapper</a> onto tract-level demographics from the U.S. Census Bureau to explore environmental justice issues associated with the methane emissions. Carbon Mapper collects methane emission data through their airborne pilot projects with advanced remote sensing technology. Census tract-level demographics are obtained from the 2009-2012 five-year moving average <a href="https://www.census.gov/programs-surveys/acs" target="_blank" rel="noopener noreferrer">American Community Survey</a>. This map and scatter plots can be viewed at <a href="https://carbon-plotter.air.arizona.edu/map" target="_blank" rel="noopener noreferrer">Carbon Plotter</a> website.</p>
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<p>LoRaWAN is a Low Power Wide Area Networking (LPWAN) open communications protocol used in a variety of remote sensing and Internet of Things (IoT) applications. AIR supports the <a href="https://tumamoc.arizona.edu/" target="_blank" rel="noopener noreferrer">Desert Laboratory on Tumamoc Hill</a> which is an 860-acre preserve located west of downtown Tucson. AIR has a number of LoRaWAN Gateways and is using Tumamoc as our current testbed for future remote sensing projects.</p>
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<p>This project stemmed from a UN funding award through the Japanese Embassy to provide a mapping service of recycle centers across Lebanon. The project then branched out to include sustainable businesses and other environmentally friendly organizations.</p>
<p>This was a collaboration between a technology company in Lebanon called Imperium Code and other Lebanon non-profits. Our role in the overall project was to provide the mapping solution for the data set. This included mapping the points, displaying their categorizations, as well as implementing a tooltip/popup to display information about each data point. This work can be found on the <a href="https://regeneratehub.org/" target="_blank" rel="noopener noreferrer">Regenerate Hub</a> site.</p>
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<p>This was a proof-of-concept application that was built using Google's Vision AI to detect Coffee Leaf Rust which is a fungus that kills coffee plant crop fields. The idea was to detect this fungus via photos taken by farmers in Jamaica. We would then process those photos through the Vision AI algorithm we created to return the probability of positive coffee rust detection. In turn, this would help government officials to focus remediation efforts.</p>
<p>We developed this proof-of-concept model using plant life located in UArizona's ENR2 building. We trained this model by feeding it various images of different plant life to show how the image analysis would function and the different probability results.</p>
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<p>This project was developed for the <a href="https://haury.arizona.edu/" target="_blank" rel="noopener noreferrer">Agnese Nelms Haury Program in Environment and Social Justice</a> proposal submission process for research funding. The idea was to use models and algorithms in Natural Language Processing (NLP) to find similarities or potential collaboration opportunities across abstract and proposal submissions. We incorporated a number of models/algorithms in this process to determine which is best suited for the particular set of research themes.</p>
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<p>This invasive species tracking application was designed to plot the detection of the Fall Army Worm (FAW) across maize crops in Zambia. The result of this tracking helped governmental agriculture decision makers in focussing remediation efforts of FAW. To achieve this we worked with data aggregated via analog phone text messaging that was collected by an company in Africa called TextIt. We export that data in a format that allowed us to store responses in a relational way to more easily pull data into our front-end plotter. This data was gathered every 2 weeks during their grow season and plotted via the text campaign date. This application was architected in a way that would allow us to track other species in future research. More information about this project can be found on our <a href="http://faw.environment.arizona.edu/" target="_blank" rel="noopener noreferrer">FAW environment</a> site.</p>
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