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

Water quality prediction and classification based on principal component regression and gradient boosting classifier approach DOI Creative Commons

Md. Saikat Islam Khan,

Nazrul Islam, Jia Uddin

et al.

Journal of King Saud University - Computer and Information Sciences, Journal Year: 2021, Volume and Issue: 34(8), P. 4773 - 4781

Published: June 14, 2021

Estimating water quality has been one of the significant challenges faced by world in recent decades. This paper presents a prediction model utilizing principal component regression technique. Firstly, index (WQI) is calculated using weighted arithmetic method. Secondly, analysis (PCA) applied to dataset, and most dominant WQI parameters have extracted. Thirdly, predict WQI, different algorithms are used PCA output. Finally, Gradient Boosting Classifier utilized classify status. The proposed system experimentally evaluated on Gulshan Lake-related dataset. results demonstrate 95% accuracy for method 100% classification method, which show credible performance compared with state-of-art models.

Language: Английский

Citations

139
Machine learning algorithms for efficient water quality prediction DOI
Mourade Azrour, Jamal Mabrouki, Ghizlane Fattah

et al.

Modeling Earth Systems and Environment, Journal Year: 2021, Volume and Issue: 8(2), P. 2793 - 2801

Published: Aug. 26, 2021

Language: Английский

Citations

117
Advances in machine learning and IoT for water quality monitoring: A comprehensive review DOI Creative Commons
Ismail Essamlali, Hasna Nhaila, Mohamed El Khaïli

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(6), P. e27920 - e27920

Published: March 1, 2024

Water holds great significance as a vital resource in our everyday lives, highlighting the important to continuously monitor its quality ensure usability. The advent of the. Internet Things (IoT) has brought about revolutionary shift by enabling real-time data collection from diverse sources, thereby facilitating efficient monitoring water (WQ). By employing Machine learning (ML) techniques, this gathered can be analyzed make accurate predictions regarding quality. These predictive insights play crucial role decision-making processes aimed at safeguarding quality, such identifying areas need immediate attention and implementing preventive measures avert contamination. This paper aims provide comprehensive review current state art monitoring, with specific focus on employment IoT wireless technologies ML techniques. study examines utilization range technologies, including Low-Power Wide Area Networks (LpWAN), Wi-Fi, Zigbee, Radio Frequency Identification (RFID), cellular networks, Bluetooth, context Furthermore, it explores application both supervised unsupervised algorithms for analyzing interpreting collected data. In addition discussing art, survey also addresses challenges open research questions involved integrating (WQM).

Language: Английский

Citations

33
Efficient Data-Driven Machine Learning Models for Water Quality Prediction DOI Creative Commons
Ηλίας Δρίτσας, Μαρία Τρίγκα

Computation, Journal Year: 2023, Volume and Issue: 11(2), P. 16 - 16

Published: Jan. 18, 2023

Water is a valuable, necessary and unfortunately rare commodity in both developing developed countries all over the world. It undoubtedly most important natural resource on planet constitutes an essential nutrient for human health. Geo-environmental pollution can be caused by many different types of waste, such as municipal solid, industrial, agricultural (e.g., pesticides fertilisers), medical, etc., making water unsuitable use any living being. Therefore, finding efficient methods to automate checking suitability great importance. In context this research work, we leveraged supervised learning approach order design accurate possible predictive models from labelled training dataset identification suitability, either consumption or other uses. We assume set physiochemical microbiological parameters input features that help represent water’s status determine its class (namely safe nonsafe). From methodological perspective, problem treated binary classification task, machine models’ performance (such Naive Bayes–NB, Logistic Regression–LR, k Nearest Neighbours–kNN, tree-based classifiers ensemble techniques) evaluated with without application balancing (i.e., nonuse Synthetic Minority Oversampling Technique–SMOTE), comparing them terms Accuracy, Recall, Precision Area Under Curve (AUC). our demonstration, results show Stacking model after SMOTE 10-fold cross-validation outperforms others Accuracy Recall 98.1%, 100% AUC equal 99.9%. conclusion, article, framework presented support researchers’ efforts toward quality prediction using (ML).

