A Review of Machine Learning Models for Harmful Algal Bloom Monitoring in Freshwater Systems DOI Open Access
Ibrahim Busari,

Debabrata Sahoo,

R. Daren Harmel

и другие.

Journal of Natural Resources and Agricultural Ecosystems, Год журнала: 2023, Номер 1(2), С. 63 - 76

Опубликована: Янв. 1, 2023

Highlights Machine Learning (ML) models are identified, reviewed, and analyzed for HAB predictions. Data preprocessing is vital efficient ML model development. toxin production monitoring limited. Abstract. Harmful algal blooms (HABs) detrimental to livestock, humans, pets, the environment, global economy, which calls a robust approach their management. While process-based can inform practitioners about enabling conditions, they have inherent limitations in accurately predicting harmful blooms. To address these limitations, potentially leverage large volumes of IoT data aid near real-time evolved as tools understanding patterns relationships between water quality parameters expansion. This review describes currently used forecasting HABs freshwater ecosystems presents structures application related toxins. The revealed that regression trees, random forest, Artificial Neural Network (ANN), Support Vector Regression (SVR), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) most frequently monitoring. shows models' prowess identifying significant variables influencing growth, drivers, multistep prediction. Hybrid also improve prediction algal-related through improved optimization techniques variable selection algorithms. often focus on biomass prediction, few studies apply limitation be associated with lack high-frequency datasets development, exploring this domain encouraged. serves guide policymakers researchers implement reveals potential decision support early Keywords: Cyanobacteria, Freshwater, blooms, learning, Water quality.

Язык: Английский

Algal bloom forecasting with time-frequency analysis: A hybrid deep learning approach DOI

Muyuan Liu,

Junyu He, Yuzhou Huang

и другие.

Water Research, Год журнала: 2022, Номер 219, С. 118591 - 118591

Опубликована: Май 14, 2022

Язык: Английский

Процитировано

57

COVID-19 Diagnosis: A Review of Rapid Antigen, RT-PCR and Artificial Intelligence Methods DOI Creative Commons
Raphael Taiwo Aruleba, Tayo Alex Adekiya, Nimibofa Ayawei

и другие.

Bioengineering, Год журнала: 2022, Номер 9(4), С. 153 - 153

Опубликована: Апрель 3, 2022

As of 27 December 2021, SARS-CoV-2 has infected over 278 million persons and caused 5.3 deaths. Since the outbreak COVID-19, different methods, from medical to artificial intelligence, have been used for its detection, diagnosis, surveillance. Meanwhile, fast efficient point-of-care (POC) testing self-testing kits become necessary in fight against COVID-19 assist healthcare personnel governments curb spread virus. This paper presents a review various types detection diagnostic technologies, surveillance approaches that or proposed. The provided this article should be beneficial researchers field health policymakers at large.

Язык: Английский

Процитировано

43

Bayesian model averaging by combining deep learning models to improve lake water level prediction DOI
Gang Li, Zhangjun Liu, Jingwen Zhang

и другие.

The Science of The Total Environment, Год журнала: 2023, Номер 906, С. 167718 - 167718

Опубликована: Окт. 12, 2023

Язык: Английский

Процитировано

30

A state-of-the-art review of long short-term memory models with applications in hydrology and water resources DOI
Zhong-kai Feng, J. Zhang, Wen-jing Niu

и другие.

Applied Soft Computing, Год журнала: 2024, Номер unknown, С. 112352 - 112352

Опубликована: Окт. 1, 2024

Язык: Английский

Процитировано

12

Modeling freshwater plankton community dynamics with static and dynamic interactions using graph convolution embedded long short-term memory DOI
Hyo Gyeom Kim,

Eun-Young Jung,

Heewon Jeong

и другие.

Water Research, Год журнала: 2024, Номер 266, С. 122401 - 122401

Опубликована: Сен. 6, 2024

Язык: Английский

Процитировано

10

A combined hydrodynamic model and deep learning method to predict water level in ungauged rivers DOI
Gang Li, Haoyu Zhu,

Hongfu Jian

и другие.

Journal of Hydrology, Год журнала: 2023, Номер 625, С. 130025 - 130025

Опубликована: Авг. 10, 2023

Язык: Английский

Процитировано

21

Effects of riparian pioneer plants on soil aggregate stability: Roles of root traits and rhizosphere microorganisms DOI
Xiaoxiao Wang, Ping Huang, Maohua Ma

и другие.

The Science of The Total Environment, Год журнала: 2024, Номер 940, С. 173584 - 173584

Опубликована: Май 31, 2024

Язык: Английский

Процитировано

9

Employing hybrid deep learning for near-real-time forecasts of sensor-based algal parameters in a Microcystis bloom-dominated lake DOI
Lan Wang, Kun Shan, Yi Yang

и другие.

The Science of The Total Environment, Год журнала: 2024, Номер 922, С. 171009 - 171009

Опубликована: Фев. 24, 2024

Язык: Английский

Процитировано

8

A survey on applications of machine learning algorithms in water quality assessment and water supply and management DOI Creative Commons
Abdulhalık Oğuz, Ömer Faruk Ertuğrul

Water Science & Technology Water Supply, Год журнала: 2023, Номер 23(2), С. 895 - 922

Опубликована: Фев. 1, 2023

Abstract Managing water resources and determining the quality of surface groundwater is one most significant issues fundamental to human societal well-being. The process maintaining managing well involves complications due human-induced errors. Therefore, applications that facilitate enhance these processes have gained importance. In recent years, machine learning techniques been applied successfully in preservation management planning resources. Water researchers effectively used integrate them into public systems. this study, data sources, pre-processing, methods research are briefly mentioned, algorithms categorized. Then, a general summary literature presented on determination management. Lastly, study was detailed using investigations two publicly shared datasets.

Язык: Английский

Процитировано

15

Research progress in water quality prediction based on deep learning technology: a review DOI
Wenhao Li,

Yin Zhao,

Yining Zhu

и другие.

Environmental Science and Pollution Research, Год журнала: 2024, Номер 31(18), С. 26415 - 26431

Опубликована: Март 27, 2024

Язык: Английский

Процитировано

6