Urban Water Consumption Prediction Based on CPMBNIP

The prediction of urban water consumption is of great significance for urban planning and management, addressing water demand conflicts among various industries in a city and balancing supply and demand. The prediction of future data by data-driven models is largely based on the assumption of data consistency. However, large-scale human migration, the rapid development of economic activity, climate change and other factors affect the consistency of urban water consumption data, thus creating challenges for traditional data-driven models. In response, a combined prediction model based on the new information priority theory (CPMBNIP) is proposed to predict urban water consumption in a changing environment. To represent the linear and nonlinear characteristics of the urban water consumption system, the autoregressive moving average (ARMA) model and gray model (GM(1,1)) are selected as basic models. Based on the prediction results of the basic models, an optimization model with the corresponding weights of the two basic models as the decision variables is constructed. The optimization model is solved using the nondominated sorting genetic algorithm II (NSGA II) to obtain the set of weight combinations. Based on the principle of new information priority, the final weight combination is selected from the weight combination set according to the criterion of the best fit with the verification set. The final weight combination is incorporated into the two basic models to obtain CPMBNIP and predict the data for the test set. Based on urban water consumption sequence data from six lower-tier cities in southern China from 1965 to 2004, the urban water consumption of these six cities from 2005 to 2013 is predicted by CPMBNIP. Additionally, CPMBNIP is compared with two basic models (ARMA and GM(1,1)) and a single-objective combined prediction model (SOCPM). The percentage errors of CPMBNIP for the six cities' test sets are 4.54%, 3.88%, 6.14%, 4.34%, 3.01% and 3.43%. The prediction effect of CPMBNIP for the test set is better than that of the other models. The results show that CPMBNIP yields the best prediction performance. In addition, compared with the other models, CPMBNIP can better use the information provided by the new data to improve the prediction of some nonstationary time series. This study provides support for urban water consumption prediction under changing environments.