Version 1
: Received: 22 December 2023 / Approved: 25 December 2023 / Online: 25 December 2023 (13:20:14 CET)
Version 2
: Received: 19 February 2024 / Approved: 20 February 2024 / Online: 20 February 2024 (11:44:18 CET)
How to cite:
Oliveira Santos, V.; Guimarães, B.M.D.M.; Lima Neto, I.E.; De Souza Filho, F.D.A.; Costa Rocha, P.A.; Van Griensven Thé, J.; Gharabaghi, B. Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing and Machine Learning. Preprints2023, 2023121854. https://doi.org/10.20944/preprints202312.1854.v2
Oliveira Santos, V.; Guimarães, B.M.D.M.; Lima Neto, I.E.; De Souza Filho, F.D.A.; Costa Rocha, P.A.; Van Griensven Thé, J.; Gharabaghi, B. Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing and Machine Learning. Preprints 2023, 2023121854. https://doi.org/10.20944/preprints202312.1854.v2
Oliveira Santos, V.; Guimarães, B.M.D.M.; Lima Neto, I.E.; De Souza Filho, F.D.A.; Costa Rocha, P.A.; Van Griensven Thé, J.; Gharabaghi, B. Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing and Machine Learning. Preprints2023, 2023121854. https://doi.org/10.20944/preprints202312.1854.v2
APA Style
Oliveira Santos, V., Guimarães, B.M.D.M., Lima Neto, I.E., De Souza Filho, F.D.A., Costa Rocha, P.A., Van Griensven Thé, J., & Gharabaghi, B. (2024). Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing and Machine Learning. Preprints. https://doi.org/10.20944/preprints202312.1854.v2
Chicago/Turabian Style
Oliveira Santos, V., Jesse Van Griensven Thé and Bahram Gharabaghi. 2024 "Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing and Machine Learning" Preprints. https://doi.org/10.20944/preprints202312.1854.v2
Abstract
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.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
