SBIR Phase I: Hydro-financial modeling architecture for the automated optimization of low basis risk indices

小型企业创新研究(SBIR)第一阶段项目的更广泛影响/商业潜力是提高了依赖稳定水条件的众多行业的财务弹性。这项创新将使旱灾和洪灾带来的金融风险能够在一个日益有效的市场中转移和汇集。在美国，超过2500亿美元的工业活动依赖于能源、农业和公用事业市场上稳定的淡水系统。洪水、干旱、冰冻和其他条件每年仅在美国就摧毁了大约100亿美元的经济价值--在准确量化并绘制出明确和可衡量的指标之前，这些价值无法恢复。这项SBIR研究将能够生成准确的风险指数，使不透明和复杂的不稳定水状况的财务影响变得清晰。这将提高基于指数的保险合同的可及性、成本和有效性，为企业提供关键的财政救济，使其免受旱灾和洪灾的影响，这些旱灾和洪水阻碍了企业的运营。这类合同还为投资者创造了新的投资机会，带来了多样化的好处。这个小型企业创新研究(SBIR)第一阶段项目致力于克服以下技术挑战：1)结合和分析与水文-金融风险相关的海量异质数据集；2)管理涵盖水文学、工业运营、市场和精算科学的多种模型；3)优化数据和模型的配置，以生成准确和精确的风险指数。该项目将构建和测试水文-金融风险的统一语义数据模型，并进行优化，以最大限度地提高特定工业用例指数的准确性。该架构允许自动配置不同的数据集，并部署一流的建模和优化工具以生成详细的风险指数。这项研究将在必要时创造新的词汇，以弥合水文学、工业资产运营、精算分析和金融市场状况等现有本体之间的差距。如果研究成功，这项研究将大大减少进行水文-财务风险分析所需的时间和成本，并减少这些分析中的错误情况。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is the improved financial resilience of the numerous industries that are reliant on stable water conditions. The innovation will enable the transfer and pooling of the financial risks posed by drought and flood in an increasingly efficient market. In the US, over $250B of industrial activity depends on stable freshwater systems across the energy, agricultural, and utility markets. Floods, droughts, freezes, and other conditions destroy roughly $10B in economic value annually in the US alone - value which cannot be recovered until it is accurately quantified and mapped to clear and measurable indicators. This SBIR research will enable the generation of accurate risk indices that bring clarity to the opaque and complex financial impacts of volatile water conditions. This will improve the accessibility, cost, and effectiveness of index-based insurance contracts, which provide businesses with crucial financial relief from the droughts and floods that hamper their operations. Such contracts also create new investment opportunities with diversification benefits for investors. This Small Business Innovation Research (SBIR) Phase I project seeks to overcome the technical challenges of seamlessly and scalably 1) combining and analyzing massive heterogeneous datasets relevant to hydrologic-financial risks, 2) managing a diverse set of models that cover hydrology, industrial operations, markets, and actuarial sciences, and 3) optimizing the configuration of data and models to generate accurate and precise risk indices. This project will construct and test a unified semantic data model for hydrologic-financial risk and deploy optimizations to maximize accuracy of the indices for a specific industrial use case. The architecture allows for the automated provisioning of disparate datasets and the deployment of best-in-class modeling and optimization tools to generate detailed risk indices. The research will create new vocabulary, when necessary, to bridge the gaps between existing ontologies in hydrology, industrial asset operations, actuarial analysis, and financial market conditions. If successful, the research will enable a dramatic reduction in both the time and cost required to produce hydrologic-financial risk analyses and the instances of errors in those analyses.