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Environmental Data Science
Scopus
ISSN 2634-4602 · Cambridge University Press · 198 artikel di indeks
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Artikel Jurnal
Actively inferring methane sources with drones
This study presents a framework that combines Bayesian inference with reinforcement learning to guide drone-based sampling for methane source estimation. Synthetic gas concentration and wind observations are generated using a calibrated model derived from real-world drone measurements, providing a…
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Artikel Jurnal
Which meteorological parameters influence extreme wind speed in a wind farm? A heterogeneous Granger causality approach
For efficient wind farm management and optimized power generation under adverse weather conditions, understanding the causal meteorological drivers is essential. In this paper, we investigate the temporal causal influences of wind speed-related meteorological processes within a wind farm using the…
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Artikel Jurnal
Utilization of artificial intelligence and thermal cameras in material analysis for hot-summer Mediterranean climates
This study aims to evaluate the thermal behaviors of surface materials in arid climates to enhance environmental sustainability and energy efficiency. Conducted over 1 year at Dokumapark in Antalya, Turkey, it examines surface temperatures of asphalt, concrete, granite, wood, grass, and soil using…
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Artikel Jurnal
Skillful subseasonal Indian Ocean marine heatwave forecasts using a neural network
Marine heat waves (MHWs) are prolonged periods of elevated ocean temperatures that can devastate marine ecosystems, fisheries, and coastal communities. Skillfully predicting these events with sufficient lead time is crucial for mitigating their adverse effects. This study presents a probabilistic…
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Artikel Jurnal
Predicting stream water temperature: A data-driven approach highlighting the impact of riparian vegetation
Stream water temperature strongly influences aquatic ecosystem health, affecting dissolved oxygen, species distributions, and thermal stress for sensitive taxa. We present a data-driven framework to predict water temperatures across rivers in the Canton of Vaud, Switzerland, explicitly quantifying…
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Artikel Jurnal
Precipitation nowcasting of satellite data using physically aligned neural networks
Accurate short-term precipitation forecasts predominantly rely on dense weather-radar networks, limiting operational value in places most exposed to climate extremes. We present TUPANN (Transferable and Universal Physics-Aligned Nowcasting Network), a satellite-only model trained on GOES-16 RRQPE…
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Artikel Jurnal
Correcting dry/wet classification bias in precipitation downscaling via generative adversarial networks
Accurately downscaling precipitation from coarse to high spatial resolutions remains a critical challenge in climate and hydrometeorological modeling. A key limitation is the frequent misclassification of dry and wet regions, which compromises the realism and reliability of high-resolution outputs…
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Artikel Jurnal
Downscaling land surface temperature data using edge detection and block-diagonal Gaussian process regression
Accurate and high-resolution estimation of land surface temperature (LST) is crucial in estimating evapotranspiration, a measure of plant water use and a central quantity in agricultural applications. In this work, we develop a novel statistical method for downscaling LST data obtained from NASA’s…
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Artikel Jurnal
Data-driven integration kernels for interpretable nonlocal operator learning
Machine learning models can represent climate processes that are nonlocal in horizontal space, height, and time, often by combining information across these dimensions in highly nonlinear ways. While this can improve predictive skill, it makes learned relationships difficult to interpret and prone…
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Artikel Jurnal
A machine learning approach to detecting environmental crimes in Brazil
This article contributes a machine learning approach to detecting environmental crimes in Brazil. Detecting these crimes is important for the health of the environment and public safety. The measurement of environmental crimes is challenging, in part due to efforts of those involved to remain…
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Human-centric skills are essential for the responsible and rigorous application of AI in ecology
Artificial intelligence (AI) can rapidly process large ecological datasets, uncover patterns, and inform conservation decisions, but responsible adoption depends as much on human-centric skills as on technical methods. Ecologists face steep learning curves, an overwhelming and fast-evolving model…
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Artikel Jurnal
Cross-domain offshore wind power forecasting: transfer learning through meteorological clusters
Ambitious decarbonization targets are rapidly increasing the commissioning of new offshore wind farms. For these newly commissioned plants to run, accurate power forecasts are needed from the onset. These allow grid stability, good reserve management, and efficient energy trading. Despite machine…
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Artikel Jurnal
CNN-based forecasting of early winter NAO using sea surface temperature
The North Atlantic Oscillation (NAO) is the dominant mode of atmospheric variability over the North Atlantic, influencing temperature and precipitation across Europe. While the NAO’s impact on North Atlantic sea surface temperatures (SSTs) is well understood, the NAO can also be driven by SST…
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Dissipating the correlation smokescreen: causal decomposition of the radiative effects of biomass burning aerosols over the South-East Atlantic
Biomass burning aerosols (BBAs) from Southern Africa seasonally overlie the semi-permanent South-East Atlantic (SEA) stratocumulus deck, impacting the region’s energy budget through complex aerosol-cloud-radiation-meteorology interactions. Climate model intercomparison initiatives, like the Aerosol…
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Artikel Jurnal
Calibrated conformal prediction intervals for microphysical process rates
Conformal prediction (CP) can yield statistically valid prediction intervals for any regression model, with no model modifications and small computational costs. To assess its practical value, we apply conformal methods to quantify uncertainty in machine learning emulators of six microphysical…
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Artikel Jurnal
Emulating non-differentiable metrics via knowledge-guided learning: Introducing the Minkowski image loss
The “differentiability gap” presents a primary bottleneck in Earth system deep learning: Since models cannot be trained directly on non-differentiable scientific metrics and must rely on smooth proxies (e.g., MSE), they often fail to capture high-frequency details, yielding “blurry” outputs. We…
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One stone three birds: Three-dimensional implicit neural network for compression and continuous representation of multi-altitude climate data
Wind energy stands out as a promising clean and renewable energy alternative, not only for its potential to combat global warming but also for its capacity to meet the ever-growing demand for energy. However, analysis of wind data to fully harness the benefits of wind energy demands tackling…
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Artikel Jurnal
Explainable machine learning highlights the role of diffuse radiation in ecosystem carbon uptake of boreal forest
Boreal forests play a critical role in the global carbon cycle as they are one of the largest terrestrial carbon sinks globally. In this study, we employ explainable machine learning (ML) techniques to investigate the influence of environmental and vegetation variables on net ecosystem exchange…
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Artikel Jurnal
Future wildfire risk in Southern Europe under changing land use, population, and climate: a data-driven approach
Wildfires are one of the most devastating natural disasters with wide impacts across economic sectors and society in Europe. It is, however, unclear how wildfire risk will evolve in the future under different climate scenarios, as it depends on future climate change scenarios as well as on land use…