Testing protocols for smoothing datasets of hydraulic variables acquired during unsteady flows DOI
Özlem Baydaroğlu, Marian Muste, Atiye Cikmaz

et al.

Hydrological Sciences Journal, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 18

Published: Aug. 19, 2024

Flood wave propagation involves complex flow variable dependencies. Continuous in-situ hydrograph peak magnitude and timing data provide the most relevant information for understanding these New acoustic instruments can produce experimental evidence by extracting usable signals from noisy datasets. This study presents a new screening protocol to smoothen streamflow unwanted influences noise generated perturbations observational fluctuations. The combines quantitative (statistical fitness parameters) qualitative (domain expert judgments) evaluations. It is tested with 18 smoothing methods identify optimal conditioning candidates. Sensitivity analyses assess validity, generality, scalability of procedures. goal this analysis set mathematical foundation empirical results that lead unified, general conclusions on principles or protocols unsteady flows propagating in open channels, formulating practical guidance future acquisition processing, using better support data-driven modeling efforts.

Language: Английский

Multivariate Regression Analysis for Identifying Key Drivers of Harmful Algal Bloom in Lake Erie DOI Creative Commons
Omer Mermer, İbrahim Demir

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(9), P. 4824 - 4824

Published: April 26, 2025

Harmful Algal Blooms (HABs), predominantly driven by cyanobacteria, pose significant risks to water quality, public health, and aquatic ecosystems. Lake Erie, particularly its western basin, has been severely impacted HABs, largely due nutrient pollution climatic changes. This study aims identify key physical, chemical, biological drivers influencing HABs using a multivariate regression analysis. Water quality data, collected from multiple monitoring stations in Erie 2013 2020, were analyzed develop predictive models for chlorophyll-a (Chl-a) total suspended solids (TSS). The correlation analysis revealed that particulate organic nitrogen, turbidity, carbon the most influential variables predicting Chl-a TSS concentrations. Two developed, achieving high accuracy with R2 values of 0.973 0.958 TSS. demonstrates robustness techniques identifying HAB drivers, providing framework applicable other systems. These findings will contribute better prediction management strategies, ultimately helping protect resources health.

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
Testing protocols for smoothing datasets of hydraulic variables acquired during unsteady flows DOI
Özlem Baydaroğlu, Marian Muste, Atiye Cikmaz

et al.

Hydrological Sciences Journal, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 18

Published: Aug. 19, 2024

Flood wave propagation involves complex flow variable dependencies. Continuous in-situ hydrograph peak magnitude and timing data provide the most relevant information for understanding these New acoustic instruments can produce experimental evidence by extracting usable signals from noisy datasets. This study presents a new screening protocol to smoothen streamflow unwanted influences noise generated perturbations observational fluctuations. The combines quantitative (statistical fitness parameters) qualitative (domain expert judgments) evaluations. It is tested with 18 smoothing methods identify optimal conditioning candidates. Sensitivity analyses assess validity, generality, scalability of procedures. goal this analysis set mathematical foundation empirical results that lead unified, general conclusions on principles or protocols unsteady flows propagating in open channels, formulating practical guidance future acquisition processing, using better support data-driven modeling efforts.

Language: Английский

Citations

0