Artificial Intelligence in Aquatic Biodiversity Research: A PRISMA-Based Systematic Review DOI Creative Commons
Tymoteusz Miller, Grzegorz Michoński, Irmina Durlik

et al.

Biology, Journal Year: 2025, Volume and Issue: 14(5), P. 520 - 520

Published: May 8, 2025

Freshwater ecosystems are increasingly threatened by climate change and anthropogenic activities, necessitating innovative scalable monitoring solutions. Artificial intelligence (AI) has emerged as a transformative tool in aquatic biodiversity research, enabling automated species identification, predictive habitat modeling, conservation planning. This systematic review follows the PRISMA framework to analyze AI applications freshwater studies. Using structured literature search across Scopus, Web of Science, Google Scholar, we identified 312 relevant studies published between 2010 2024. categorizes into assessment, ecological risk evaluation, strategies. A bias assessment was conducted using QUADAS-2 RoB 2 frameworks, highlighting methodological challenges, such measurement inconsistencies model validation. The citation trends demonstrate exponential growth AI-driven with leading contributions from China, United States, India. Despite growing use this field, also reveals several persistent including limited data availability, regional imbalances, concerns related generalizability transparency. Our findings underscore AI’s potential revolutionizing but emphasize need for standardized methodologies, improved integration, interdisciplinary collaboration enhance insights efforts.

Language: Английский

A deep learning-enabled IoT framework for early hypoxia detection in aqua water using light weight spatially shared attention-LSTM network DOI
Arepalli Peda Gopi, K. Jairam Naik

The Journal of Supercomputing, Journal Year: 2023, Volume and Issue: 80(2), P. 2718 - 2747

Published: Aug. 19, 2023

Language: Английский

Citations

21
Research on a multiparameter water quality prediction method based on a hybrid model DOI
Zhiqiang Zheng, Hao Ding, Zhi Weng

et al.

Ecological Informatics, Journal Year: 2023, Volume and Issue: 76, P. 102125 - 102125

Published: May 16, 2023

Language: Английский

Citations

16
Application of classification machine learning algorithms for characterizing nutrient transport in a clay plain agricultural watershed DOI

Ahmed Elsayed,

Sarah Rixon, Jana Levison

et al.

Journal of Environmental Management, Journal Year: 2023, Volume and Issue: 345, P. 118924 - 118924

Published: Sept. 6, 2023

Language: Английский

Citations

13
Machine learning models for prediction of nutrient concentrations in surface water in an agricultural watershed DOI Creative Commons

Ahmed Elsayed,

Sarah Rixon, Jana Levison

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 372, P. 123305 - 123305

Published: Nov. 19, 2024

Language: Английский

Citations

5
Evaluation of water quality and its driving forces in the Shaying River Basin with the grey relational analysis based on combination weighting DOI
Jie Tao, Xinhao Sun, Yang Cao

et al.

Environmental Science and Pollution Research, Journal Year: 2021, Volume and Issue: 29(12), P. 18103 - 18115

Published: Oct. 22, 2021

Language: Английский

Citations

31
What will the water quality of the Yangtze River be in the future? DOI
Wenxun Dong, Yanjun Zhang, Liping Zhang

et al.

The Science of The Total Environment, Journal Year: 2022, Volume and Issue: 857, P. 159714 - 159714

Published: Oct. 24, 2022

Language: Английский

Citations

22
Artificial intelligence based detection and control strategies for river water pollution: A comprehensive review DOI
Deepak L. Bhatt, Mamata Swain, Dhananjay Yadav

et al.

Journal of Contaminant Hydrology, Journal Year: 2025, Volume and Issue: 271, P. 104541 - 104541

Published: March 17, 2025

Language: Английский

Citations

0
Regression-based machine learning models for nitrate and chloride prediction in surface water in a small agricultural sand plain sub-watershed in southwestern Ontario, Canada DOI Creative Commons

Ahmed Elsayed,

Jana Levison, Andrew Binns

et al.

Frontiers in Environmental Science, Journal Year: 2025, Volume and Issue: 13

Published: March 28, 2025

Machine learning (ML) models have proven to be an efficient technique for better understanding and quantification of surface water quality, especially in agricultural watersheds where considerable anthropogenic activities occur. However, there is a lack systematic investigations that can examine the application different ML regression settings predict quality using group input variables, including hydrological (e.g., flow), meteorological precipitation), field crop cover) conditions. In this study, multiple models, support vector machine (SVM) trees (RT), were employed on 2-year dataset collected from sand plain sub-watershed southwestern Ontario, Canada (i.e., Lower Whitemans Creek) nitrate chloride concentrations at nine sampling sites within sub-watershed. The prediction capabilities these determined evaluation metrics coefficient determination (R 2 ) root-mean squared error (RMSE). general, Gaussian Process Regression (GPR) model was optimal algorithm 0.99 0.98 respectively training testing). According results feature importance analysis, it found conditions (specifically location site (main channel or tributary site) most crucial variables accurate predictions output variables. This study underscores implemented effectively quantify properties easily measurable parameters. These assist decision makers advancing successful actions steps towards protecting available resources.

Language: Английский

Citations

0
Prediction of Water Quality Using Machine Learning Models in IoT Environment DOI

Marwa Faydi,

Amira Zrelli, Tahar Ezzedine

et al.

Lecture notes on data engineering and communications technologies, Journal Year: 2025, Volume and Issue: unknown, P. 341 - 352

Published: Jan. 1, 2025

Language: Английский

Citations

0
Emulating process-based water quality modelling in water source reservoirs using machine learning DOI Creative Commons

Hadi Mohammed,

Hoese Michel Tornyeviadzi, Razak Seidu

et al.

Journal of Hydrology, Journal Year: 2022, Volume and Issue: 609, P. 127675 - 127675

Published: March 3, 2022

Process-based models are very efficient in simulating hydrodynamics and water quality surface bodies. However, their complex characteristics terms of implementation, data requirements, simulation time limit application regular drinking source management. This study demonstrates the potential a ML model (Long Short-Term Memory (LSTM)) as viable alternatives to process-based hydrodynamic Using meteorological hydrological measurements, was first calibrated predict series, profiles, contours variables namely Escherichia coli (E. coli), faecal coliforms, zinc, lead concentrations Brusdalsvatnet lake, which is for city Ålesund Norway. The results obtained were combined with input train suite LSTM emulate achieved modelling. indicate that can conveniently reproduce spatio-temporal evolution lake achievable model, particularly when specific locations within interest. Compared R2, NS MSE ranges 0.72–0.87, 0.68–0.85, 0.21–0.44 prediction temperature 0.78–0.95, 0.75–0.89, 0.011–0.028 respectively testing models. Similar performance levels Zinc, Lead at different depths lake. While setting up training simulations time-consuming, validated developed this offer an opportunity real-time sources integrated cloud transmission from field sensors.

Language: Английский

Citations

18