Improving Rainfall Index Insurance: Evaluating Effects of Fine-Scale Data and Interactive Tools in the PRF-RI Program

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dc.contributor.authorRamanujan, Ramarajaen
dc.contributor.committeechairFox, Edward A.en
dc.contributor.committeechairBenami, Elinoren
dc.contributor.committeememberChen, Yanen
dc.contributor.departmentComputer Science and#38; Applicationsen
dc.date.accessioned2024-06-05T08:04:09Zen
dc.date.available2024-06-05T08:04:09Zen
dc.date.issued2024-06-04en
dc.description.abstractSince its inception, the Pasture, Rangeland, and Forage Rainfall Index (PRF-RI) insurance program has issued a total of $8.8 billion in payouts. Given the program's significance, this thesis investigates methodologies to help improve it. For the first part, we evaluated the impact of finer-scale precipitation data on insurance payouts by comparing how the payout distribution differs between the program's current dataset and the finer-scale precipitation dataset by creating a simulated scenario where all parameters are constant except the rainfall index computed by the respective dataset. The analysis for Texas in 2021 revealed that using the finer-scale dataset to compute the rainfall index would result in payouts worth $27 million less than the current dataset. The second part of the research involved the development of two interactive decision-support tools: the "Next-Gen PRF" web tool and the "AgInsurance LLM" chatbot. These tools were designed to help users understand complex insurance parameters and make informed decisions regarding their insurance policies. User studies for the "Next-Gen PRF" tool measured usability, comprehension decision-making efficiency, and user experience, showing that it outperforms traditional methods by providing insightful visualizations and detailed descriptions. The findings suggest that using fine-scale precipitation data and advanced decision-support technologies can substantially benefit the PRF-RI program by reducing spatial basis risk and promoting user education, thus leading to higher user engagement and enrollment.en
dc.description.abstractgeneralThe Pasture, Rangeland, and Forage Rainfall Index (PRF-RI) program helps farmers manage drought risk. Since it started, it has paid farmers about $8.8 billion. This study looks into ways to improve the program. We first examined whether using rain data at a more finer spatial resolution could affect how much money is paid out. In Texas in 2021, we found that using this finer spatial resolution data could have reduced payouts by $27 million, underscoring the importance of evaluating our proposed change. Additionally, we created two new tools to help farmers understand and choose their insurance options more easily: the "Next-Gen PRF" web tool and the "AgInsurance LLM" chatbot. These tools seek to provide clear visuals and explanations. User studies with these tools show they help users learn more effectively and make more informed decisions compared to existing tools. Overall, our research suggests that using finer spatial resolution precipitation data as well as these interactive tools can enhance the insurance program, including by making it easier to engage with, and enabling farmers to evaluate if and how this program can help them resolve their weather risk management problems.en
dc.description.degreeMaster of Scienceen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:40959en
dc.identifier.urihttps://hdl.handle.net/10919/119295en
dc.language.isoenen
dc.publisherVirginia Techen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectgridded weatheren
dc.subjectindex insuranceen
dc.subjectspatial resolutionen
dc.subjecthuman-computer interactionen
dc.subjectlarge language modelsen
dc.titleImproving Rainfall Index Insurance: Evaluating Effects of Fine-Scale Data and Interactive Tools in the PRF-RI Programen
dc.typeThesisen
thesis.degree.disciplineComputer Science & Applicationsen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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