Machine learning and the quest for objectivity in climate model parameterization

Jebeile, Julie; Lam, Vincent; Majszak, Mason; Räz, Tim

doi:10.1007/s10584-023-03532-1

Cited by 5 publications

(2 citation statements)

References 43 publications

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“…This involves several key steps. Parameter tuning ( Jebeile et al, 2023 ) which fine-tunes the model parameters to optimize performance. Feature engineering focuses on selecting or creating relevant features to enhance model accuracy and predictive power.…”

Section: Discussionmentioning

confidence: 99%

An adaptive data-driven architecture for mental health care applications

Sundaram,

Subramaniam,

Ab Hamid

et al. 2024

PeerJ

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Background In the current era of rapid technological innovation, our lives are becoming more closely intertwined with digital systems. Consequently, every human action generates a valuable repository of digital data. In this context, data-driven architectures are pivotal for organizing, manipulating, and presenting data to facilitate positive computing through ensemble machine learning models. Moreover, the COVID-19 pandemic underscored a substantial need for a flexible mental health care architecture. This architecture, inclusive of machine learning predictive models, has the potential to benefit a larger population by identifying individuals at a heightened risk of developing various mental disorders. Objective Therefore, this research aims to create a flexible mental health care architecture that leverages data-driven methodologies and ensemble machine learning models. The objective is to proficiently structure, process, and present data for positive computing. The adaptive data-driven architecture facilitates customized interventions for diverse mental disorders, fostering positive computing. Consequently, improved mental health care outcomes and enhanced accessibility for individuals with varied mental health conditions are anticipated. Method Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, the researchers conducted a systematic literature review in databases indexed in Web of Science to identify the existing strengths and limitations of software architecture relevant to our adaptive design. The systematic review was registered in PROSPERO (CRD42023444661). Additionally, a mapping process was employed to derive essential paradigms serving as the foundation for the research architectural design. To validate the architecture based on its features, professional experts utilized a Likert scale. Results Through the review, the authors identified six fundamental paradigms crucial for designing architecture. Leveraging these paradigms, the authors crafted an adaptive data-driven architecture, subsequently validated by professional experts. The validation resulted in a mean score exceeding four for each evaluated feature, confirming the architecture’s effectiveness. To further assess the architecture’s practical application, a prototype architecture for predicting pandemic anxiety was developed.

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Section: Discussionmentioning

confidence: 99%

An adaptive data-driven architecture for mental health care applications

Sundaram,

Subramaniam,

Ab Hamid

et al. 2024

PeerJ

View full text Add to dashboard Cite

show abstract

“…These limitations do not mean we cannot use ML in the carbon cycle. A potential application of ML could be to optimise parameters in process-based models (Bastrikov et al, 2018;Dagon et al, 2020;Jebeile et al, 2023;Li et al, 2023a), or even replace parametrisations in such processbased models by ML (Kraft et al, 2022). A second potential application of ML is bias correction of process-based models (Petetin et al, 2022).…”

Section: Process-unaware Modellingmentioning

confidence: 99%