Sundarambal Palani

An ANN application for water quality forecasting

Rapid urban and coastal developments often witness deterioration of regional seawater quality. As part of the management process, it is important to assess the baseline characteristics of the marine environment so that sustainable development can be pursued. In this study, artificial neural networks (ANNs) were used to predict and forecast quantitative characteristics of water bodies. The true power and advantage of this method lie in its ability to (1) represent both linear and non-linear relationships and (2) learn these relationships directly from the data being modeled. The study focuses on Singapore coastal waters. The ANN model is built for quick assessment and forecasting of selected water quality variables at any location in the domain of interest. Respective variables measured at other locations serve as the input parameters. The variables of interest are salinity, temperature, dissolved oxygen, and chlorophyll-alpha. A time lag up to 2Delta(t) appeared to suffice to yield good simulation results. To validate the performance of the trained ANN, it was applied to an unseen data set from a station in the region. The results show the ANNs great potential to simulate water quality variables. Simulation accuracy, measured in the Nash-Sutcliffe coefficient of efficiency (R(2)), ranged from 0.8 to 0.9 for the training and overfitting test data. Thus, a trained ANN model may potentially provide simulated values for desired locations at which measured data are unavailable yet required for water quality models.

Determination of total nitrogen in atmospheric wet and dry deposition samples.

A microwave-assisted persulfate oxidation method followed by ion chromatographic determination of nitrate was developed for total nitrogen determination in atmospheric wet and dry deposition samples. Various operating parameters such as oxidation reagent concentrations, microwave power, and extraction time were optimized to maximize the conversion of total nitrogen to nitrate for subsequent chemical analysis. Under optimized conditions, 0.012 M K(2)S(2)O(8) and 0.024 M NaOH were found to be necessary for complete digestion of wet and dry deposition samples at 400 W for 7 min using microwave. The optimized extraction method was then validated by testing different forms of organic nitrogen loaded to pre-baked filter substrates and NIST SRM 1648 (urban particulate matter), and satisfactory results were obtained. In the case of wet deposition samples, standard addition experiments were performed. The suitability of the method for real-world application was assessed by analyzing a number of wet and dry deposition samples collected in Singapore during the period of March-April 2007. The organic nitrogen content was 15% (wet) and 30% (dry) of the total nitrogen. During the study period, the estimated wet fluxes for nitrate (NO(3)(-)), ammonium (NH(4)(+)), organic nitrogen (ON), and total nitrogen (TN) were 16.1+/-6.5 kg ha(-1)year(-1), 11.5+/-5.7 kg ha(-1)year(-1), 3.8+/-1.5 kg ha(-1)year(-1)and 31.5+/-13.2 kg ha(-1)year(-1), respectively, while the dry fluxes were 2.5+/-0.8 kg ha(-1)year(-1), 1.4+/-0.9 kg ha(-1)year(-1), 2.3+/-1.4 kg ha(-1)year(-1) and 7.5+/-2.6 kg ha(-1)year(-1), respectively.

ANN application for prediction of atmospheric nitrogen deposition to aquatic ecosystems

The occurrences of increased atmospheric nitrogen deposition (ADN) in Southeast Asia during smoke haze episodes have undesired consequences on receiving aquatic ecosystems. A successful prediction of episodic ADN will allow a quantitative understanding of its possible impacts. In this study, an artificial neural network (ANN) model is used to estimate atmospheric deposition of total nitrogen (TN) and organic nitrogen (ON) concentrations to coastal aquatic ecosystems. The selected model input variables were nitrogen species from atmospheric deposition, Total Suspended Particulates, Pollutant Standards Index and meteorological parameters. ANN models predictions were also compared with multiple linear regression model having the same inputs and output. ANN model performance was found relatively more accurate in its predictions and adequate even for high-concentration events with acceptable minimum error. The developed ANN model can be used as a forecasting tool to complement the current TN and ON analysis within the atmospheric deposition-monitoring program in the region.

Temporal variability and climatology of hydrodynamic, water property and water quality parameters in the West Johor Strait of Singapore.

The study presents a baseline variability and climatology study of measured hydrodynamic, water properties and some water quality parameters of West Johor Strait, Singapore at hourly-to-seasonal scales to uncover their dependency and correlation to one or more drivers. The considered parameters include, but not limited by sea surface elevation, current magnitude and direction, solar radiation and air temperature, water temperature, salinity, chlorophyll-a and turbidity. FFT (Fast Fourier Transform) analysis is carried out for the parameters to delineate relative effect of tidal and weather drivers. The group and individual correlations between the parameters are obtained by principal component analysis (PCA) and cross-correlation (CC) technique, respectively. The CC technique also identifies the dependency and time lag between driving natural forces and dependent water property and water quality parameters. The temporal variability and climatology of the driving forces and the dependent parameters are established at the hourly, daily, fortnightly and seasonal scales.

