A processor implemented method for infrastructure monitoring, comprising: collecting infrastructure data as input, via one or more hardware processors; extracting values of a plurality of parameters by processing the input data, via the one or more hardware processors; identifying a correlation between the plurality of parameters, via the one or more hardware processors, the method being characterized in that it further comprises: for each pair of parameters from among the plurality of parameters: determining a value of a direct correlation of a first parameter and a second parameter in the pair of parameters, wherein the direct correlation between the first parameter and the second parameter indicates that the second parameter is affected for change in the first parameter; determining a value of a chained correlation between the first parameter and the second parameter in the pair of parameters, wherein the chained correlation is determined by considering an indirect relation between the parameters; determining one of the direct correlation and the chained correlation as a dominant correlation, by comparing the determined values of the direct correlation and the chained correlation, and by taking the correlation having highest value between the direct correlation and the chained correlation as the dominant correlation; and filling a correlation matrix using the value of the dominant correlation, wherein the correlation matrix captures information on the correlation between each pair of parameters in terms of the dominant correlation, wherein the information on the correlation captured in the correlation matrix is used to select a pair of parameters which are used to train a machine learning model for generating predictions with respect to values of the parameters. collecting infrastructure data as input, via one or more hardware processors; extracting values of a plurality of parameters by processing the input data, via the one or more hardware processors; identifying a correlation between the plurality of parameters, via the one or more hardware processors, the method being characterized in that it further comprises: for each pair of parameters from among the plurality of parameters: determining a value of a direct correlation of a first parameter and a second parameter in the pair of parameters, wherein the direct correlation between the first parameter and the second parameter indicates that the second parameter is affected for change in the first parameter; determining a value of a chained correlation between the first parameter and the second parameter in the pair of parameters, wherein the chained correlation is determined by considering an indirect relation between the parameters; determining one of the direct correlation and the chained correlation as a dominant correlation, by comparing the determined values of the direct correlation and the chained correlation, and by taking the correlation having highest value between the direct correlation and the chained correlation as the dominant correlation; and filling a correlation matrix using the value of the dominant correlation, wherein the correlation matrix captures information on the correlation between each pair of parameters in terms of the dominant correlation, wherein the information on the correlation captured in the correlation matrix is used to select a pair of parameters which are used to train a machine learning model for generating predictions with respect to values of the parameters. determining a value of a direct correlation of a first parameter and a second parameter in the pair of parameters, wherein the direct correlation between the first parameter and the second parameter indicates that the second parameter is affected for change in the first parameter; determining a value of a chained correlation between the first parameter and the second parameter in the pair of parameters, wherein the chained correlation is determined by considering an indirect relation between the parameters; determining one of the direct correlation and the chained correlation as a dominant correlation, by comparing the determined values of the direct correlation and the chained correlation, and by taking the correlation having highest value between the direct correlation and the chained correlation as the dominant correlation; and filling a correlation matrix using the value of the dominant correlation, wherein the correlation matrix captures information on the correlation between each pair of parameters in terms of the dominant correlation, wherein the information on the correlation captured in the correlation matrix is used to select a pair of parameters which are used to train a machine learning model for generating predictions with respect to values of the parameters.