Drinking Water Tank Level Analysis with ARIMA Models: A Case Study

Abstract

The operational management of tanks for urban water distribution networks is usually a critical element due to the dynamic nature of the water demand and the age of the distribution networks themselves. Today, in a context of water resource scarcity, optimal management is a key point for the sustainable management of urban systems. For this purpose, it is useful to implement predictive tools, able to provide short-term forecasts to inform urban water managers on the most suitable procedure to be applied in the case of routine or critical events. A possible approach is to use autoregressive integrated moving average (ARIMA) models, which combine the autoregression and the moving average approaches, with the possibility to work on a differenced series of the data. They can further embed a seasonal- component (Seasonal ARIMA models), to account for possible periodic patterns in the observed data. In this study, the data of water levels measured from May 2018 to 10 January 2019 in a water storage tank in the area of Benevento, Campania region (Italy), were considered as a case study. The standard ARIMA techniques were applied to find the best model for this dataset, according to “Deviance Information Criterion” (DIC) and “Bayesian Information Criterion” (BIC) optimization. The results are discussed, shedding light on the behaviour of the time series with reference to the management of the infrastructure and the dataset. The residual analysis, carried out to check if the autocorrelation was still present and if the residuals were normally distributed, revealed a narrow distribution. Small values were found throughout the dataset, except for a few periods, corresponding to the imputed data. This application represents a preliminary step of more detailed research that will be carried out to detect the best model for forecasting tank levels for the case study to help to manage the urban water supply.