Rashmi Bhardwaj

River Water Prediction Modeling Using Neural Networks, Fuzzy and Wavelet Coupled Model

In this paper, new prediction model introduced by coupling of neural networks model, fuzzy model and wavelet model for the water resources management. Artificial neural network (ANN), fuzzy, wavelet and adaptive neuro-fuzzy inference system (ANFIS) are found to be a sturdy tool to model many non-linear hydrological processes. Wavelet transformation will improve the ability of a prediction model by capturing valuable information on different resolution levels. The target of this research is to compare our model with other famous data-driven models for monthly forecasting of water quality parameter chemical oxygen demand (COD) level monitored at Nizamuddin station, New Delhi, India of river Yamuna based on the past history. The data has been decomposed into wavelet domain constitutive sub series using Daubechies wavelet at level 8 (Db8). Statistical behavior of wavelet domain constitutive series has been studied. The foretelling performance of the wavelet coupled model has been compared with classical neuro fuzzy, artificial neural network and regression models. The result shows that the wavelet coupled model produces considerably higher leads to comparison to neuro fuzzy, neural network, regression models.

Time series and predictability analysis of air pollutants in Delhi

Air pollution is a challenging problem globally and the complete globe is facing the hazards caused by it. In the recent years, it draws an attention of all as it is directly related with the health concern of an individual. Air pollution is a big concern for Delhi. More than 15 million people are exposed to severely high pollutant concentrations. Air pollution have a high impact on the environment also. Government of Delhi performed a litmus test of vehicular emissions in the form of Odd-Even Scheme (15April 2016–30 April 2016). This paper deals with the estimation of regression coefficient, Hurst exponent, Fractal Dimension and Predictability Index of air pollutants NO, NO2, NOx, SO2, PM2.5 and the weather conditions relative humidity and temperature during (15April 2016–30 April 2016), pre (30 March 2016–14 April 2016) and post Odd-Even Scheme (1 May 2016–16 May 2016) in the Dwarka area adjoining Indira Gandhi International Airport, Delhi. The Hurst exponent is defined as the index of long-range dependence. It measures a relative tendency of a time series either regress strongly to the mean or to cluster in a direction. It is related to fractal dimension which gives measure for roughness of surface. The Predictability Index describes the behaviour of time series. The data for the above period is taken from Central Pollution Control Board, Government of India. It is observed that carbon mono oxide (CO) behavior is unpredictable with Relative Humidity and sulphur-di-oxide (SO2) as they follows a Brownian motion for pre and post the Odd-Even Scheme simultaneously. During the period of Odd-Even Scheme, the behaviour of temperature with respect to PM2.5 is unpredictable. It is concluded that CO with PM2.5 follows a Brownian time series and hence the trend is unpredictable and SO2 for pre and post Odd-Even Scheme follows a Brownian time series. Thus it is difficult to predict the behavior and trend of the pollutants.

Surface roughness effect on couple stress fluid lubricated Porous pivoted slider bearings

Abstract A theoretical model is developed to study the surface roughness effect on couple stress fluid lubricated Porous pivoted slider bearings. Mathematical model of Reynolds equation is obtained for rough porous pivoted slider bearing. Capacity for load bearing and point where pressure is centered are evaluated in form of various parameters that are couple stress, permeability and surface roughness. It is concluded that capacity for load bearing increases with roughness and decreases with increases in permeability parameters. Normal behaviour exists for surface roughness parameters with pressure and pressure with permeability parameters.

Fractal, predictability index and variability in trends analysis of river-water dynamics

ABSTRACT Statistical modelling, analysis of physico-chemical parameters chemical oxygen demand (COD), biochemical oxygen demand (BOD), dissolved oxygen (DO), water temperature (WT), free ammonia (AMM), total Kjeldahl nitrogen (TKN), total coliform (TC), fecal coliform (FC) and potential of hydrogen (pH) monitored at the Hathnikund barrage (Haryana) sample site of river Yamuna in India have been studied. It has been observed that water-quality parameters such as COD-BOD, AMM-TKN, WT-pH and TC-FC are positively correlated whereas COD-DO, BOD-DO, TKN-FC and DO-WT are negatively correlated. For water-quality parameters such as pH, AMM, TC and FC no seasonal pattern is observed. Parameters such as COD, BOD, TKN, DO and WT follow a six-month seasonal pattern. All the parameters except DO and WT follow a positive trend for monthly and annual variations. BOD, AMM and TKN have anti-persistence behaviour for both monthly and yearly variations. For parameters COD (+27.83%), BOD (+42.36%), AMM (+49.63%), TKN (+22.71%), TC (+141.80%) and FC (+42.89%) the future trend remains positive with high variability. WT (−7.47%) follows a negative trend with low variation and DO (−17.12%) has a negative trend with lofty variation. Using fractal, predictability index and variability in trend analysis, it is concluded that all parameters, except pH and WT, cross the prescribed limits of WHO/EPA and if the same trend should be followed, then in the future the quality of water shall continuously deteriorate and water may not be fit for drinking, agriculture and industrial use.

Accelerating order of convergence using secant type methods

Abstract By amalgamating a secant type method and a Newton type method, another method of order 4 has been derived. The method has been supported by examples and has been compared with existing similar methods.

