CIRA Guide to Custom Loss Functions for Neural Networks in Environmental Sciences -- Version 1

Ebert‐Uphoff, Imme; Lagerquist, Ryan; Hilburn, Kyle; Lee, Yoonjin; Haynes, Katherine; Stock, Jason; Kumler, Christina; Stewart, Jebb Q.

doi:10.48550/arxiv.2106.09757

Cited by 2 publications

(2 citation statements)

References 35 publications

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“…A weight matrix is created and has the same shape as the loss tensor, where the weight value for any pixel is determined by the WoFS value and the total loss is the element wise product between the weight matrix and the loss tensor. Creating a custom loss function with weighting is inspired by Ebert-Uphoff et al (2021).…”

Section: B Machine Learning Methodsmentioning

confidence: 99%

Machine Learning Estimation of Maximum Vertical Velocity from Radar

Chase,

McGovern,

Homeyer

et al. 2024

Artificial Intelligence for the Earth Systems

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The quantification of storm updrafts remains unavailable for operational forecasting despite their inherent importance to convection and its associated severe weather hazards. Updraft proxies, like overshooting top area from satellite images, have been linked to severe weather hazards but only relate to a limited portion of the total storm updraft. This study investigates if a machine learning model, namely U-Nets, can skillfully retrieve maximum vertical velocity and its areal extent from 3-dimensional gridded radar reflectivity alone. The machine learning model is trained using simulated radar reflectivity and vertical velocity from the National Severe Storm Laboratory’s convection permitting Warn on Forecast System (WoFS). A parametric regression technique using the sinh-arcsinh-normal distribution is adapted to run with U-Nets, allowing for both deterministic and probabilistic predictions of maximum vertical velocity. The best models after hyperparameter search provided less than 50% root mean squared error, a coefficient of determination greater than 0.65 and an intersection over union (IoU) of more than 0.45 on the independent test set composed of WoFS data. Beyond the WoFS analysis, a case study was conducted using real radar data and corresponding dual-Doppler analyses of vertical velocity within a supercell. The U-Net consistently underestimates the dual-Doppler updraft speed estimates by 50%. Meanwhile, the area of the 5 and 10 m s−1 updraft cores show an IoU of 0.25. While the above statistics are not exceptional, the machine learning model enables quick distillation of 3D radar data that is related to the maximum vertical velocity which could be useful in assessing a storm’s severe potential.

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Section: B Machine Learning Methodsmentioning

confidence: 99%

Machine Learning Estimation of Maximum Vertical Velocity from Radar

Chase,

McGovern,

Homeyer

et al. 2024

Artificial Intelligence for the Earth Systems

View full text Add to dashboard Cite

show abstract

“…Therefore, knowledge-based functions are necessary to exert a bigger penalty on wrongly-predicted pixels in coastal areas than in deep ocean area or land area. For environmental science applications, there is a guide to custom loss function for neural networks [20].…”

Section: Knowledge-based Loss Functions For Deep Learningmentioning

confidence: 99%

Enhanced Deep Learning Super-Resolution for Bathymetry Data

Williams

et al. 2022

2022 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT)

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Spatial resolution is critical for observing and monitoring environmental phenomena. Acquiring high-resolution bathymetry data directly from satellites is not always feasible due to limitations on equipment, so spatial data scientists and researchers turn to single image super-resolution (SISR) methods that utilize deep learning techniques as an alternative method to increase pixel density. While super resolution residual networks (e.g., SR-ResNet) are promising for this purpose, several challenges still need to be addressed: (1) Earth data such as bathymetry is expensive to obtain and relatively limited in its data record amount; (2) certain domain knowledge needs to be complied with during model training; (3) certain areas of interest require more accurate measurements than other areas. To address these challenges, following the transfer learning principle, we study how to leverage an existing pre-trained superresolution deep learning model, namely SR-ResNet, for highresolution bathymetry data generation. We further enhance the SR-ResNet model to add corresponding loss functions based on domain knowledge. To let the model perform better for certain spatial areas, we add additional loss functions to increase the penalty of the areas of interest. Our experiments show our approaches achieve higher accuracy than most baseline models when evaluating using metrics including MSE, PSNR, and SSIM.

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CIRA Guide to Custom Loss Functions for Neural Networks in Environmental Sciences -- Version 1

Cited by 2 publications

References 35 publications

Machine Learning Estimation of Maximum Vertical Velocity from Radar

Machine Learning Estimation of Maximum Vertical Velocity from Radar

Enhanced Deep Learning Super-Resolution for Bathymetry Data

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