Harrmful Algal Bloom Prediction using Emprical Dynamic Modelling DOI Creative Commons
Özlem Baydaroğlu

EarthArXiv (California Digital Library), Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 2, 2024

Harmful Algal Blooms (HABs) can originate from a variety of reasons, including water pollution coming agriculture, effluent treatment plants, sewage system leaks, pH and light levels, the consequences climate change. In recent years, HAB events have become serious environmental problem, paralleling population growth, agricultural development, increasing air temperatures, declining precipitation. Hence, it is crucial to identify mechanisms responsible for formation harmful algal blooms (HABs), accurately assess their short- long-term impacts, quantify variations based on projections developing accurate action plans effectively managing resources. This present study utilizes empirical dynamic modeling (EDM) predict chlorophyll-a (chl-a) concentration Lake Erie. method characterized by its nonlinearity nonparametric nature. EDM has significant benefit in that surpasses constraints conventional statistical through use data-driven attractor reconstruction. Chl-a critical commonly used parameter prediction events. Erie an inland body experiences frequent phenomena as result location. With MAPE 4.31%, RMSE 6.24, coefficient determination 0.98, showed exceptional performance. These findings suggest underlying dynamics chl-a changes be well captured model.

Language: Английский

A Conversational Intelligent Assistant for Enhanced Operational Support in Floodplain Management with Multimodal Data DOI

Vinay Pursnani,

Yusuf Sermet, İbrahim Demir

et al.

International Journal of Disaster Risk Reduction, Journal Year: 2025, Volume and Issue: unknown, P. 105422 - 105422

Published: March 1, 2025

Language: Английский

Citations

0
A community-centric intelligent cyberinfrastructure for addressing nitrogen pollution using web systems and conversational AI DOI

Samrat Shrestha,

Jerry Mount,

Gabriel Vald

et al.

Environmental Science & Policy, Journal Year: 2025, Volume and Issue: 167, P. 104055 - 104055

Published: April 4, 2025

Language: Английский

Citations

0
Harmful algal bloom prediction using empirical dynamic modeling DOI
Özlem Baydaroğlu

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 959, P. 178185 - 178185

Published: Dec. 22, 2024

Language: Английский

Citations

1
Harrmful Algal Bloom Prediction using Emprical Dynamic Modelling DOI Creative Commons
Özlem Baydaroğlu

EarthArXiv (California Digital Library), Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 2, 2024

Harmful Algal Blooms (HABs) can originate from a variety of reasons, including water pollution coming agriculture, effluent treatment plants, sewage system leaks, pH and light levels, the consequences climate change. In recent years, HAB events have become serious environmental problem, paralleling population growth, agricultural development, increasing air temperatures, declining precipitation. Hence, it is crucial to identify mechanisms responsible for formation harmful algal blooms (HABs), accurately assess their short- long-term impacts, quantify variations based on projections developing accurate action plans effectively managing resources. This present study utilizes empirical dynamic modeling (EDM) predict chlorophyll-a (chl-a) concentration Lake Erie. method characterized by its nonlinearity nonparametric nature. EDM has significant benefit in that surpasses constraints conventional statistical through use data-driven attractor reconstruction. Chl-a critical commonly used parameter prediction events. Erie an inland body experiences frequent phenomena as result location. With MAPE 4.31%, RMSE 6.24, coefficient determination 0.98, showed exceptional performance. These findings suggest underlying dynamics chl-a changes be well captured model.

Language: Английский

Citations

0