Using machine learning models to estimate Escherichia coli concentration in an irrigation pond from water quality and drone-based RGB imagery data

Water Research, Journal Year: 2024, Volume and Issue: 260, P. 121861 - 121861

Published: May 31, 2024

The rapid and efficient quantification of Escherichia coli concentrations is crucial for monitoring water quality. Remote sensing techniques machine learning algorithms have been used to detect E. in estimate its concentrations. application these approaches, however, challenged by limited sample availability unbalanced quality datasets. In this study, we estimated the concentration an irrigation pond Maryland, USA, during summer season using demosaiced natural color (red, green, blue: RGB) imagery visible infrared spectral ranges, a set 14 parameters. We did deploying four models - Random Forest (RF), Gradient Boosting Machine (GBM), Extreme (XGB), K-nearest Neighbor (KNN) under three data utilization scenarios: parameters only, combined small unmanned aircraft system (sUAS)-based RGB data, only. To select training test datasets, applied two data-splitting methods: ordinary quantile splitting. These methods provided constant splitting ratio each decile distribution. Quantile resulted better model performance metrics smaller differences between both testing When trained with after hyperparameter optimization, RF, GBM, XGB had R

Language: Английский

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Prediction of g–C3N4–based photocatalysts in tetracycline degradation based on machine learning

Chemosphere, Journal Year: 2024, Volume and Issue: 362, P. 142632 - 142632

Published: June 17, 2024

Language: Английский

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Integrating Remote Sensing Methods for Monitoring Lake Water Quality: A Comprehensive Review

Hydrology, Journal Year: 2024, Volume and Issue: 11(7), P. 92 - 92

Published: June 26, 2024

Remote sensing methods have the potential to improve lake water quality monitoring and decision-making in management. This review discusses use of remote for assessing lakes. It explains principles different used retrieving parameters complex waterbodies. The highlights importance considering variability optically active need comprehensive studies that encompass seasons time frames. paper addresses specific physical biological can be effectively estimated using sensing, such as chlorophyll-α, turbidity, transparency (Secchi disk depth), electrical conductivity, surface salinity, temperature. further provides a summary bands, band combinations, equations commonly these per satellite sensor. also limitations challenges associated with systems. recommends integrating situ measurements computer modelling understanding quality. suggests future research directions, including optimizing grid selection frame by combining hydrodynamic models retrieval methods, when analysing imagery, development advanced technologies, integration machine learning algorithms effective problem-solving. concludes proposed workflow lakes methods.

Language: Английский

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Characterization and Modeling of Harmful Algal Blooms: A Review

Journal of Hydraulic Engineering, Journal Year: 2025, Volume and Issue: 151(2)

Published: Jan. 2, 2025

Language: Английский

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Protocols for Water and Environmental Modeling Using Machine Learning in California

Hydrology, Journal Year: 2025, Volume and Issue: 12(3), P. 59 - 59

Published: March 14, 2025

The recent surge in popularity of generative artificial intelligence (GenAI) tools like ChatGPT has reignited global interest AI, a technology with well-established history spanning several decades. California Department Water Resources (DWR) been at the forefront this field, leveraging Artificial Neural Networks (ANNs), core technique machine learning (ML), which is subfield for water and environmental modeling (WEM) since early 1990s. While protocols WEM exist California, they were designed primarily traditional statistical or process-based models that rely on predefined equations physical principles. In contrast, ML learn patterns from data require different development methodologies, existing do not address. This study, drawing DWR’s extensive experience ML, addresses gap by developing standardized implementation California. proposed cover four key phases implementation: (1) problem definition, ensuring clear objectives contextual understanding; (2) preparation, emphasizing collection, quality control, accessibility; (3) model development, advocating progression simple to hybrid ensemble approaches while integrating domain knowledge improved accuracy; (4) deployment, highlighting documentation, training, open-source practices enhance transparency collaboration. A case study provided demonstrate practical application these step step. Once implemented, can help achieve standardization, assurance, interoperability, using

Language: Английский

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Multimodal deep learning models incorporating the adsorption characteristics of the adsorbent for estimating the permeate flux in dynamic membranes

Journal of Membrane Science, Journal Year: 2024, Volume and Issue: 709, P. 123105 - 123105

Published: July 18, 2024

Language: Английский

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

Marine Pollution Bulletin, Journal Year: 2024, Volume and Issue: 202, P. 116307 - 116307

Published: April 1, 2024

Language: Английский

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