In this paper, we have investigated the two-dimensional magnetohydrodynamic steady boundary layer flow of a viscous magnetomicropolar liquid via an extending area. The impact of heat sink/source and chemical reaction is considered. The governing equations are modeled in Cartesian coordinate system. Using the suitable similarity transformations, the partial differential equations system is changed into the nonlinear ordinary differential equations system. The resulting system of equations is solved via mathematical renowned software Mathematica. The impact of diverse parameters through microrotation, concentration, temperature and velocity is examined via graphs. The present study reveals that the velocity is rising function of Soret number, Richardson number and Grashof number. It is mentioned that the greater velocity is located in the case of Newtonian liquid in contrast with the micropolar liquid. In the absence of chemical reaction parameter, the velocity is more as compared with higher chemical reaction parameter. Radiation, Hartmann and chemical reaction parameters augment the temperature. Concentration is a reducing function of radiation, Hartmann and chemical reaction parameters.
Water quality analysis involves analysis of physio-chemical, biological and microbiological parameters that reflect the abiotic and biotic status of ecosystems. This assessment facilitates planning for the utilization, antipollution and conservation strategies for sustainable use of aquatic ecosystem. Many mathematical models are available for predicting water quality. They have complex structures and require detailed information about sources and receptors, which are difficult and noneconomical. Difficulties in applying mathematical models promote the application of alternative approaches for data-driven techniques for analysis of the results.The present study focuses on water quality predictions for the Gangapur Reservoir for a 30 days in advance scenario, using genetic programming (GP) and least square support vector machines (LS-SVMs). A data period of 11 years (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011) of Gangapur Reservoir temporal water quality was evaluated. The data were taken from a single sampling point representing climatological, hydrological and surface water quality measurements. One of the most important steps in application of data-driven technique is selection of significant input parameters. Genetic programming equations were used for selecting significant input parameters. These significant input parameters are used for 30 days advance predictions of faecal coliform. A performance analysis of GP and LS-SVM models was carried out with the help of coefficient of determination, root-mean-square error and correlation coefficient. In the absence of availability of data, a typical situation for Indian case studies, the model runs were conducted with the use of available parameters. The developed models, along with their performance indicators, also are discussed.
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