A Genetic Algorithm-Optimized Neural Network for Chlorophyll a Estimation Using MODIS Satellite Data in Coastal Water: Application to the Sinpho Bay of DPR Korea

Ri, Tong-Chol; Jo, Jong-Song

doi:10.1007/s12524-023-01719-8

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2024

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Cited by 3 publications

References 48 publications

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Shoreline change along the Yombunjin sandy coast (DPR Korea) during Typhoon Maysak (2020): comparison of four longshore sediment transport formulae

Pak,

Jo,

Kim

2024

J Coast Conserv

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Shoreline change along the Yombunjin sandy coast (DPR Korea) during Typhoon Maysak (2020): comparison of four longshore sediment transport formulae

Pak,

Jo,

Kim

2024

J Coast Conserv

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Identification of Algal Blooms in Lakes in the Baltic States Using Sentinel-2 Data and Artificial Neural Networks

Grendaitė,

Petkevičius

2024

IEEE Access

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Algal blooms are a common problem in inland waters, which raise growing awareness on monitoring lakes' conditions. The on site monitoring is expensive and requires large human resources efforts. This work proposes remote monitoring techniques using satellite images and machine learning algorithms to predict chlorophyll concentration in water bodies and identify algal blooms. The training and test dataset used in this study includes diverse range of lakes in Baltic countries. The lake spectral features obtained from Sentinel-2 satellite images are used as predictors for proposed deep neural network models. The prediction of chlorophyll concentration with MAE 7.97 mg/m 3 and bloom vs. non-bloom classification with 71.6 % accuracy was achieved. The use of Bèzier curves for smoothing the point-wise prediction is proposed for identification of algal bloom characteristics: the bloom start date, end date, and duration. The results showed lake type impact on the blooming time. The experimental data and code are released with paper.INDEX TERMS Satellite image processing, chlorophyll prediction, deep neural networks, remote sensing, Bèzier curves.

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Contributing to Fishery Productivity in Colombia: A Machine Learning Approach to Predict Missing Chlorophyll-A Values Using MODIS Satellite Imagery

Vargas,

Laurido,

Latorre

et al. 2024

2024 XVIII National Meeting on Optics and the IX Andean and Caribbean Conference on Optics and Its Applications (ENO-CANCOA)

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A Genetic Algorithm-Optimized Neural Network for Chlorophyll a Estimation Using MODIS Satellite Data in Coastal Water: Application to the Sinpho Bay of DPR Korea

Cited by 3 publications

References 48 publications

Shoreline change along the Yombunjin sandy coast (DPR Korea) during Typhoon Maysak (2020): comparison of four longshore sediment transport formulae

Shoreline change along the Yombunjin sandy coast (DPR Korea) during Typhoon Maysak (2020): comparison of four longshore sediment transport formulae

Identification of Algal Blooms in Lakes in the Baltic States Using Sentinel-2 Data and Artificial Neural Networks

Contributing to Fishery Productivity in Colombia: A Machine Learning Approach to Predict Missing Chlorophyll-A Values Using MODIS Satellite Imagery

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