Comparing artificial intelligence techniques for chlorophyll-a prediction in US lakes

Luo, Wenjing; Zhu, Senlin; Wu, Shanshan; Dai, Jiangyu

doi:10.1007/s11356-019-06360-y

Cited by 30 publications

(15 citation statements)

References 37 publications

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“…Comparing with the classical SARIMA model, the univariate and linear background did not achieve results needed for it to outperform the ML models. Compared with the ML literature, studies like [3] and [11] achieved results of R 2 ranging from 0.50 to 0.80, analyzing shorter time series of chl-a in lakes. [45] predicted variations of chlorophyll-a in different sites of the North Sea using Generalized Additive Models (GAM) and the R 2 results ranged from 0.15 to 0.63.…”

Section: Discussionmentioning

confidence: 99%

“…Pelagic Chlorophyll-a concentrations (chl-a) are a common indicator of primary production and key to evaluation of the health and productivity of marine and freshwater systems [1], [2]. It is therefore of crucial importance to accurately measure/ predict chlorophyll from proxy parameters in such systems [3]. Accelerated global warming is exacerbating climate change and unsettling ecosystems processes, while the impacts directly affect the marine primary production triggering an upwards transfer of effects which reach humans.…”

Section: Introductionmentioning

confidence: 99%

“…State-of-the-art ML algorithms for time series regression include Random Forest Regressor (RF), Support Vector Machine Regressor (SVR) and Neural Networks Multi-layer Perceptron Regressor (MLP). All of these have been used to some degree in the literature for the prediction of chlorophyll-a concentrations in aquatic systems, and achieve significantly accurate results in both error and goodness of fit metrics [3], [11], [14]. These are studies based in chl-a time series either with short length and daily frequency or long term low frequency sampling time series, using different ML methods to best predict chl-a behavior.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Machine Learning predictions of a highly resolved time series of Chlorophyll-a concentration

Amorim

Rick

Lohmann³

2021

Preprint

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Pelagic Chlorophyll-a concentrations are key for evaluation of the environmental status and productivity of marine systems. In this study, chlorophyll-a concentrations for the Helgoland Roads Time Series were modeled using a number of measured water and environmental parameters. We chose three common Machine Learning algorithms from the literature: Support Vector Machine Regressor, Neural Networks Multi-layer Perceptron Regressor and Random Forest Regressor. Results showed that Support Vector Machine Regressor slightly outperformed other models. The evaluation with a test dataset and verification with an independent validation dataset for chlorophyll-a concentrations showed a good generalization capacity, evaluated by the root mean squared errors of less than 1 µg L-1. Feature selection and engineering are important and improved the models significantly, as measured in performance, improving by a minimum of 48% the adjusted R2. We tested SARIMA in comparison and found that the univariate nature of SARIMA does not allow for better results than the Machine Learning models. Additionally, the computer processing time needed was much higher (prohibitive) for SARIMA.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation of Machine Learning predictions of a highly resolved time series of Chlorophyll-a concentration

Amorim

Rick

Lohmann³

2021

Preprint

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“…They pointed out that the limited data set was one of the drawbacks of their research and encouraged others to collect more data to recalibrate and revalidate the model. Wang et al [19] employed a typical three-layer of MLP structure [77][78][79][80][81][82][83][84][85][86][87][88][89] with the BP algorithm to achieve Chl-a prediction. They divided the dataset into training (75%) and testing parts (25%).…”

