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- College of Forest Resources and Environmental Science (2)
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- Lake surface temperature (2)
- Decay (1)
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- Department of Mechanical Engineering-Engineering Mechanics (1)
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Articles 1 - 3 of 3
Full-Text Articles in Forest Sciences
Reconstructing 42 Years (1979–2020) Of Great Lakes Surface Temperature Through A Deep Learning Approach, Miraj Kayastha, Tao Liu, Daniel Titze, Timothy C. Havens, Chenfu Huang, Pengfei Xue
Reconstructing 42 Years (1979–2020) Of Great Lakes Surface Temperature Through A Deep Learning Approach, Miraj Kayastha, Tao Liu, Daniel Titze, Timothy C. Havens, Chenfu Huang, Pengfei Xue
Michigan Tech Publications, Part 2
Accurate estimates for the lake surface temperature (LST) of the Great Lakes are critical to understanding the regional climate. Dedicated lake models of various complexity have been used to simulate LST but they suffer from noticeable biases and can be computationally expensive. Additionally, the available historical LST datasets are limited by either short temporal coverage (<30 >years) or lower spatial resolution (0.25° × 0.25°). Therefore, in this study, we employed a deep learning model based on Long Short-Term Memory (LSTM) neural networks to produce a daily LST dataset for the Great Lakes that spans an unparalleled 42 years (1979–2020) at …
Integrating Deep Learning And Hydrodynamic Modeling To Improve The Great Lakes Forecast, Pengfei Xue, Aditya Wagh, Gangfeng Ma, Yilin Wang, Yongchao Yang, Tao Liu, Chenfu Huang
Integrating Deep Learning And Hydrodynamic Modeling To Improve The Great Lakes Forecast, Pengfei Xue, Aditya Wagh, Gangfeng Ma, Yilin Wang, Yongchao Yang, Tao Liu, Chenfu Huang
Michigan Tech Publications
The Laurentian Great Lakes, one of the world’s largest surface freshwater systems, pose a modeling challenge in seasonal forecast and climate projection. While physics-based hydrodynamic modeling is a fundamental approach, improving the forecast accuracy remains critical. In recent years, machine learning (ML) has quickly emerged in geoscience applications, but its application to the Great Lakes hydrodynamic prediction is still in its early stages. This work is the first one to explore a deep learning approach to predicting spatiotemporal distributions of the lake surface temperature (LST) in the Great Lakes. Our study shows that the Long Short-Term Memory (LSTM) neural network, …
Nondestructive Assessment Of Wood Members In A Viewing Tower In Potawatomi State Park, Door County, Wisconsin, Us, Robert Ross, Xiping Wang, C. Adam Senalik
Nondestructive Assessment Of Wood Members In A Viewing Tower In Potawatomi State Park, Door County, Wisconsin, Us, Robert Ross, Xiping Wang, C. Adam Senalik
College of Forest Resources and Environmental Science Publications
The State of Wisconsin’s Department of Natural Resources is responsible for operating one of the largest state park systems in the United States. Potawatomi State Park, located on the Door County peninsula, consists of about 1,200 acres of flat to gently rolling upland terrain bordered by steep slopes and rugged limestone cliffs along Lake Michigan’s shoreline. A 75-ft observation tower sits atop a 150-ft bluff overlooking Lake Michigan. The USDA Forest Service, Forest Products Laboratory, was asked to conduct an assessment of the main support timbers of the tower. This report summarizes the results obtained from the inspection and assessment. …