Predictability and Wavelet Analysis of Air Pollutants for Commercial and Industrial Regions in Delhi

This paper deals with the statistical and wavelet analysis of air pollutants SO2 (sulphur dioxide), CO (carbon monoxide) and PM10 (coarse particulate matter) monitored at two regions of New Delhi-Shadipur, the residential cum industrial area and Indira Gandhi International Airport (IGIA), the primary civilian aviation hub. Pearson product-moment correlation coefficient is a measure of the strength and direction of the linear relationship between two variables. It is observed that CO is highly correlated with SO2 and PM10 follows Brownian motion with SO2. Descriptive statistics are ways of summarising large sets of quantitative (numerical) information. It is observed that CO and PM10 have higher values of mean, median, mode at IGIA than Shadipur but SO2 has higher values at Shadipur than IGIA. Also, CO and PM10 have lower values of kurtosis, skewness at IGIA than Shadipur but SO2 has lower values at Shadipur than IGIA. All parameters have low value of range and standard deviation, at IGIA as compared to Shadipur. As the values of standard deviation for air pollutants PM10, CO and SO2 are high thus pollutant concentrations are spread out from the mean line.

Location specific forecasting of maximum and minimum temperatures over India by using the statistical bias corrected output of global forecasting system

The output from Global Forecasting System (GFS) T574L64 operational at India Meteorological Department (IMD), New Delhi is used for obtaining location specific quantitative forecast of maximum and minimum temperatures over India in the medium range time scale. In this study, a statistical bias correction algorithm has been introduced to reduce the systematic bias in the 24–120 hour GFS model location specific forecast of maximum and minimum temperatures for 98 selected synoptic stations, representing different geographical regions of India. The statistical bias correction algorithm used for minimizing the bias of the next forecast is Decaying Weighted Mean (DWM), as it is suitable for small samples. The main objective of this study is to evaluate the skill of Direct Model Output (DMO) and Bias Corrected (BC) GFS for location specific forecast of maximum and minimum temperatures over India. The performance skill of 24–120 hour DMO and BC forecast of GFS model is evaluated for all the 98 synoptic stations during summer (May-August 2012) and winter (November 2012–February 2013) seasons using different statistical evaluation skill measures. The magnitude of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) for BC GFS forecast is lower than DMO during both summer and winter seasons. The BC GFS forecasts have higher skill score as compared to GFS DMO over most of the stations in all day-1 to day-5 forecasts during both summer and winter seasons. It is concluded from the study that the skill of GFS statistical BC forecast improves over the GFS DMO remarkably and hence can be used as an operational weather forecasting system for location specific forecast over India.

Time Delay Stabilizes Chaos Dynamics in Economic System

This paper discusses the role of time delay feedback which stabilizes the chaos of nonlinear financial model. The interest rate, investment demand and price index are modelled with the help of saving amount, cost per investment, demand elasticity of commercial markets and the strength of feedback. All these parameters are considered to be positive. The stability of financial model is studied by Routh-Hurwitz criterion. The distributed time delay feedback strength stabilized the unstable financial system. Bifurcation of parameter and Lyapunov exponent is simulated for time delay feedback system. Using the numerical method, it is observed that the inappropriate combination of saving amount, cost per investment and elasticity of investment demand of commercial market in the financial system is the root cause of chaos. The unstable system is stabilized by introducing the distributed time delay feedback strength. The stability and chaotic behaviour of systems gives the condition and behaviour of economic implications. It is concluded that the system is chaotic and to ensure stability of state, it is controlled by time delay feedback controlled system.

Chaos in Nanofluidic Convection of CuO Nanofluid

This paper deals with the nonlinear stability dynamics of nanofluid convection under magnetic and temperature variation for Copper Oxide (CuO) nanofluid, which is used as coolant in heat transfer applications. The system comprises a cavity in which the fluid layer is subjected to external magnetic field and heat exposure. The partial differential equations of conservation of momentum and energy are the governing equations, which are converted to a system of nonlinear differential equations. Using stability, phase portrait and time series analysis, the effect of magnetic field and temperature variation through Hartmann number and Rayleigh number on the chaotic CuO nanofluid convection is studied. It is observed that as the value of Hartman number increases, then the system enters into a stable phase. However, on increasing the Rayleigh number system becomes chaotic. Also, it is observed that by controlling the Rayleigh number chaos cannot be controlled but only on increasing the applied field the chaotic state in nanofluid convection can be controlled, which indicates towards a kind of magnetic cooling. It is concluded that as temperature varies the nanofluid convection exhibit chaotic motion which can be stabilized by applying magnetic field which has many applications in drug delivery, nano technology, environmental engineering, industrial engineering and in pharmaceutical industry.

Fractal and variability analysis of simulations in ozone level due to oxides of nitrogen and sulphur

Air pollution refers to the release of pollutants into the air. These pollutants are detrimental to human the planet as a whole. Apart from causing respiratory infections and pulmonary disorders, rising levels of Nitrogen Dioxide is worsening ozone pollution. Formation of Ground-level ozone involves nitrogen oxides and volatile gases in the sunlight. Volatile gases are emitted from vehicles primarily. Ozone is harmful gas and its exposure can trigger serious health effects as it damages lung tissues. In order to decrease the level of ozone, level of oxides leading to ozone formation has to be dealt with. This paper deals with the simulations in ozone due to oxides of nitrogen and sulphur. The data from Central Pollution Control Board shows positive correlation for ozone with oxides of sulphur and nitrogen for RK Puram, Delhi in India where high concentration of ozone has been found. The correlation between ozone and sulphur, nitrogen oxides is moderate during summer while weak during winters. Ozone with n...