Research on Chlorophyll-a Concentration Retrieval Based on BP Neural Network Model—Case Study of Dianshan Lake, China

Zhu, Weidong; Qian, Chu-Yi; He, Nai-Ying; Kong, Yu-Xiang; Zou, Zi-Ya; Li, Yuwei

doi:10.3390/su14148894

Cited by 11 publications

(7 citation statements)

References 10 publications

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“…Chlorophyll-a (Chla), a photosynthetic pigment in major algae groups, is widely used as a critical indicator of phytoplankton presence [1,2]. As the abundance of algae can reflect the state of eutrophication, Chla is one of the most important parameters for evaluating the trophic condition of water bodies [3,4].…”

Section: Introductionmentioning

confidence: 99%

Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing Data and Six Machine Learning Methods

Oliveira Santos,

Guimarães,

Neto

et al. 2024

Remote Sensing

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It is crucial to monitor algal blooms in freshwater reservoirs through an examination of chlorophyll-a (Chla) concentrations, as they indicate the trophic condition of these waterbodies. Traditional monitoring methods, however, are expensive and time-consuming. Addressing this hindrance, we conducted a comprehensive investigation using several machine learning models for Chla modeling. To this end, we used in situ collected water sample data and remote sensing data from the Sentinel-2 satellite, including spectral bands and indices, for large-scale coverage. This approach allowed us to conduct a comprehensive analysis and characterization of the Chla concentrations across 149 freshwater reservoirs in Ceará, a semi-arid region of Brazil. The implemented machine learning models included k-nearest neighbors, random forest, extreme gradient boosting, the least absolute shrinkage, and the group method of data handling (GMDH); in particular, the GMDH approach has not been previously explored in this context. The forward stepwise approach was used to determine the best subset of input parameters. Using a 70/30 split for the training and testing datasets, the best-performing model was the GMDH model, achieving an R2 of 0.91, an MAPE of 102.34%, and an RMSE of 20.4 μg/L, which were values consistent with the ones found in the literature. Nevertheless, the predicted Chla concentration values were most sensitive to the red, green, and near-infrared bands.

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Section: Introductionmentioning

confidence: 99%

Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing Data and Six Machine Learning Methods

Oliveira Santos,

Guimarães,

Neto

et al. 2024

Remote Sensing

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show abstract

“…Chlorophyll-a (Chla), a photosynthetic pigment in major algae groups, is widely used as a critical indicator of phytoplankton presence [7,8]. Because the abundance of algae can reflect the state of eutrophication, Chla is one of the most important parameters for evaluating the trophic condition of water bodies [9,10].…”

Section: Introductionmentioning

confidence: 99%

Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing and Machine Learning

Oliveira Santos,

Guimarães,

Lima Neto

et al. 2024

Preprint

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Eutrophication, a global concern, impacts water quality, ecosystems, and human health. It’s crucial to monitor algal blooms in freshwater reservoirs, as they indicate the trophic condition of a waterbody through Chlorophyll-a (Chla) concentration. Traditional monitoring methods, however, are expen-sive and time-consuming. Addressing this hindrance, we developed models using remotely sensed data from the Sentinel-2 satellite for large-scale coverage, including its bands and spectral indexes, to estimate the Chla concentration on 149 freshwater reservoirs in Ceará, Brazil. Several machine learning models were trained and tested, including k-nearest neighbours, random forests, extreme gradient boosting, the least absolute shrinkage, group method of data handling (GMDH), and sup-port vector machine models. A stepwise approach determined the best subset of input parameters. Using a 70/30 split for the training and testing datasets, the best-performing model was the GMDH, achieving an R2 of 0.91, MAPE of 102.34%, and RMSE of 20.38 g/L, which are values consistent with the ones found in the literature. Nevertheless, the predicted Chla concentration values were most sensitive to the red, green, and near infra-red bands.

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“…Different machine learning models have been used to forecast lake surface water temperature [32,33]. Other artificial neural network models have been used for chlorophyll-α prediction [34,35].…”

Section: Introductionmentioning

confidence: 99%

Energy Budget, Water Quality Parameters and Primary Production Modeling in Lake Volvi in Northern Greece

Antonopoulos

Gianniou

2023

Sustainability

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A lake’s water quality and its ecosystem structure are mainly determined by heat storage change through its energy budget, dissolved oxygen, nutrients and primary productivity. A one-dimensional water quality model for lakes was used to estimate temperature, dissolved oxygen, phytoplankton (as chlorophyll-α), and inorganic and organic phosphorus. Evaporation, energy budget and surface water temperature were also computed. The results of the mathematical model simulation are presented and evaluated. Data from Lake Volvi in Central Macedonia (in northern Greece) for three successive years (2013 to 2015) were used to calibrate and recalibrate the model. The model results of water temperature, dissolved oxygen and primary productivity (Chlα) were compared with measurements for the years 2013 to 2015. The comparison showed that the predicted values of these parameters were all in good agreement with the measurements. The simulation results of water quality parameters generally exhibited the same seasonal dynamic and inter-annual variations as the measured data. The simulation results of the model application provided important information on changes in the physical, chemical and biological variables of the lake. The water temperature and heat fluxes at the water–atmosphere interface are crucial variables related to climate changes.

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Research on Chlorophyll-a Concentration Retrieval Based on BP Neural Network Model—Case Study of Dianshan Lake, China

Cited by 11 publications

References 10 publications

Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing Data and Six Machine Learning Methods

Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing Data and Six Machine Learning Methods

Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing and Machine Learning

Energy Budget, Water Quality Parameters and Primary Production Modeling in Lake Volvi in Northern Greece

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