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김동균 프로필 사진

김동균

Kim, Dong Kyun

공과대학

건설환경공학과

연도별 연구 활동 현황

전체 132건 중 41번부터 50번까지의 결과를 표시합니다.

2024

Article

Performance comparison of retrieval-augmented generation and fine-tuned large language models for construction safety management knowledge retrieval

Article

딥 뉴럴 네트워크를 활용한 강우 나우캐스팅의 최신 동향과 한계 극복을 위한 향후 과제

Article

Evaluation of Optimized Multi-Model Ensembles for Extreme Precipitation Projection Considering Various Objective Functions

Article

A machine learning downscaling framework based on a physically constrained sliding window technique for improving resolution of global water storage anomaly

Article

A socio-economic vulnerability assessment framework against natural disasters: A case study in Seoul, South Korea

Article

Daily river flow simulation using ensemble disjoint aggregating M5-Prime model

Article

Analysis on the temporal scaling behavior of extreme rainfall in Korean Peninsula based on high-resolution radar-based precipitation data

Article

Hybrid machine learning system based on multivariate data decomposition and feature selection for improved multitemporal evapotranspiration forecasting

Article

Incorporation of water quality index models with machine learning-based techniques for real-time assessment of aquatic ecosystems

Article

Innovative approach for estimating evapotranspiration and gross primary productivity by integrating land data assimilation, machine learning, and multi-source observations

상세 보기

김동균

자료유형

발행연도

키워드

언어

Performance comparison of retrieval-augmented generation and fine-tuned large language models for construction safety management knowledge retrieval

딥 뉴럴 네트워크를 활용한 강우 나우캐스팅의 최신 동향과 한계 극복을 위한 향후 과제

Evaluation of Optimized Multi-Model Ensembles for Extreme Precipitation Projection Considering Various Objective Functions

A machine learning downscaling framework based on a physically constrained sliding window technique for improving resolution of global water storage anomaly

A socio-economic vulnerability assessment framework against natural disasters: A case study in Seoul, South Korea

Daily river flow simulation using ensemble disjoint aggregating M5-Prime model

Analysis on the temporal scaling behavior of extreme rainfall in Korean Peninsula based on high-resolution radar-based precipitation data

Hybrid machine learning system based on multivariate data decomposition and feature selection for improved multitemporal evapotranspiration forecasting

Incorporation of water quality index models with machine learning-based techniques for real-time assessment of aquatic ecosystems

Innovative approach for estimating evapotranspiration and gross primary productivity by integrating land data assimilation, machine learning, and multi-source observations