Language: Английский

Citations

37
Large-scale prediction of stream water quality using an interpretable deep learning approach DOI
Hang Zheng,

Yueyi Liu,

Wenhua Wan

et al.

Journal of Environmental Management, Journal Year: 2023, Volume and Issue: 331, P. 117309 - 117309

Published: Jan. 17, 2023

Language: Английский

Citations

27
Impacts of watershed and meteorological characteristics on stream water quality resilience DOI
Yujin Park,

Se-Rin Park,

Sang‐Woo Lee

et al.

Journal of Hydrology, Journal Year: 2025, Volume and Issue: 652, P. 132663 - 132663

Published: Jan. 7, 2025

Language: Английский

Citations

1
Investigating the critical influencing factors of rural public services resilience in China: A grey relational analysis approach DOI
Hui Yan, Haomiao Li, Lin Zhang

et al.

Environment Development and Sustainability, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 9, 2025

Language: Английский

Citations

1
IoT-Enabled Water Distribution Systems—A Comparative Technological Review DOI Creative Commons

Nibi Kulangara Velayudhan,

Preeja Pradeep, Sethuraman N. Rao

et al.

IEEE Access, Journal Year: 2022, Volume and Issue: 10, P. 101042 - 101070

Published: Jan. 1, 2022

Water distribution systems are one of the critical infrastructures and major assets water utility in a nation. The infrastructure consists resources, treatment plants, reservoirs, lines, consumers. A sustainable network management has to take care accessibility, quality, quantity, reliability water. As is becoming depleting resource for coming decades, regulation accounting terms above four parameters task. There have been many efforts towards establishment monitoring controlling framework, capable automating various stages processes. current trending technologies such as Information Communication Technologies (ICT), Internet Things (IoT), Artificial Intelligence (AI) potential track this spatially varying collect, process, analyze attributes events. In work, we investigate role scope IoT different systems. Our survey covers state-of-the-art control networks, status architectures networks. We explore existing systems, providing necessary background information on status. This work also presents an Architecture Intelligent Networks - IoTA4IWNet, real-time believe that build robust network, these components need be designed implemented effectively.

Language: Английский

Citations

38
An improved graph convolutional network with feature and temporal attention for multivariate water quality prediction DOI
Qingjian Ni,

Xuehan Cao,

Chaoqun Tan

et al.

Environmental Science and Pollution Research, Journal Year: 2022, Volume and Issue: 30(5), P. 11516 - 11529

Published: Sept. 12, 2022

Language: Английский

Citations

33
Reconstructing Daily Discharge in a Megadelta Using Machine Learning Techniques DOI Creative Commons
Hung Vo Thanh, Đoàn Văn Bình, Sameh A. Kantoush

et al.

Water Resources Research, Journal Year: 2022, Volume and Issue: 58(5)

Published: May 1, 2022

Abstract In this study, six machine learning (ML) models, namely, random forest (RF), Gaussian process regression (GPR), support vector (SVR), decision tree (DT), least squares (LSSVM), and multivariate adaptive spline (MARS) were employed to reconstruct the missing daily‐averaged discharge in a mega‐delta from 1980 2015 using upstream‐downstream multi‐station data. The performance accuracy of each ML model assessed compared with stage‐discharge rating curves (RCs) four statistical indicators, Taylor diagrams, violin plots, scatter time‐series heatmaps. Model input selection was performed mutual information correlation coefficient methods after three data pre‐processing steps: normalization, Fourier series fitting, first‐order differencing. results showed that models are superior their RC counterparts, MARS RF most reliable algorithms, although achieves marginally better than RF. Compared RC, reduced root mean square error (RMSE) by 135% 141% absolute 194% 179%, respectively, year‐round However, developed for climbing (wet season) recession (dry limbs separately worsened slightly Specifically, RMSE falling limb 856 1,040 m 3 /s, while obtained 768 789 respectively. DT is not recommended, GPR SVR provide acceptable results.

Language: Английский

Citations

31