Abstract. With increasing lake monitoring data, data-driven machine learning (ML) models might be able to capture the complex algal bloom dynamics that cannot completely described in process-based (PB) models. We applied two ML models, gradient boost regressor (GBR) and long short-term memory (LSTM) network, predict blooms seasonal changes chlorophyll concentrations (Chl) a mesotrophic lake. Th...

The waters of Lubuk Gaung Village, Sungai Sembilan District are areas that widely used as transportation routes. Activities in these will cause environmental pollution, which affects the life phytoplankton. purpose this study was to determine relationship between primary productivity phytoplankton with oceanographic parameters District, Dumai. method is survey research station and situ for meas...

Abstract Based on weekly sampling, the present short-term study was set out over a one-year cycle in Eastern Harbor (Alexandria, Egypt). This aims to assess water quality monitoring data. Among 13 indexes used define harbor, 6 depend physical and chemical variables, other are biological criteria. Chlorophyll concentrations phytoplankton cell numbers were also as indicators for assessing trophic...

BACKGROUND Mortality is a main driver in zooplankton population biology but it is poorly constrained in models that describe zooplankton population dynamics, food web interactions and nutrient dynamics. Mortality due to non-predation factors is often ignored even though anecdotal evidence of non-predation mass mortality of zooplankton has been reported repeatedly. One way to estimate non-predat...

A variety of models have been developed for estimating chlorophyll-a (Chl-a) concentration in turbid and productive waters. All are based on optical information in a few spectral bands in the red and near-infra-red regions of the electromagnetic spectrum. The wavelength locations in the models used were meticulously tuned to provide the highest sensitivity to the presence of Chl-a and minimal s...