Biochemical oxygen demand prediction for Chaophraya river using alpha-trimmed ARIMA model

Water is the key factor for sustainable human life. In addition to having adequate water source, the quality of water is also important. Water that is safe for human must meet standard water quality; otherwise, it is useless even though there is plenty of water. Thus, water quality must be measured regarding its physical, chemical, and biological properties. The purpose of this study is to apply time series analysis to model and predict Biochemical Oxygen Demand (BOD) for water quality at four monitoring stations along Chaophraya River of Thailand. In this paper, we propose an α-trimmed ARIMA model which can be used to predict BOD value of the up-coming year using a collection of BOD data from the past. The main advantage of our proposed model is that it can be used with both seasonal and non-seasonal time series data. The model was evaluated on a set of BOD data that were collected during 1996-2013. The predicted BOD results are compared to the BOD results obtained from other three existing models and the results reveal that the relative errors of our proposed model are less than half of the relative errors of those three existing models.