Adapting some statistical methods to analyze TDS in drinking water

Abstract

In this study, numerous statistical models were used including the Box-Jenkins models with several stages to build and forecasting the best model in the analysis of time series. Modern methods in time series analysis including fuzzy logic and fuzzy sets, have appeared as the most important alternatives to classical statistical methods. They have a mechanical ability to find solutions because they do not require the availability of classical model conditions, which are difficult to achieve in most cases.
This paper aims to find the best method to analyze the behavior of pollution rates by studying Box-Jenkins and high order fuzzy time series methods. Then, an adaptation has conducted between the two methods as a proposed procedure on chemical examined data for total dissolved solids in drinking water for Baghdad city. The data are recorded from January 2004 to December 2018. These methods are compared in details through statistical criteria RMSE, MAE, MAPE.