The AIHABs Project: Towards an Artificial Intelligence-Powered Forecast for Harmful Algal Blooms

Cobo, Fernando; Vieira‐Lanero, Rufino; Barca, Sandra; Cobo, M. Carmen; Quesada, Antonio; Nasr, Ahmed Elssidig; Bedri, Zeinab; Álvarez-Cid, Marcos Xosé; Saberioon, Mohammadmehdi; Brom, Jakub; Espiña, Begoña

doi:10.3390/blsf2022014013

Cited by 3 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, lake freshwater quality forecast models may need to account for watershed inputs that are integrated into lake water quality, particularly over seasonal or annual time scales. However, recent innovations in freshwater quality forecasting methodology, including embedding freshwater‐relevant physical processes into machine learning model architectures (Daw et al, 2020; Read et al, 2019) and data assimilation of multiple freshwater quality data streams with different attributes (Abdul Wahid & Arunbabu, 2022; Chen et al, 2021; Cho et al, 2020; Cobo et al, 2022), illustrate the benefits of adopting practices from other disciplines for water quality forecasting.…”

Section: Discussion and Synthesis: Opportunities To Advance Near‐term...mentioning

confidence: 99%

Progress and opportunities in advancing near‐term forecasting of freshwater quality

Lofton

Howard

Thomas

et al. 2023

Global Change Biology

View full text Add to dashboard Cite

Near‐term freshwater forecasts, defined as sub‐daily to decadal future predictions of a freshwater variable with quantified uncertainty, are urgently needed to improve water quality management as freshwater ecosystems exhibit greater variability due to global change. Shifting baselines in freshwater ecosystems due to land use and climate change prevent managers from relying on historical averages for predicting future conditions, necessitating near‐term forecasts to mitigate freshwater risks to human health and safety (e.g., flash floods, harmful algal blooms) and ecosystem services (e.g., water‐related recreation and tourism). To assess the current state of freshwater forecasting and identify opportunities for future progress, we synthesized freshwater forecasting papers published in the past 5 years. We found that freshwater forecasting is currently dominated by near‐term forecasts of water quantity and that near‐term water quality forecasts are fewer in number and in the early stages of development (i.e., non‐operational) despite their potential as important preemptive decision support tools. We contend that more freshwater quality forecasts are critically needed and that near‐term water quality forecasting is poised to make substantial advances based on examples of recent progress in forecasting methodology, workflows, and end‐user engagement. For example, current water quality forecasting systems can predict water temperature, dissolved oxygen, and algal bloom/toxin events 5 days ahead with reasonable accuracy. Continued progress in freshwater quality forecasting will be greatly accelerated by adapting tools and approaches from freshwater quantity forecasting (e.g., machine learning modeling methods). In addition, future development of effective operational freshwater quality forecasts will require substantive engagement of end users throughout the forecast process, funding, and training opportunities. Looking ahead, near‐term forecasting provides a hopeful future for freshwater management in the face of increased variability and risk due to global change, and we encourage the freshwater scientific community to incorporate forecasting approaches in water quality research and management.

show abstract

Section: Discussion and Synthesis: Opportunities To Advance Near‐term...mentioning

confidence: 99%

Progress and opportunities in advancing near‐term forecasting of freshwater quality

Lofton

Howard

Thomas

et al. 2023

Global Change Biology

View full text Add to dashboard Cite

show abstract

“…Forecasting techniques and ideas gleaned from other disciplines will likely require adaptation to account for unique attributes of water quality data and freshwater ecosystem processes before being applied in a freshwater quality forecasting context. However, recent innovations in freshwater quality forecasting methodology, including embedding freshwaterrelevant physical processes into machine learning model architectures (Daw et al, 2020; and data assimilation of multiple freshwater quality data streams with different attributes (Abdul Wahid & Arunbabu, 2022;Chen et al, 2021;Cho et al, 2020;Cobo et al, 2022), illustrate the benefits of adapting practices from other disciplines for water quality forecasting.…”

Section: Integration Of Insights From Other Forecasting Disciplinesmentioning

confidence: 99%

Progress and opportunities in advancing near-term forecasting of freshwater quality

Lofton

Howard

Thomas

et al. 2022

Preprint

View full text Add to dashboard Cite

Near-term freshwater forecasts, defined as sub-daily to decadal future predictions of a freshwater variable with quantified uncertainty, are urgently needed to improve water quality management as freshwater ecosystems exhibit greater variability due to global change. Shifting baselines in freshwater ecosystems due to land use and climate change prevent managers from relying on historical averages for predicting future conditions, necessitating near-term forecasts to mitigate freshwater risks to human health and safety (e.g., flash floods, harmful algal blooms). To assess the current state of freshwater forecasting and identify opportunities for future progress, we synthesized freshwater forecasting papers published in the past five years. We found that freshwater forecasting is currently dominated by near-term forecasts of water quantity and that near-term water quality forecasts are fewer in number and in early stages of development (i.e., non-operational), despite their potential as important preemptive decision support tools. We contend that more freshwater quality forecasts are critically needed, and that near-term water quality forecasting is poised to make substantial advances based on examples of recent progress in forecasting methodology, workflows, and end user engagement. For example, current water quality forecasting systems can predict water temperature, dissolved oxygen, and algal bloom/toxin events five days ahead with reasonable accuracy. Continued progress in freshwater quality forecasting will be greatly accelerated by adapting tools and approaches from freshwater quantity forecasting (e.g., machine learning modeling methods). In addition, future development of effective operational freshwater quality forecasts necessitates substantive engagement of end users throughout the forecast process, funding, and training opportunities. Looking ahead, near-term forecasting provides a hopeful future for freshwater management in the face of increased variability and risk due to global change, and we encourage the freshwater scientific community to incorporate forecasting approaches in water quality research and management.

show abstract

Research on Cyanobacterial Blooms and Cyanotoxin Production in Galician Inland Waters

Gradín,

Bravo,

Lanero

et al. 2023

The Environment in Galicia: A Book of Images

View full text Add to dashboard Cite

The AIHABs Project: Towards an Artificial Intelligence-Powered Forecast for Harmful Algal Blooms

Abstract: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

Cited by 3 publications

References 0 publications

Progress and opportunities in advancing near‐term forecasting of freshwater quality

Progress and opportunities in advancing near‐term forecasting of freshwater quality

Progress and opportunities in advancing near-term forecasting of freshwater quality

Research on Cyanobacterial Blooms and Cyanotoxin Production in Galician Inland Waters

Contact Info

Product

Resources

About