Analyzing Electroencephalogram (EEG) signal is a challenge due to the various artifacts used by Electromyogram, eye blink and Electrooculogram. The present de-noising techniques that are based on the frequency selective filtering suffers from a substantial loss of the EEG data. Noise removal using wavelet has the characteristic of preserving signal uniqueness even if noise is going to be minimized. To remove noise from EEG signal, this research employed discrete wavelet transform. Root mean square difference has been used to find the usefulness of the noise elimination. In this research, four different discrete wavelet functions have been used to remove noise from the Electroencephalogram signal gotten from two different types of patients (healthy and epileptic) to show the effectiveness of DWT on EEG noise removal. The result shows that the WF orthogonal meyer is the best one for noise elimination from the EEG signal of epileptic subjects and the WF Daubechies 8 (db8) is the best one for noise elimination from the EEG signal on healthy subjects.

The prediction of algal chlorophyll-a and water clarity in lentic ecosystems is a hot issue due to rapid deteriorations of drinking water quality and eutrophication processes. Our key objectives of the study were to predict long-term algal chlorophyll-a and transparency (water clarity), measured as Secchi depth, in spatially heterogeneous and temporally dynamic reservoirs largely influenced by the Asian monsoon during 2000-2017 and then determine the reservoir trophic state using a multiple linear regression (MLR), support vector machine (SVM) and artificial neural network (ANN). We tested the models to analyze the spatial patterns of the riverine zone (Rz), transitional zone (Tz) and lacustrine zone (Lz) and temporal variations of premonsoon, monsoon and postmonsoon. Monthly physicochemical parameters and precipitation data (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017) were used to build up the models of MLR, SVM and ANN and then were confirmed by cross-validation processes. The model of SVM showed better predictive performance than the models of MLR and ANN, in both before validation and after validation. Values of root mean square error (RMSE) and mean absolute error (MAE) were lower in the SVM model, compared to the models of MLR and ANN, indicating that the SVM model has better performance than the MLR and ANN models. The coefficient of determination was higher in the SVM model, compared to the MLR and ANN models. The mean and maximum total suspended solids (TSS), nutrients (total nitrogen (TN) and total phosphorus (TP)), water temperature (WT), conductivity and algal chlorophyll (CHL-a) were in higher concentrations in the riverine zone compared to transitional and lacustrine zone due to surface run-off from the watershed. During the premonsoon and postmonsoon, the average annual rainfall was 59.50 mm and 54.73 mm whereas it was 236.66 mm during the monsoon period. From 2013 to 2017, the trophic state of the reservoir on the basis of CHL-a and SD was from mesotrophic to oligotrophic. Analysis of the importance of input variables indicated that WT, TP, TSS, TN, NP ratios and the rainfall influenced the chlorophyll-a and transparency directly in the reservoir. These findings of the algal chlorophyll-a predictions and Secchi depth may provide key clues for better management strategy in the reservoir.Water 2020, 12, 30 2 of 20 state of the reservoirs and to manage them efficiently, some techniques have to be developed for monitoring and modeling.Mechanistic modeling of the eutrophication is a difficult task due to insufficient observations and the complex behavior of the reservoir ecosystem [5]. One promising action could be the chlorophyll-a and transparency (Secchi depth) prediction by incorporating key environmental variables like as precipitation, water temperature, nutrients, biological oxygen demand and total suspended solids. The reason for using CHL-a and transparency is their wide application as indicators of the eutrophication and turbidit...

Long-term variations in reservoir water chemistry could provide essential data in making sustainable water quality management decisions. Here, we analyzed the spatiotemporal variabilities of nutrients, sestonic chlorophyll-a (CHL-a), nutrient enrichment, dominant algal species, and overall chemical water health of the third-largest drinking water reservoir in South Korea during 2000–2020. Our results distinctly explained the strong influence of monsoon rainfall on spatial and annual water chemistry variations. We observed a consistent increase in the chemical oxygen demand alluding to organic matter pollutants, while a steady declining trend in the sestonic CHL-a. The long-term total phosphorus (TP) level showed a steady reduction from the riverine zone to the lacustrine area. However, a higher total coliform bacteria (TCB) was observed at the water intake tower sites. TP displayed a strong link to algal CHL-a and ambient nitrogen phosphorus ratios, suggesting a robust phosphorus-limitation state. The severe phosphorus-limitation was also corroborated by the findings of trophic state index deviation. The high and low flow dynamics exhibited the strong influence of intensive rainfall carrying many nutrients and sediments and flushing out the sestonic CHL-a. Successive eutrophic conditions prevailed along with dominating blue-green algae species (Microcystis and Anabaena). We observed a strong positive correlation (r = 0.62) between water temperature and CHL-a and between total suspended solids and TP (r = 0.65). The multi-metric water pollution index characterized the overall water quality as ‘good’ at all the study sites. In conclusion, the long-term spatiotemporal variabilities of the ecological functions based on the nutrient-CHL-a empirical models are regulated mainly by the intensive monsoon precipitation. The drinking water could become hazardous under the recurrent eutrophication events and chemical degradations due to uncontrolled and untreated inflow of sewage and wastewater treatment plant effluents. Therefore, we strongly advocate stringent criteria to mitigate phosphorus and organic pollutant influx for sustainable management of Daecheong Reservoir.

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The main objectives of the study were to determine the trophic response of the temperate reservoir to seasonal and interannual variabilities of monsoon inorganic solids and nutrients along the gradients of the morphologically complex Asian reservoir using long-term datasets between 2000–2018. Nutrient regime (total nitrogen—TN, total phosphorus—TP), total suspended solids (TSS), and chlorophyll-a (CHL-a) were primarily affected by an intensity of summer monsoon and the longitudinal structure of riverine (Rz), transitional (Tz), and lacustrine (Lz) zone. The reservoir is a nitrogen-rich system and the phosphorus content of the water was relatively low, and it had low mean N:P ratios (<40), implying a P-limiting system. The Lz was a highly P-limited zone in comparison to Rz and Tz zone during both drought (2015) and flood year (2011). The TP content was higher in the mainstem (S3) than the embankment (S4 and S6) of the reservoir due to the monsoon river inputs of the nutrients. Nonparametric Mann–Kendall tests indicated that TP decreased over the long-term years in the Rz, while it did not show any trend in Tz, Lz, IT1, and IT2. TN showed an increasing trend in Rz, Tz, Lz, and IT2 except for IT1. The empirical regression model for chlorophyll nutrients showed that CHL-a had a strong positive relationship with TP (R2 = 0.67, p < 0.01) than TN (R2 = 0.06, p < 0.01), supporting the view that algal growth in lentic systems responds to TP enrichment and TP may provide a reliable basis for predicting algal biomass. The seasonality of CHL-a and TP showed a monomodal pattern and indicates that summer TP influences summer algal growth in Tz, Lz, and IT2. The water clarity (SD) of the reservoir was significantly (p < 0.01) influenced by TP (R2 = 0.62), TSS (R2 = 0.67), and CHL-a (R2 = 0.68) rather than TN (R2 = 0.10). The non-algal light attenuation coefficient (Kna) was determined mainly by suspended solids and the monsoon hydrology. The trophic state was much higher when assessments were based on TSI (CHL-a) than on TSI (TP) and TSI (SD). TSI (CHL-a) indicated the eutrophic state of the reservoirs except for the zone of Lz during the premonsoon season. Analysis of trophic state index deviation (TSID) suggested that the blue-green algae dominated the algal community, and the effects of non-algal turbidity and zooplankton grazing were minor in the reservoir.