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Environmental Data Science is a peer-reviewed journal published by Cambridge University Press, covering Meteorological Phenomena and Simulations, Climate variability and models, Hydrological Forecasting Using AI.
| Publisher | Cambridge University Press |
|---|---|
| Reference style | Numbered Numbered — [1], [2] in the text [1] A. Smith, B. Jones, and C. Lee, A representative article title, Environmental Data Science 12 (2023) 45–58.
Formats any DOI in the closest standard style — Environmental Data Science has no published style definition, so this is an approximation. No sign-up. |
| Publishes research in | Meteorological Phenomena and Simulations Climate variability and models Hydrological Forecasting Using AI Oceanographic and Atmospheric Processes Atmospheric and Environmental Gas Dynamics |
| ISSN | 2634-4602 |
| Citation impact (2-yr) | 3.85 |
| h-index | 16 |
| i10-index | 34 |
| Total citations | 1,205 |
| Open access | Yes |
| Top institutions publishing here | University of Cambridge |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Abstract Given the growing use of Artificial intelligence (AI) and machine learning (ML) methods across all aspects of environmental sciences, it is imperative that we initiate a discussion about the ethical and responsible use of AI. In fact, much can be learned from other domains where AI was introduced, often with the best of intentions,…
Abstract Despite the increasingly successful application of neural networks to many problems in the geosciences, their complex and nonlinear structure makes the interpretation of their predictions difficult, which limits model trust and does not allow scientists to gain physical insights about the problem at hand. Many different methods have been introduced in the emerging field…
Abstract Energy consumption in buildings, both residential and commercial, accounts for approximately 40% of all energy usage in the United States, and similar numbers are being reported from countries around the world. This significant amount of energy is used to maintain a comfortable, secure, and productive environment for the occupants. So, it is crucial that…
Abstract Modeling dependencies between climate extremes is important for climate risk assessment, for instance when allocating emergency management funds. In statistics, multivariate extreme value theory is often used to model spatial extremes. However, most commonly used approaches require strong assumptions and are either too simplistic or over-parameterized. From a machine learning perspective, generative adversarial networks…
Abstract Classical approaches for flood prediction apply numerical methods for the solution of partial differential equations that capture the physics of inundation processes (e.g., the 2D Shallow Water equations). However, traditional inundation models are still unable to satisfy the requirements of many relevant applications, including early-warning systems, high-resolution (or large spatial domain) simulations, and robust…
