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Although it is known that air pollution may lead to increased asthma prevalence, no clear scientific evidence of direct association between air pollution and asthma rate has been reported. In the present study, a Geographical Information System (GIS) approach was developed to determine the association between asthma hospital discharge rate (ADR) and seasonal exposure to specific ambient air pollutants in eastern Texas, USA, during the period 2009 to 2011. Quarterly asthma data were obtained from Texas State Department of Health, National Asthma Survey surveillance of Texas State, USA. Quarterly mean concentrations of fine particular matter (PM 2.5 ), nitrogen dioxide (NO 2 ), and ozone (O 3 ) were determined from the corresponding measured daily data collected by various air quality monitoring stations distributed in different counties in the study area. Using Pearson correlation analysis, quarterly average of air pollutant concentrations was compared to quarterly Asthma discharge rate (ADR). The results revealed that the association between quarterly exposure of air pollution and ADR was not statistically significant in the study area. During the study period, a negative correlation coefficient was observed between the quarterly mean concentration of ozone and NO 2 with the quarterly ADR. However, in most of the cases a positive correlation coefficient was observed between the quarterly mean concentration of PM 2.5 and the quarterly ADR, indicating a probable association between ambient air pollution exposure and asthma prevalence.

In the Development stage, information about the environment should be accessed. This data is pre-processed with the help of Principal Component Analysis (PCA) to reduce the irrelevant attributes. After that MLA’s such as Decision Tree (DT), Random Forest (RF), KNN, Linear regression (LR) & multilayer Neural Network model (MLP-ANN) models are utilized in testing datasets to predict the wind energy. The Power which is determined necessities to check and separate from the first ability to refresh the framework until and except if the necessary dependability is assembled from learning. In the assessment stage, the prepared expectation framework is then utilized to anticipate the Power for the test samples. In this research four statistical performance indicators & training time used to identify the best-fitted model. In the analysis section, it is seen that PCA Based DT outperformed the others algorithm by means of MAE, MSE, RMSE, Regression & Training time. Keyword : Wind speed prediction, Machine Learning techniques, Regression Analysis, Performance metrics