Modelling of Chlorophyll-A Concentrations in Pulicat Lagoon , Southeast Coast of India Using Artificial Neural Network (original) (raw)
Related papers
Applied Sciences
The Youngsan River estuary, located on the southwest coast of South Korea, has transitioned from a natural to an artificial estuary since dike construction in 1981 separated freshwater and seawater zones. This artificial transition has induced changes in the physical properties and circulation within the estuary, which has led to hypoxia and algal blooms. In this study, an artificial neural network (ANN) model was employed to simulate phytoplankton variations, including algal blooms and size fractions based on chlorophyll a, using data obtained by long-term monitoring (2008–2018) of the seawater zone of the Youngsan River estuary. The model was validated through statistical analyses, and the validated model was used to determine the contribution of the environmental factors on size-fractionated phytoplankton variations. The statistical validation of the model showed extremely low sum square error (SSE ≤ 0.0003) and root mean square error (RMSE ≤ 0.0173) values, with R2 ≥ 0.9952. The...
Use of AI to predict estuarine chlorophyll level.pdf
The time series analysis of chlorophyll a was carried out for more than 3 decades (1984-2018) from the coastal water of Digha and the data bank were subject to Nonlinear Autoregressive Neural Network Model to evaluate the status of the coastal water in 2050. The concentration of chlorophyll a ranged between 1.05 mgm-3 (in 2009) to 5.16 mgm-3 (in 1984) during the span of 35 years (real-time data). Chlorophyll a has a great role to drive the marine and estuarine food chain as it acts as the engine to transfer the energy derived from the Sun through different tires of the food chain. The decreasing trend of chlorophyll a with time is a warning signal for the fishery products from the region as the phytoplankton containing chlorophyll a serve as the major food of the fishes.
Forecasting Algal Blooms at a Surface Water System with Artificial Neural Network
2006
Algal blooms (AB) in potable water supplies are becoming an increasingly prevalent and serious water quality problem around the world. AB events can cause taste and odor problems, damage the environment, and some algal classes like cyanobacteria (blue-green algae) may release toxins that can cause human illness or even death. There is a need to develop models that can accurately forecast algal bloom events on the basis of predictive physical, meteorological, chemical, and biological information. Such forecasting models can provide valuable lead time for water treatment systems to implement measures to minimize the consequences of the AB event, if not actually prevent it. Given the multitude, interplay, and complexity of the various controlling environmental factors, modeling and forecasting AB is a daunting challenge. This research focused on the feasibility of using artificial neural network (ANN) technology as an accurate, realtime modeling and forecasting tool. Previously-collect...
Artificial Neural Network Model to Prediction of Eutrophication and Microcystis Aeruginosa Bloom
Emerging Science Journal, 2020
Maekuang reservoir is one of the water resources which provides water supply, livestock, and recreational in Chiangmai city, Thailand. The water quality and Microcystis aeruginosa are a severe problem in many reservoirs. M. aeruginosa is the most widespread toxic cyanobacteria in Thailand. Difficulty prediction for planning protects Maekuang reservoirs, the artificial Neural Network (ANN) model is a powerful tool that can be used to machine learning and prediction by observation data. ANN is able to learn from previous data and has been used to predict the value in the future. ANN consists of three layers as input, hidden, and output layer. Water quality data is collected biweekly at Maekuang reservoir (1999-2000). Input data for training, including nutrients (ammonium, nitrate, and phosphorus), Secchi depth, BOD, temperature, conductivity, pH, and output data for testing as Chlorophyll a and M. aeruginosa cells. The model was evaluated using four performances, namely; mean squared ...