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Shih-Chieh Kao

Government of the United States of America - Oak Ridge National Laboratory

SCHOLARLY PAPERS

2

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100

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Scholarly Papers (2)

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1.

Intercomparison of Flood Inundation Models Across Land Use Types and Hydrological Flood Stages

Number of pages: 48 Posted: 15 Oct 2025
Parvaneh Nikrou, Sadra Seyvani, Sagy Cohen, Sudershan Gangrade, Joseph L. Gutenson, Dan Tian, Anupal Baruah, Michael L. Follum and Shih-Chieh Kao
affiliation not provided to SSRN, affiliation not provided to SSRN, affiliation not provided to SSRN, affiliation not provided to SSRN, affiliation not provided to SSRN, University of Alabama, affiliation not provided to SSRN, affiliation not provided to SSRN and Government of the United States of America - Oak Ridge National Laboratory
Downloads 78 (837,727)

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Flood Inundation Mapping, Hydrodynamic Modeling, Terrain-based Flood Models, Neuse River Basin, Remote sensing validation

2.

From Regulatory Text to Structured Data: A Domain-Specific LLM Framework for Extracting Hydropower Operational Constraints

Downloads 22 (1,491,251)

From Regulatory Text to Structured Data: A Domain-Specific LLM Framework for Extracting Hydropower Operational Constraints

Number of pages: 23 Posted: 09 Apr 2026
Faisal Bin Ashraf, Hong-Jun Yoon, Carly H. Hansen, Thomas A. Ruggles, Debjani Singh, Esther S. Parish and Shih-Chieh Kao
University of Oulu, Government of the United States of America - Oak Ridge National Laboratory, affiliation not provided to SSRN, Government of the United States of America - Oak Ridge National Laboratory, Government of the United States of America - Oak Ridge National Laboratory, Government of the United States of America - Oak Ridge National Laboratory and Government of the United States of America - Oak Ridge National Laboratory
Downloads 17 (1,548,841)

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Large language models, information extraction, Prompt Engineering, regulatory documents, Natural Language Processing, domain knowledge

From Regulatory Text to Structured Data: A Domain-Specific LLM Framework for Extracting Hydropower Operational Constraints

Number of pages: 23 Posted: 16 Jun 2026
Faisal Bin Ashraf, Hong-Jun Yoon, Carly H. Hansen, Thomas A. Ruggles, Debjani Singh, Esther S. Parish and Shih-Chieh Kao
University of Oulu, Government of the United States of America - Oak Ridge National Laboratory, affiliation not provided to SSRN, Government of the United States of America - Oak Ridge National Laboratory, Government of the United States of America - Oak Ridge National Laboratory, Government of the United States of America - Oak Ridge National Laboratory and Government of the United States of America - Oak Ridge National Laboratory
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large language models, information extraction, Prompt Engineering, regulatory documents, Natural Language Processing, domain knowledge