Section: Artificial Neural Network Models For Water Quality Predictionmentioning

confidence: 99%

A Review of the Artificial Neural Network Models for Water Quality Prediction

et al. 2020

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Water quality prediction plays an important role in environmental monitoring, ecosystem sustainability, and aquaculture. Traditional prediction methods cannot capture the nonlinear and non-stationarity of water quality well. In recent years, the rapid development of artificial neural networks (ANNs) has made them a hotspot in water quality prediction. We have conducted extensive investigation and analysis on ANN-based water quality prediction from three aspects, namely feedforward, recurrent, and hybrid architectures. Based on 151 papers published from 2008 to 2019, 23 types of water quality variables were highlighted. The variables were primarily collected by the sensor, followed by specialist experimental equipment, such as a UV-visible photometer, as there is no mature sensor for measurement at present. Five different output strategies, namely Univariate-Input-Itself-Output, Univariate-Input-Other-Output, Multivariate-Input-Other(multi), Multivariate-Input-Itself-Other-Output, and Multivariate-Input-Itself-Other (multi)-Output, are summarized. From results of the review, it can be concluded that the ANN models are capable of dealing with different modeling problems in rivers, lakes, reservoirs, wastewater treatment plants (WWTPs), groundwater, ponds, and streams. The results of many of the review articles are useful to researchers in prediction and similar fields. Several new architectures presented in the study, such as recurrent and hybrid structures, are able to improve the modeling quality of future development.

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“…While long-term data driven chlorophyll-a concentration prediction for climate impact assessment is not widespread, there have been few studies conducted on both inland water systems (Cho et al, 2018;Keller et al, 2018;Liu et al, 2019;Luo et al, 2019) and marine systems (Irwin and Finkel, 2008;Blauw et al, 2018;Krasnopolsky et al, 2018;de Amorim et al, 2021) that performed short term predictions. Blauw et al (2018) predicted chlorophyll-a in the North Sea at different sites applying Generalized Additive Models (GAMs) with accuracies (R 2 values) ranging from 0.25 to 0.51 for hourly time scale, 0.15-0.22 for daily time scale, and 0.27-0.63 for bi-weekly time scale.…”

Section: Long Term Chlorophyll-a Projectionmentioning

confidence: 99%

Climate Change Induced Trends and Uncertainties in Phytoplankton Spring Bloom Dynamics

et al. 2021

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Spring phytoplankton blooms in the southern North Sea substantially contribute to annual primary production and largely influence food web dynamics. Studying long-term changes in spring bloom dynamics is therefore crucial for understanding future climate responses and predicting implications on the marine ecosystem. This paper aims to study long term changes in spring bloom dynamics in the Dutch coastal waters, using historical coastal in-situ data and satellite observations as well as projected future solar radiation and air temperature trajectories from regional climate models as driving forces covering the twenty-first century. The main objective is to derive long-term trends and quantify climate induced uncertainties in future coastal phytoplankton phenology. The three main methodological steps to achieve this goal include (1) developing a data fusion model to interlace coastal in-situ measurements and satellite chlorophyll-a observations into a single multi-decadal signal; (2) applying a Bayesian structural time series model to produce long-term projections of chlorophyll-a concentrations over the twenty-first century; and (3) developing a feature extraction method to derive the cardinal dates (beginning, peak, end) of the spring bloom to track the historical and the projected changes in its dynamics. The data fusion model produced an enhanced chlorophyll-a time series with improved accuracy by correcting the satellite observed signal with in-situ observations. The applied structural time series model proved to have sufficient goodness-of-fit to produce long term chlorophyll-a projections, and the feature extraction method was found to be robust in detecting cardinal dates when spring blooms were present. The main research findings indicate that at the study site location the spring bloom characteristics are impacted by the changing climatic conditions. Our results suggest that toward the end of the twenty-first century spring blooms will steadily shift earlier, resulting in longer spring bloom duration. Spring bloom magnitudes are also projected to increase with a 0.4% year−1 trend. Based on the ensemble simulation the largest uncertainty lies in the timing of the spring bloom beginning and -end timing, while the peak timing has less variation. Further studies would be required to link the findings of this paper and ecosystem behavior to better understand possible consequences to the ecosystem.

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Comparing artificial intelligence techniques for chlorophyll-a prediction in US lakes

Cited by 30 publications

References 37 publications

Evaluation of Machine Learning predictions of a highly resolved time series of Chlorophyll-a concentration

Evaluation of Machine Learning predictions of a highly resolved time series of Chlorophyll-a concentration

A Review of the Artificial Neural Network Models for Water Quality Prediction

Climate Change Induced Trends and Uncertainties in Phytoplankton Spring Bloom Dynamics

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