Heterogeneity in Domestic Water Demand: An Application of Multilevel Analysis to the City of Valencia (Spain)

This paper deals with the question of unobservable heterogeneity and problems of scale in urban water demand. For this purpose, the determinants of domestic water consumption and the elasticities were estimated using a hierarchical model. For our empirical analysis, a household level data panel from Valencia (Spain) between 2009 and 2011 was available. Households were assigned to the city neighbourhoods to which they belong, which allowed us to incorporate the intra-urban scale into the analysis. In the estimate, the average price paid by each household in each bimonthly period was used due to the current tariff structure in Valencia. Regarding our results, there were differences in the consumption between the different neighbourhoods that were not independent of the average price paid by households. We found that 27% of the variability in consumption was explained by differences in household behaviour. In addition, an average price-elasticity in Valencia for all periods of −1.868 was obtained as well as a range of elasticities for the different neighbourhoods between (−1.53 and −1.21). From the results obtained, it is possible to extract relevant information for local water managers in order to apply economic instruments, prices and taxes to urban water demand.

The use of band similarity in urban water demand forecasting as a new method

Water consumptions and demands by persons vary from time to time and from location to location depending on countless factors, notably, population, socio-economic and climatic variables. Today, studies which create models on water consumption of persons, using numerous methods including artificial neural networks and regression models in this regard and ensure that projections are made are ongoing. In this study; parameters affecting water consumption were examined within the scope of the study area, and the parameter reduction was realized with the help of the Factor Analysis. Then, as a new method, the Band Similarity method was used together with the Artificial Bee Colony optimization algorithm, and urban water demand models were produced and the temporal dependence of the relevant variables was examined. As a result of the study, it was seen that the Band Similarity method improved the results obtained with the optimization algorithm and helped to understand the temporal dependencies of the variables. The fact that the Band Similarity method, which was put forward for the first time in its field, worked successfully and produced results, can be said to be the main contribution of this study to the knowledge.

Managing Scarce water resources for socially acceptable solutions, through hydrological and econometric modeling

Covering increasing water demand for competitive uses with limited resources is becoming one of the most challenging water management issues. The effects are more evident in arid areas, where conflicts are more likely to occur. Such an example is Urumqi County, China; Urumqi River is the main water supply source, and in order to balance the upstream agricultural water demand and the downstream urban water demand, the government imposed fallow measures. The region is traditionally a rural area with high production expectations, however, urban water demand is continuously increasing over the last decades, following the population and urbanization trends. Irrigation needs are covered from the river, during the summer period, creating seasonal demand peaks. The fallow measures aim to sustain agriculture and the government defines which farmers will fallow each year. This study uses a questionnaire survey to examine the farmers’ willingness to continue fallow, and the fallow period preference; both examined for the first time so far. The driving factors are used as variables to analyze and describe the preferences through regression models. A non-negligible portion of farmers highly depend on agriculture and want to cultivate. The feasibility of satisfying their needs through better water management is examined through a coupled WEAP (Water Evaluation And Planning) model. Combining econometric and hydrological tools is a novel element. The results are encouraging, with significant insights on the current water management policy, the potential of diversified fallow systems, and the achievement of sustainable and socially acceptable planning.

Short-term water demand predictions coupling an Artificial Neural Network model and a Genetic Algorithm

The application of artificial neural network (ANN) models for short-term (15 min) urban water demand predictions is evaluated. Optimization of the ANN model's hyperparameters with a Genetic Algorithm (GA) and use of a growing window approach for training the model are also evaluated. The results are compared to those of commonly used time series models, namely the Autoregressive Integrated Moving Average (ARIMA) model and a pattern-based model. The evaluations are based on data sets from two Canadian cities, providing 15 minute water consumption records over respectively 5 years and 23 months, with a respective mean water demand of 14,560 and 887 m3/d. The GA optimized ANN model performed better than the other models, with Nash-Sutcliffe Efficiencies of 0.91 and 0.83, and Relative Root Mean Square Errors of 6 and 16% for City 1 and City 2, respectively. The results of this study indicate that the optimization of the hyperparameters of an ANN model can lead to better 15 min urban water demand predictions, which are useful for many real time control applications, such as dynamic pressure control.