Real-time eutrophication status evaluation of coastal waters using support vector machine with grid search algorithm

Kong, Xianyu; Sun, Yan; Su, Rongguo; Shi, Xiaoyong

doi:10.1016/j.marpolbul.2017.04.022

Cited by 36 publications

(19 citation statements)

References 142 publications

(171 reference statements)

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“…The empirical approaches have the advantage of providing a relationship between 73 independent and dependent variables and estimating dependent variables according to the 74 changes of a set of independent variables. Empirically based algorithms have been playing an 75 increasingly important role in HAB modeling, providing an important link between conceptual 76 and dynamical modeling approaches (McGillicuddy, 2010;Blauw et al, 2010;Anderson et al, 2010;Wang and Tang, 2010;Kong et al, 2017). As more and more observational data become 78 available, many effective methods can be used to build empirical models, such as multiple 79 variable regression and neural network.…”

Section: Introductionmentioning

confidence: 99%

A data-driven modeling approach for simulating algal blooms in the tidal freshwater of James River in response to riverine nutrient loading

Shen

Qin

Wang

et al. 2019

Ecological Modelling

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Algal blooms often occur in the tidal freshwater (TF) of the James River estuary, a tributary of the Chesapeake Bay. The timing of algal blooms correlates highly to a summer low-flow period when residence time is long and nutrients are available. Because of complex interactions 5 between physical transport and algal dynamics, it is challenging to predict interannual variations 6 of bloom correctly using a complex eutrophication model without having a high-resolution model grid to resolve complex geometry and an accurate estimate of nutrient loading to drive the model. In this study, an approach using long-term observational data (from 1990-2013) and the Support vector machine (LS-SVM) for simulating algal blooms was applied. The Empirical Orthogonal Function was used to reduce the data dimension that enables the algal bloom dynamics for the entire TF to be modeled by one model. The model results indicate that the datadriven model is capable of simulating interannual algal blooms with good predictive skills and is capable of forecasting algal blooms responding to the change of nutrient loadings and environmental conditions. This study provides a link between a conceptual model and a dynamic model, and demonstrates that the data-driven model is a good approach for simulating algal blooms in this complex environment of the James River. The method is very efficient and can be applied to other estuaries as well.

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Section: Introductionmentioning

confidence: 99%

A data-driven modeling approach for simulating algal blooms in the tidal freshwater of James River in response to riverine nutrient loading

Shen

Qin

Wang

et al. 2019

Ecological Modelling

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“…With the rapid economic development of coastal areas in China, eutrophication in marine waters became more severe in coastal regions with high population densities and industrial activities. The last two characteristics result to an increasing flow of anthropogenic nutrients into rivers and accumulation in estuaries and harbors (Chen et al ; Kong et al ; Zhang et al ). Assessment of the eutrophication status is indispensable for environmental conservation management and policy making purposes.…”

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confidence: 99%

“…characteristics result to an increasing flow of anthropogenic nutrients into rivers and accumulation in estuaries and harbors (Chen et al 2016;Kong et al 2017;Zhang et al 2017). Assessment of the eutrophication status is indispensable for environmental conservation management and policy making purposes.…”

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confidence: 99%

Assessing the severe eutrophication status and spatial trend in the coastal waters of Zhejiang province (China)

