Machine learning modeling of fluorescence spectral data for prediction of trace organic contaminant removal during UV/H2O2 treatment of wastewater

Water Research, Год журнала: 2024, Номер 255, С. 121484 - 121484

Опубликована: Март 18, 2024

Язык: Английский

---

Emerging Applications of Fluorescence Excitation-Emission Matrix with Machine Learning for Water Quality Monitoring: A Systematic Review

Water Research, Год журнала: 2025, Номер 277, С. 123281 - 123281

Опубликована: Фев. 13, 2025

Язык: Английский

---

Gaining insight into the effect of laccase expression on humic substance formation during lignocellulosic biomass composting

The Science of The Total Environment, Год журнала: 2024, Номер 923, С. 171548 - 171548

Опубликована: Март 6, 2024

Язык: Английский

---

Mechanistic insights into electric field-enhanced methanation of lignite via microbial metabolic pathway optimization

Bioresource Technology, Год журнала: 2025, Номер unknown, С. 132223 - 132223

Опубликована: Фев. 1, 2025

Язык: Английский

---

Effect of multi-stage inoculation on the efficiency of hyperthermophilic composting of cationic polyacrylamide-containing sludge

Environmental Technology & Innovation, Год журнала: 2025, Номер 38, С. 104085 - 104085

Опубликована: Фев. 17, 2025

Язык: Английский

---

Practical application of machine learning for organic matter and harmful algal blooms in freshwater systems: A review

Critical Reviews in Environmental Science and Technology, Год журнала: 2023, Номер 54(12), С. 953 - 975

Опубликована: Ноя. 29, 2023

The application of machine learning (ML) techniques for understanding and predicting organic matter (OM) harmful algal blooms (HABs) in freshwater systems has increased significantly with the availability abundant data advanced monitoring technologies. However, there is a lack comprehensive reviews concentrating on practical applications delving into potential risks associated misrepresentation or inflation constructing ML models. This review aims to bridge these gaps by providing overview various aspects context OM HABs systems. It covers rapid assessment, early warning, driver analysis, highlighting diverse range employed areas. Furthermore, it discusses challenges considerations handling, including using situ remote sensing importance appropriate data-splitting avoid leakage. To ensure unbiased reproducible results, this offers recommendations model improvement, such as utilizing explainable gain insights behavior avoiding overreliance single algorithm. also emphasizes significance deploying models through user-friendly interfaces, enabling non-experts effectively utilize real-world water environments.

Язык: Английский

---

Modeling river water dissolved organic matter using ensemble computing and genetic programming techniques

Ecohydrology & Hydrobiology, Год журнала: 2024, Номер unknown

Опубликована: Май 1, 2024

Язык: Английский

---

Evaluating excitation-emission matrix for characterization of dissolved organic matter in natural and engineered water systems: Unlocking submerged secrets

TrAC Trends in Analytical Chemistry, Год журнала: 2024, Номер 181, С. 118045 - 118045

Опубликована: Ноя. 8, 2024

Язык: Английский

---

Gaining Insight into the Effect of Laccase Gene Expression on Humic Substance Formation During Lignocellulosic Biomass Composting

Опубликована: Янв. 1, 2024

Download This Paper Open PDF in Browser Add to My Library Share: Permalink Using these links will ensure access this page indefinitely Copy URL DOI

Язык: Английский

---

Exploring optical descriptors for rapid estimation of coastal sediment organic carbon and nearby land-use classifications via machine learning models

Marine Pollution Bulletin, Год журнала: 2024, Номер 202, С. 116307 - 116307

Опубликована: Апрель 1, 2024

Язык: Английский

---