Optimization of deep learning model for coastal chlorophyll a dynamic forecast

Ding, Wenxiang; Zhang, Caiyun; Shang, Shaoping; Li, Xueding

doi:10.1016/j.ecolmodel.2022.109913

Cited by 12 publications

(2 citation statements)

References 41 publications

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“…Another LSTM was used to identify the most reliable Chl a proxy for predicting changes in algal biomass. Wenxiang et al (2022) found that the change rate and the relative change rate of Chl a were more accurate outputs for predicting the changes in biomass than the absolute concentration of Chl a , especially in winter and spring. Indeed, the correlation coefficient indicating the relationship between the forecasted and the observed concentration of Chl a exceeds 0.84 for all‐year‐round predictions when using the change rate and the relative change rate of Chl a , while it dropped below 0.6 when using the absolute concentration of Chl in winter and spring.…”

Section: Quantitative Analysis Of Plankton Dynamicsmentioning

confidence: 99%

Deep‐learning‐powered data analysis in plankton ecology

Bachimanchi,

Pinder,

Robert

et al. 2024

Limnol Oceanogr Letters

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The implementation of deep learning algorithms has brought new perspectives to plankton ecology. Emerging as an alternative approach to established methods, deep learning offers objective schemes to investigate plankton organisms in diverse environments. We provide an overview of deep‐learning‐based methods including detection and classification of phytoplankton and zooplankton images, foraging and swimming behavior analysis, and finally ecological modeling. Deep learning has the potential to speed up the analysis and reduce the human experimental bias, thus enabling data acquisition at relevant temporal and spatial scales with improved reproducibility. We also discuss shortcomings and show how deep learning architectures have evolved to mitigate imprecise readouts. Finally, we suggest opportunities where deep learning is particularly likely to catalyze plankton research. The examples are accompanied by detailed tutorials and code samples that allow readers to apply the methods described in this review to their own data.

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Section: Quantitative Analysis Of Plankton Dynamicsmentioning

confidence: 99%

Deep‐learning‐powered data analysis in plankton ecology

Bachimanchi,

Pinder,

Robert

et al. 2024

Limnol Oceanogr Letters

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“…Compared with the traditional water quality monitoring method based on field investigation, remote sensing technology has the advantages of a low cost, wide range and high efficiency. With the development of highresolution and multi-source satellite sensors, the retrieval of water-environment-related elements using remote sensing has been widely applied in the monitoring of water quality and hydro-environmental pollution in large areas of water [8][9][10][11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Using a Remote-Sensing-Based Piecewise Retrieval Algorithm to Map Chlorophyll-a Concentration in a Highland River System

Sun

Liu

et al. 2022

Remote Sensing

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Chlorophyll-a(chl-a) has been used as an important indicator of water quality. Great efforts have been invested to develop remote-sensing-based chl-a retrieval models. However, due to the spatial difference in chl-a concentration, a single model usually cannot accurately predict the whole range of chl-a concentration. To test the performance of precedent chl-a models, we carried out an experiment along the upper and middle reaches of the Kaidu River and around some small ponds in the Bayanbulak Wetland. We measured water surface reflectance in the field and analyzed the chl-a concentration in the laboratory. Initially, we performed a sensitivity analysis of the spectrum band to chl-a concentration with the aim of identifying the most suitable bands for various chl-a models. We found that the water samples could be divided into two groups with a threshold of 4.50 mg/m3. Then, we tested the performance of 11 precedent chl-a retrieval models and 7 spectral index-based regression models from this study for all the sample datasets and the two separate datasets with relatively high and low chl-a concentrations. Through a complete comparison of the performance of these models, we selected the D3B model for water bodies with high chl-a concentration and OC2 model (ocean color 2) for low chl-a concentration waters, resulting in the hierarchical and piecewise retrieval algorithm OC2-D3B. The chl-a concentration of 4.50 mg/m3 corresponded to the D3B value of −0.051; therefore, we used −0.051 as the threshold value of the OC2-D3B model. The result of the OC2-D3B model showed a better performance than the other algorithms. Finally, we mapped the spatial distribution and seasonal pattern of chl-a concentration in Bayanbulak Wetland using Sentinel-2 images from 2016 to 2019. The results indicated that the chl-a concentration in the riparian ponds was generally in the range of 8–10 mg/m3, which was higher than that in rivers with a range of 2–4 mg/m3. The highest chl-a concentration usually appears in summer, followed by spring and autumn, and the lowest in winter. The correlation between meteorological data and chl-a concentration showed that temperature is the dominant factor for chl-a concentration changes. Our analytical framework could provide a better way to accurately map the spatial distribution of chl-a concentration in complex river systems.

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