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A Deep Learning Approach for Modeling and Hindcasting Lake Michigan Ice Cover

In large lakes, ice cover plays an important role in shipping and navigation, coastal erosion, regional weather and climate, and aquatic ecosystem function. In this study, a novel deep learning model for ice cover concentration prediction in Lake Mic

Externí odkaz: http://arxiv.org/abs/2407.04937

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Akademický článek

Genetic Architecture of Soybean Yield and Agronomic Traits

Publikováno v: G3: Genes, Genomes, Genetics, Vol 8, Iss 10, Pp 3367-3375 (2018)

Soybean is the world’s leading source of vegetable protein and demand for its seed continues to grow. Breeders have successfully increased soybean yield, but the genetic architecture of yield and key agronomic traits is poorly understood. We develo

Externí odkaz: https://doaj.org/article/854c68e899e44be5be6a2f64684e1e21

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Unveiling Significant Shoreline Changes in Lake Michigan After a Record-Setting Water Level Increase using High-Resolution Satellite Images

Autor: Abdelhady, Hazem U., Troy, Cary D., Zhu, Longhuan, Xue, Pengfei, Meadows, Guy, Wu, Chin H.

In this paper, high-resolution multispectral satellite images were used to uncover a remarkable shoreline transformation in Lake Michigan coastal areas, driven by a record-setting increase in the water level between 2013 and 2020. Shoreline change an

Externí odkaz: http://arxiv.org/abs/2309.14219

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A Machine Learning Framework for Extending Wave Height Time Series Using Historical Wind Records

Autor: Abdelhady, Hazem U., Troy, Cary D.

This study presents a novel machine learning-based (ML) framework that utilizes the ConvLSTM-1D model to hindcast or forecast wave heights at coastal locations using a nonuniform array of wind observations. This approach was applied to Lake Michigan

Externí odkaz: http://arxiv.org/abs/2309.14204

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