Unsteady Multi-Element Time Series Analysis and Prediction Based on Spatial-Temporal Attention and Error Forecast Fusion

Wang, Xiaofan; Xu, Lingyu

doi:10.3390/fi12020034

Cited by 10 publications

(5 citation statements)

References 15 publications

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“…For example, the LSTM model was employed for algal bloom prediction on a newly constructed WQMS on 16 rivers [29]. Besides, a study using other time series nonlinear models has been conducted [66] to improve the error caused by time series. LSTM has showcased excellent performance in other water sources, mainly river [24], [25], [26].…”

Section: Data-driven Algal Growth Prediction Methodsmentioning

confidence: 99%

A Complete Proposed Framework for Coastal Water Quality Monitoring System With Algae Predictive Model

et al. 2021

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An end-to-end process to achieve a complete framework methodology for Harmful Algal Bloom (HAB) growth prediction is crucial for water management, especially in implementing robust predictive modelling of HAB to prevent water pollution. Previous works have separately focused on the prediction part or the implementation of the water monitoring system that involves the integration of sensors through the Internet of Things (IoT). These studies lack in terms of discussion of both IoT with the algae ecological domain and prediction method. Therefore, this paper takes the initiative to provide a wider coverage on the end-to-end process including the assembly and integration of sensors, data acquisition and predictive modelling using data-driven approaches, for example, machine learning, deep learning and deep time series forecasting algorithm for future algal bloom outbreak mitigation. This paper believes that discussion in a complete framework perspective based on the execution of each phase is important besides providing a true understanding of the algae growth factors and prediction problems to achieve a robust prediction algorithm for algal growth. In the end, this paper presents proof that selecting the right features and utilising time series with deep learning are much better for tackling the issues of highly non-linear and dynamic algae ecological data that are briefly introduced in this paper. Among all the algorithms selected, Long Short-term Memory (LSTM) is the best fit for the prediction method and has outperformed other basic machine learning methods in accurately predicting algal growth through the prediction of chlorophyll-a (Chla) as a strong indicator of algal presence for coastal studies.

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Section: Data-driven Algal Growth Prediction Methodsmentioning

confidence: 99%

A Complete Proposed Framework for Coastal Water Quality Monitoring System With Algae Predictive Model

et al. 2021

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“…Among them, there are few long-term monitoring methods [66]. Most long-term monitoring only involves the analysis of remote sensing images at different times and rarely involves long-term sampling analysis [67,68]. Additionally, the remote sensing monitoring of cyanobacteria mainly involved the spatial characteristics of harmful cyanobacteria and remote sensing inversion of the phytoplankton pigment concentration.…”

Section: Comparison With Traditional Algorithmsmentioning

confidence: 99%

A Method of Cyanobacterial Concentrations Prediction Using Multispectral Images

Zhao

Chen

et al. 2022

Sustainability

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With the increasingly serious eutrophication of inland water, the frequency and scope of harmful cyanobacteria blooms are increasing, which affects the ecological balance and endangers human health. The aim of this study was to propose an alternative method for the quantification of cyanobacterial concentrations in water by correlating multispectral data. The research object was the cyanobacteria in Erhai Lake, Dali, China. Ten monitoring sites were selected, and multispectral images and cyanobacterial concentrations were measured in Erhai Lake from September to November 2021. In this study, multispectral data were used as independent variables, and cyanobacterial concentrations as dependent variables. We performed curve estimation, and significance analysis for the independent variables, and compared them with the original variable model. Here, we chose about four algorithms to establish models and compare their applicability, including Multivariable Linear Regression (MLR), Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and Extreme Learning Machine (ELM). The prediction performance was evaluated by the coefficient of determination (R2), Root-Mean-Square Error (RMSE), and Mean Relative Error (MRE). The results showed that the variable analysis model outperformed the original variable model, the ELM was superior to other algorithms, and the variable analysis model based on the ELM algorithm achieved the best results (R2 = 0.7609, RMSE = 4197 cells/mL, MRE = 0.044). This study confirmed the applicability of cyanobacterial concentrations prediction using multispectral data, which can be characterized as a quick and easy methodology, and the deep neural network has great potential to predict the concentration of cyanobacteria.

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“…Has a high complexity and computational cost. [46][47][48][49] The vector autoregressive (VAR) model, which extends the AR model to the multivariate setting, emerged as a solution to address this latter issue. A VAR model is an n-variable, n-equation linear model in which each variable is explained by its own lagged values, plus current and past values of the remaining n − 1 variables [23].…”

Section: Autoregressive Modelsmentioning

confidence: 99%

“…In a more complex approach, Wang and Xu (2020) [49] used a dual-stage attentionbased RNN (DA-RNN) [84] to predict chl-a concentration. The proposed model was applied to a two-year dataset obtained by Fujian Marine Forecasts Station, which contained the time series of chl-a concentration plus seven other water parameters measured with an interval of 30 min.…”

Section: Recurrent Neural Network (Rnns)mentioning

confidence: 99%

A Review of Recent Machine Learning Advances for Forecasting Harmful Algal Blooms and Shellfish Contamination

Cruz

Costa

Vinga

et al. 2021

JMSE

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Harmful algal blooms (HABs) are among the most severe ecological marine problems worldwide. Under favorable climate and oceanographic conditions, toxin-producing microalgae species may proliferate, reach increasingly high cell concentrations in seawater, accumulate in shellfish, and threaten the health of seafood consumers. There is an urgent need for the development of effective tools to help shellfish farmers to cope and anticipate HAB events and shellfish contamination, which frequently leads to significant negative economic impacts. Statistical and machine learning forecasting tools have been developed in an attempt to better inform the shellfish industry to limit damages, improve mitigation measures and reduce production losses. This study presents a synoptic review covering the trends in machine learning methods for predicting HABs and shellfish biotoxin contamination, with a particular focus on autoregressive models, support vector machines, random forest, probabilistic graphical models, and artificial neural networks (ANN). Most efforts have been attempted to forecast HABs based on models of increased complexity over the years, coupled with increased multi-source data availability, with ANN architectures in the forefront to model these events. The purpose of this review is to help defining machine learning-based strategies to support shellfish industry to manage their harvesting/production, and decision making by governmental agencies with environmental responsibilities.

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Unsteady Multi-Element Time Series Analysis and Prediction Based on Spatial-Temporal Attention and Error Forecast Fusion

Cited by 10 publications

References 15 publications

A Complete Proposed Framework for Coastal Water Quality Monitoring System With Algae Predictive Model

A Complete Proposed Framework for Coastal Water Quality Monitoring System With Algae Predictive Model

A Method of Cyanobacterial Concentrations Prediction Using Multispectral Images

A Review of Recent Machine Learning Advances for Forecasting Harmful Algal Blooms and Shellfish Contamination

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