Atmospheric Dry Deposition Of Total Phosphorous Prediction Using Neural Network

Surface waters are threatened by pollutants of atmospheric origin. Atmospheric deposition in Singapore and surrounding countries appears to provide significant fluxes of nutrients (Nitrogen species and Phosphorous species) of environmental concern and to play an important role in the eutrophication of surface water. The quantitative assessment of atmospheric nutrient depositions are essential for understanding regional variations, time at which critical loads exceed, and the air/water quality management on the regional scale. This paper presents a data-driven approach, artificial neural networks (ANNs), for effective and efficient prediction of total phosphorous (TP) from dry atmospheric deposition. Dry atmospheric nutrient deposition data were obtained from laboratory analysis of aerosol samples collected using a high volume air sampler every 48 hours continuously during 2006 haze period in the field monitoring, Singapore. The results of the best ANN model was rather satisfactory, with values of the Nash–Sutcliffe coefficient of efficiency

Simulation of anaerobic digestion processes using stochastic algorithm

BackgroundThe Anaerobic Digestion (AD) processes involve numerous complex biological and chemical reactions occurring simultaneously. Appropriate and efficient models are to be developed for simulation of anaerobic digestion systems. Although several models have been developed, mostly they suffer from lack of knowledge on constants, complexity and weak generalization. The basis of the deterministic approach for modelling the physico and bio-chemical reactions occurring in the AD system is the law of mass action, which gives the simple relationship between the reaction rates and the species concentrations. The assumptions made in the deterministic models are not hold true for the reactions involving chemical species of low concentration. The stochastic behaviour of the physicochemical processes can be modeled at mesoscopic level by application of the stochastic algorithms.MethodIn this paper a stochastic algorithm (Gillespie Tau Leap Method) developed in MATLAB was applied to predict the concentration of glucose, acids and methane formation at different time intervals. By this the performance of the digester system can be controlled. The processes given by ADM1 (Anaerobic Digestion Model 1) were taken for verification of the model.ResultsThe proposed model was verified by comparing the results of Gillespies algorithms with the deterministic solution for conversion of glucose into methane through degraders. At higher value of `τ` (timestep), the computational time required for reaching the steady state is more since the number of chosen reactions is less. When the simulation time step is reduced, the results are similar to ODE solver.ConclusionIt was concluded that the stochastic algorithm is a suitable approach for the simulation of complex anaerobic digestion processes. The accuracy of the results depends on the optimum selection of tau value.

Simulation of ammonium and chromium transport in porous media using coupling scheme of a numerical algorithm and a stochastic algorithm.

The migration of the species of chromium and ammonium in groundwater and their effective remediation depend on the various hydro-geological characteristics of the system. The computational modeling of the reactive transport problems is one of the most preferred tools for field engineers in groundwater studies to make decision in pollution abatement. The analytical models are less modular in nature with low computational demand where the modification is difficult during the formulation of different reactive systems. Numerical models provide more detailed information with high computational demand. Coupling of linear partial differential Equations (PDE) for the transport step with a non-linear system of ordinary differential equations (ODE) for the reactive step is the usual mode of solving a kinetically controlled reactive transport equation. This assumption is not appropriate for a system with low concentration of species such as chromium. Such reaction systems can be simulated using a stochastic algorithm. In this paper, a finite difference scheme coupled with a stochastic algorithm for the simulation of the transport of ammonium and chromium in subsurface media has been detailed.

A probabilistic cellular automaton for two dimensional contaminant transport simulation in ground water

In recent years evolutionary computing algorithms have been proposed to solve many engineering problems. Genetic algorithms, Neural Networks, and Cellular Automata are the branches of evolutionary computing techniques. In this study, it is proposed to simulate the contaminant transport in porous media using a Cellular Automaton. The physical processes and chemical reactions occurring in the ground water system are intricately connected at various scales of space, time, transport coefficients and molecular concentration. The validity of continuous approach for the simulation of chemical systems with low concentration of species and intracellular environments has become subtle. Due to the difference in scales of various processes that occur in the ground water system, the description of the system can be well defined in the intermediate scale called mesoscopic scale, which is in between microscopic and macroscopic description. Mesoscopic models provide the relationship between various parameters and their evolvement in time, thus establishing the contact between modeling at various scales at the interface. In this paper, a Probabilistic Cellular Automaton (PCA) model has been developed based on the transport and reaction probability values. The developed model was verified and validated for one, two dimensional transport systems and also for the simulation of BTEX transport in two dimensional system in ground water.

Water Quality Modelling in the East Johor and Singapore Straits

The Singapore coastal water is bounded by the (East and West) Johor Strait in the North and the Singapore Strait in the South. The hydrodynamic currents in the Singapore Strait are dominated by semi-diurnal tides with a bias in the eastern (Southwest monsoon) or western (Northeast monsoon) directions. The salinity distribution within the coastal waterways reflects the relative influx of fresh water supplied by rivers, and depends on mixing ability of receiving water. Concentration of Total Suspended Solids (TSS), in addition to the above phenomena, is varying also on shear stress and the tidal stream. Salinity and TSS are the most basic water property parameters affecting the marine water quality and ecosystem. In this study, in-house 3D water quality model NEUTRO is used to simulate transport of salinity and TSS in the water column. The model is calibrated and validated using available data and is able to reproduce the observed salinity and TSS concentrations in both the East Johor and Singapore Straits fairly well, despite the complex physical processes evident in the study domain. This paper describes the results obtained for the baseline calibration of salinity and TSS in the model domain. This numerical modeling study helps to understand the behavior of baseline water quality and is useful for environmental impact assessment (EIA) in the Singapore coastal waters.