Climate variability and change, associated with increasing water demands, can have significant implications for water availability. In the Brazilian semi-arid, eutrophication in reservoirs raises the risk of water scarcity. The reservoirs have also a high seasonal and annual variability of water level and volume, which can have important effects on chlorophyll-a concentration (Chla). Assessing the influence of climate and hydrological variability on phytoplankton growth can be important to find strategies to achieve water security in tropical regions with similar problems. This study explores the potential of machine learning models to predict Chla in reservoirs and to understand their relationship with hydrological and climate variables. The model is based mainly on satellite data, which makes the methodology useful for data-scarce regions. Tree-based ensemble methods had the best performances among six machine …
%0 Journal Article
%1 carvalho2022uncovering
%A Carvalho, Taís Maria Nunes
%A Neto, Iran Eduardo Lima
%A de Assis Souza Filho, Francisco
%D 2022
%I Springer Berlin Heidelberg
%J Environmental Science and Pollution Research
%K imported topic_earthenvironment
%N 49
%P 74967-74982
%T Uncovering the influence of hydrological and climate variables in chlorophyll-A concentration in tropical reservoirs with machine learning
%V 29
%X Climate variability and change, associated with increasing water demands, can have significant implications for water availability. In the Brazilian semi-arid, eutrophication in reservoirs raises the risk of water scarcity. The reservoirs have also a high seasonal and annual variability of water level and volume, which can have important effects on chlorophyll-a concentration (Chla). Assessing the influence of climate and hydrological variability on phytoplankton growth can be important to find strategies to achieve water security in tropical regions with similar problems. This study explores the potential of machine learning models to predict Chla in reservoirs and to understand their relationship with hydrological and climate variables. The model is based mainly on satellite data, which makes the methodology useful for data-scarce regions. Tree-based ensemble methods had the best performances among six machine …
@article{carvalho2022uncovering,
abstract = {Climate variability and change, associated with increasing water demands, can have significant implications for water availability. In the Brazilian semi-arid, eutrophication in reservoirs raises the risk of water scarcity. The reservoirs have also a high seasonal and annual variability of water level and volume, which can have important effects on chlorophyll-a concentration (Chla). Assessing the influence of climate and hydrological variability on phytoplankton growth can be important to find strategies to achieve water security in tropical regions with similar problems. This study explores the potential of machine learning models to predict Chla in reservoirs and to understand their relationship with hydrological and climate variables. The model is based mainly on satellite data, which makes the methodology useful for data-scarce regions. Tree-based ensemble methods had the best performances among six machine …},
added-at = {2024-11-29T11:56:28.000+0100},
author = {Carvalho, Taís Maria Nunes and Neto, Iran Eduardo Lima and de Assis Souza Filho, Francisco},
biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/2fa042d1bb14d705e9994a6055b61e71b/joum576e},
citation = {Environmental Science and Pollution Research 29 (49), 74967-74982, 2022},
interhash = {e4f0b065b2277d4753d986520bf3a08e},
intrahash = {fa042d1bb14d705e9994a6055b61e71b},
journal = {Environmental Science and Pollution Research},
keywords = {imported topic_earthenvironment},
number = 49,
pages = {74967-74982},
publisher = {Springer Berlin Heidelberg},
timestamp = {2024-11-29T11:56:28.000+0100},
title = {Uncovering the influence of hydrological and climate variables in chlorophyll-A concentration in tropical reservoirs with machine learning},
volume = 29,
year = 2022
}
