Applying Deep Learning in the Prediction of Chlorophyll-a in the East China Sea DOI Creative Commons

Haobin Cen,

Jiahan Jiang, Guoqing Han

et al.

Remote Sensing, Journal Year: 2022, Volume and Issue: 14(21), P. 5461 - 5461

Published: Oct. 30, 2022

The ocean chlorophyll-a (Chl-a) concentration is an important variable in the marine environment, abnormal distribution of which closely related to hazards red tides. Thus, accurate prediction its East China Sea (ECS) greatly for preventing water eutrophication and protecting coastal ecological environment. Processed by two different pre-processing methods, 10-year (2011–2020) satellite-observed data logarithmic were used as long short-term memory (LSTM) neural network training datasets this study. 2021 comparison results. past 15 days’ predict five following days. Results showed that predictions obtained both methods could simulate seasonal Chl-a ECS effectively. Moreover, performance model driven original values was better medium- low-concentration regions. However, high-concentration region, extreme concentrations data-driven LSTM models underestimation, considering better. sensitivity experiments accuracy decreased considerably when backward time step increased. In study, only chlorophyll-a, whose forecasted, effect other relevant elements on not considered, current weakness

Language: Английский

Ensemble Machine Learning of Gradient Boosting (XGBoost, LightGBM, CatBoost) and Attention-Based CNN-LSTM for Harmful Algal Blooms Forecasting DOI Creative Commons
Jung Min Ahn, Jungwook Kim, Kyunghyun Kim

et al.

Toxins, Journal Year: 2023, Volume and Issue: 15(10), P. 608 - 608

Published: Oct. 10, 2023

Harmful algal blooms (HABs) are a serious threat to ecosystems and human health. The accurate prediction of HABs is crucial for their proactive preparation management. While mechanism-based numerical modeling, such as the Environmental Fluid Dynamics Code (EFDC), has been widely used in past, recent development machine learning technology with data-based processing capabilities opened up new possibilities prediction. In this study, we developed evaluated two types learning-based models prediction: Gradient Boosting (XGBoost, LightGBM, CatBoost) attention-based CNN-LSTM models. We Bayesian optimization techniques hyperparameter tuning, applied bagging stacking ensemble obtain final results. result was derived by applying optimal techniques, applicability evaluated. When predicting an technique, it judged that overall performance can be improved complementing advantages each model averaging errors overfitting individual Our study highlights potential emphasizes need incorporate latest into important field.

Language: Английский

Citations

43
Water quality forecasting based on data decomposition, fuzzy clustering and deep learning neural network DOI

Jin‐Won Yu,

Ju-Song Kim,

Xia Li

et al.

Environmental Pollution, Journal Year: 2022, Volume and Issue: 303, P. 119136 - 119136

Published: March 10, 2022

Language: Английский

Citations

65
Algal bloom forecasting with time-frequency analysis: A hybrid deep learning approach DOI

Muyuan Liu,

Junyu He, Yuzhou Huang

et al.

Water Research, Journal Year: 2022, Volume and Issue: 219, P. 118591 - 118591

Published: May 14, 2022

Language: Английский

Citations

55
Long-term prediction of sea surface chlorophyll-a concentration based on the combination of spatio-temporal features DOI
Liu Na, Shaoyang Chen,

Cheng Zhenyan

et al.

Water Research, Journal Year: 2022, Volume and Issue: 211, P. 118040 - 118040

Published: Jan. 4, 2022

Language: Английский

Citations

47
Simultaneous feature engineering and interpretation: Forecasting harmful algal blooms using a deep learning approach DOI
TaeHo Kim, Jihoon Shin, Doyeon Lee

et al.

Water Research, Journal Year: 2022, Volume and Issue: 215, P. 118289 - 118289

Published: March 12, 2022

Language: Английский

Citations

42
Harmful Cyanobacterial Blooms: Biological Traits, Mechanisms, Risks, and Control Strategies DOI Open Access
Lirong Song, Yunlu Jia, Boqiang Qin

et al.

Annual Review of Environment and Resources, Journal Year: 2023, Volume and Issue: 48(1), P. 123 - 147

Published: Sept. 8, 2023

Harmful cyanobacterial blooms (CyanoHABs) impact lakes, estuaries, and freshwater reservoirs worldwide. The duration, severity, spread of CyanoHABs have markedly increased over the past decades will likely continue to increase. This article addresses universal phenomena occurring in many ecosystems Based on analysis ecophysiological traits bloom-forming cyanobacteria their interactions with environmental processes, we summarize decipher driving forces leading initiation, outbreak, persistence blooms. Due coupling effects eutrophication, rising CO 2 levels global warming, a multidisciplinary joint research approach is critical for better understanding CyanoHAB phenomenon its prediction, remediation, prevention. There an urgent need evaluate guide proper use bloom control techniques at large scales, using science-based environmentally friendly approaches.

Language: Английский

Citations

36
Long short-term memory models of water quality in inland water environments DOI Creative Commons
JongCheol Pyo, Yakov Pachepsky, Soobin Kim

et al.

Water Research X, Journal Year: 2023, Volume and Issue: 21, P. 100207 - 100207

Published: Nov. 16, 2023

Water quality is substantially influenced by a multitude of dynamic and interrelated variables, including climate conditions, landuse seasonal changes. Deep learning models have demonstrated predictive power water due to the superior ability automatically learn complex patterns relationships from variables. Long short-term memory (LSTM), one deep for prediction, type recurrent neural network that can account longer-term traits time-dependent data. It most widely applied used predict time series First, we reviewed applications standalone LSTM discussed its calculation time, prediction accuracy, good robustness with process-driven numerical other machine learning. This review was expanded into model data pre-processing techniques, Complete Ensemble Empirical Mode Decomposition Adaptive Noise method Synchrosqueezed Wavelet Transform. The then focused on coupling convolutional network, attention transfer coupled networks their performance over model. We also emphasized influence static variables in transformation dataset. Outlook further challenges were addressed. outlook research application hydrology concludes review.

Language: Английский

Citations

31
Current status and prospects of algal bloom early warning technologies: A Review DOI
X.L. Xiao, Yazhou Peng, Wei Zhang

et al.

Journal of Environmental Management, Journal Year: 2023, Volume and Issue: 349, P. 119510 - 119510

Published: Nov. 9, 2023

Language: Английский

Citations

30
Deep learning-based algorithms for long-term prediction of chlorophyll-a in catchment streams DOI
Ather Abbas, Minji Park, Sang‐Soo Baek

et al.

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 626, P. 130240 - 130240

Published: Sept. 26, 2023

Language: Английский

Citations

26
A coupled model to improve river water quality prediction towards addressing non-stationarity and data limitation DOI
Shengyue Chen, Jinliang Huang, Peng Wang

et al.

Water Research, Journal Year: 2023, Volume and Issue: 248, P. 120895 - 120895

Published: Nov. 20, 2023

Language: Английский

Citations

25