Cited by A novel interpretable hybrid model for multi-step ahead dissolved oxygen forecasting in the Mississippi River basin

A deep learning interpretable model for river dissolved oxygen multi-step and interval prediction based on multi-source data fusion DOI

Zhaocai Wang, Qingyu Wang, Zhixiang Liu

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 629, P. 130637 - 130637

Published: Jan. 14, 2024

Language: Английский

Citations

Interpretable prediction, classification and regulation of water quality: A case study of Poyang Lake, China DOI

Zhiyuan Yao, Zhaocai Wang,

Jinghan Huang

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 951, P. 175407 - 175407

Published: Aug. 9, 2024

Language: Английский

Citations

Photocatalytic degradation of drugs and dyes using a maching learning approach DOI

Ganesan Anandhi,

M. Iyapparaja

RSC Advances, Journal Year: 2024, Volume and Issue: 14(13), P. 9003 - 9019

Published: Jan. 1, 2024

The waste management industry uses an increasing number of mathematical prediction models to accurately forecast the behavior organic pollutants during catalytic degradation.

Language: Английский

Citations

Prediction of riverine daily minimum dissolved oxygen concentrations using hybrid deep learning and routine hydrometeorological data DOI

Yue Hu, Chuankun Liu, W. M. Wollheim

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 918, P. 170383 - 170383

Published: Jan. 26, 2024

Language: Английский

Citations

Dissolved oxygen prediction using regularized extreme learning machine with clustering mechanism in a black bass aquaculture pond DOI

Pei Shi, Liang Kuang,

Limin Yuan

et al.

Aquacultural Engineering, Journal Year: 2024, Volume and Issue: 105, P. 102408 - 102408

Published: Feb. 5, 2024

Language: Английский

Citations

On the use of hydrodynamic modelling and random forest classifiers for the prediction of hypoxia in coastal lagoons DOI

Irene Simonetti,

Claudio Lubello,

Lorenzo Cappietti

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 951, P. 175424 - 175424

Published: Aug. 12, 2024

Hypoxia is one of the fundamental threats to water quality globally, particularly for partially enclosed basins with limited renewal, such as coastal lagoons. This work proposes combined use a machine learning technique, field observations, and data derived from hydrodynamic heat exchange numerical model predict, forecast up 10 days in advance, occurrence hypoxia eutrophic lagoon. The random forest algorithm used, training validating set models classify dissolved oxygen levels Orbetello lagoon, central Mediterranean Sea (Italy), has provided test case assessing reliability proposed methodology. Results proved that methodology effective providing reliable short-term evaluation DO levels, high resolution both time space throughout an entire An overall classification accuracy 91 % was found models, score identifying severe - i.e. hourly lower than 2 mg/l 86 %. predictors extracted allows us overcome intrinsic limitation modelling approaches which rely on input relatively few, local measurements, inability capture spatial heterogeneity distributions, unless several measuring points are available. methodological approach application similar environments.

Language: Английский

Citations

Multi-step ahead dissolved oxygen concentration prediction based on knowledge guided ensemble learning and explainable artificial intelligence DOI

Tunhua Wu, Zhaocai Wang, Jinghan Dong

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 636, P. 131297 - 131297

Published: May 9, 2024

Language: Английский

Citations

Dissolved Oxygen Modeling by a Bayesian-Optimized Explainable Artificial Intelligence Approach DOI

Qiulin Li,

Jinchao He,

Dewei Mu

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(3), P. 1471 - 1471

Published: Jan. 31, 2025

Dissolved oxygen (DO) is a vital water quality index influencing biological processes in aquatic environments. Accurate modeling of DO levels crucial for maintaining ecosystem health and managing freshwater resources. To this end, the present study contributes Bayesian-optimized explainable machine learning (ML) model to reveal dynamics predict concentrations. Three ML models, support vector regression (SVR), tree (RT), boosting ensemble, coupled with Bayesian optimization (BO), are employed estimate Mississippi River. It concluded that BO-SVR outperforms others, achieving coefficient determination (CD) 0.97 minimal error metrics (root mean square = 0.395 mg/L, absolute 0.303 mg/L). Shapley Additive Explanation (SHAP) analysis identifies temperature, discharge, gage height as most dominant factors affecting levels. Sensitivity confirms robustness models under varying input conditions. With perturbations from 5% 30%, temperature sensitivity ranges 1.0% 6.1%, discharge 0.9% 5.2%, 0.8% 5.0%. Although experience reduced accuracy extended prediction horizons, they still achieve satisfactory results (CD > 0.75) forecasting periods up 30 days. The established also exhibit higher than many prior approaches. This highlights potential BO-optimized reliable forecasting, offering valuable insights resource management.

Language: Английский

Citations

Quantifying the Relative Contributions of Forcings to the Variability of Estuarine Surface Suspended Sediments Using a Machine Learning Framework DOI

Juliana Távora, Roy El Hourany, Elisa Helena Leão Fernandes

et al.

Continental Shelf Research, Journal Year: 2025, Volume and Issue: unknown, P. 105429 - 105429

Published: Feb. 1, 2025

Language: Английский

Citations

An improved graph neural network integrating indicator attention and spatio-temporal correlation for dissolved oxygen prediction DOI

Fei Ding, Shilong Hao,

Mingcen Jiang

et al.

Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 103126 - 103126

Published: April 1, 2025

Language: Английский

Citations