Prediction of Microcystis Occurrences and Analysis Using Machine Learning in High-Dimension, Low-Sample-Size and Imbalanced Water Quality Data

Harmful Algae, Journal Year: 2022, Volume and Issue: 117, P. 102273 - 102273

Published: June 25, 2022

Language: Английский

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Forecasting seed production in perennial plants: identifying challenges and charting a path forward

New Phytologist, Journal Year: 2023, Volume and Issue: 239(2), P. 466 - 476

Published: May 18, 2023

Summary Interannual variability of seed production, known as masting, has far‐reaching ecological impacts including effects on forest regeneration and the population dynamics consumers. Because relative timing management conservation efforts in ecosystems dominated by masting species often determines their success, there is a need to study mechanisms develop forecasting tools for production. Here, we aim establish production new branch discipline. We evaluate predictive capabilities three models – foreMast, Δ T , sequential model designed predict trees using pan‐European dataset Fagus sylvatica The are moderately successful recreating dynamics. availability high‐quality data prior improved model's power, suggesting that effective monitoring methods crucial creating tools. In terms extreme events, better at predicting crop failures than bumper crops, likely because factors preventing understood processes leading large reproductive events. summarize current challenges provide roadmap help advance discipline encourage further development mast forecasting.

Language: Английский

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Forecasting the abundance of disease vectors with deep learning

Ecological Informatics, Journal Year: 2023, Volume and Issue: 78, P. 102272 - 102272

Published: Aug. 20, 2023

Arboviral diseases such as dengue, Zika, chikungunya or yellow fever are a worldwide concern. The abundance of vector species plays key role in the emergence outbreaks these diseases, so forecasting numbers is fundamental preventive risk assessment. Here we describe and demonstrate novel approach that uses state-of-the-art deep learning algorithms to forecast disease abundances. Unlike classical statistical machine methods, models use time series data directly predictors identify features most relevant from predictive perspective. We for first application this predict short-term temporal trends number Aedes aegypti mosquito eggs across Madeira Island period 2013 2019. Specifically, apply whether, following week, Ae. will remain unchanged, whether it increase decrease, considering different percentages change. obtained high performance all years considered (mean AUC = 0.92 ± 0.05 SD). Our performed better than methods. also found preceding highly informative predictor future trends. Linking our transmission importation contribute operational, early warning systems arboviral risk.

Language: Английский

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Increase in MJO predictability under global warming

Nature Climate Change, Journal Year: 2023, Volume and Issue: 14(1), P. 68 - 74

Published: Dec. 20, 2023

Language: Английский

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Early detection of sea lice epidemic transitions and changes in long-term abundance levels in salmon farming areas

Aquaculture, Journal Year: 2025, Volume and Issue: unknown, P. 742385 - 742385

Published: March 1, 2025

Language: Английский

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