Jiang

et al. 2018

Limnology & Oceanography

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The eutrophication of the coastal waters of Zhejiang Province has become one of the main contamination threats to the region's coastal marine ecosystems. Accordingly, the comprehensive characterization of the eutrophication status in terms of improved quantitative methods is valuable for local risk assessment and policy making. A novelty of this work is that the spatial distributions of chemical oxygen demand, dissolved inorganic nitrogen, and dissolved inorganic phosphorus were estimated across space by the Bayesian maximum entropy (BME) method. The BME estimates were found to have the best cross‐validation performance compared to ordinary kriging and inverse distance weighted techniques. Based on the BME maps, it was found that about 25.95%, 19.18%, 20.53%, and 34.34% of these coastal waters were oligotrophic, mesotrophic, eutrophic, and hypereutrophic. Another novelty of the present work is that comprehensive stochastic site indicators (SSI) were introduced in the quantitative characterization of the eutrophication risk in the Zhejiang coastal waters under conditions of in situ uncertainty. The results showed that the level of the eutrophication index (EI) increased almost linearly with increasing threshold values; and that 71%, 51%, and 19% of coastal locations separated by various spatial lags experience considerable mesotrophic, eutrophic, and hypereutrophic risks, respectively. The average EI values over the subregions of the Zhejiang coastal waters graded as “oligotrophic or higher,” “eutrophic or higher,” and “hypereutrophic” were about 11.14, 14.28, and 25.34, respectively. Our results also revealed that the joint eutrophication strength between coastal locations in the Zhejiang region was consistently greater than the combined strength of independent eutrophications at these locations (we termed this situation “positive quadrant eutrophication dependency”). It was found that a critical eutrophication threshold ζcr ≈ 8.38 exists so that below ζcr the spatial eutrophication dependency in the Zhejiang coastal waters increases with ζ, whereas above ζcr the opposite is true. Moreover, the eutrophication dependency decreases as the separation distance δs increases. Interestingly, at distances δs smaller than a critical distance δscr ≈ 15 km, the eutrophication locations are concentrated in the coastal waters of the Zhejiang province rather than being dispersed (this observation holds even for large thresholds ζ). Elasticity analysis of eutrophication indicators offered a quantitative measure of the excess eutrophication change in the Zhejiang coastal waters caused by a threshold change (the larger the elasticity is, the more sensitive eutrophication is to threshold changes). The above findings can contribute to an improved understanding of seawater quality and provide a practical approach for the identification of critical coastal water regions.

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“…Besides, if the SVM model wants to achieve a relatively ideal regression effect, it must set and adjust relevant parameters; the most important parameters are the penalty parameter and the kernel function parameter . Previous studies applied grid search method (GSM) to seek the optimal parameters and of SVM [18][19][20][21]. Although GSM can obtain the global optimal solution, that is, the optimal regression accuracy, it is usually conducted within the specified range.…”

Section: Introductionmentioning

confidence: 99%

A Novel Agricultural Commodity Price Forecasting Model Based on Fuzzy Information Granulation and MEA-SVM Model

Zhang

2018

Mathematical Problems in Engineering

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Accurately predicting the price of agricultural commodity is very important for evading market risk, increasing agricultural income, and accomplishing government macroeconomic regulation. With the price index predictions of 6 commodities of Food and Agriculture Organization of the United Nations (FAO) as examples, this paper proposed a novel agricultural commodity price forecasting model which combined the fuzzy information granulation, mind evolutionary algorithm (MEA), and support vector machine (SVM). Firstly, the time series data of agricultural commodity price index was transformed into fuzzy information granulation particles made up ofLow,R, andUp, which represented the trend and magnitude of price movement. Secondly, MEA algorithm was employed to seek the optimal parameterscandgfor SVM to establish the MEA-SVM model. Finally, FOA price index fluctuation range and change trend in the future were predicted by the MEA-SVM model. The empirical analysis showed that the MEA-SVM model was effective and had higher prediction accuracy and faster calculation speed in the forecasting of agricultural commodity price.

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Real-time eutrophication status evaluation of coastal waters using support vector machine with grid search algorithm

Cited by 36 publications

References 142 publications

A data-driven modeling approach for simulating algal blooms in the tidal freshwater of James River in response to riverine nutrient loading

A data-driven modeling approach for simulating algal blooms in the tidal freshwater of James River in response to riverine nutrient loading

Assessing the severe eutrophication status and spatial trend in the coastal waters of Zhejiang province (China)

A Novel Agricultural Commodity Price Forecasting Model Based on Fuzzy Information Granulation and MEA-SVM Model

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