Modelling Inters Seasonal Variability Impact on Water Demand in a Smart City

Urbanisation development has evolved many modern challenges that demand continuous Smart City Configuration (SCC). This study explores online data mining with a multistage prospective design to model how climatic variables and the demography factor combined influence water demand in developing a water resources utilisation model for a smart city. The multiple logistic regression (MLR) models were used to analyse/model the water demand for the study area of Mthatha in the Eastern Cape Province in South Africa. The water demand is modelled as a function of the climatic variables and demographic factors. Various seasonal indices were used to assess the inter and intra-seasonal annual variability impacts on the available water. The results show a greater variation between 0.45 and 0.67 for demography explanatory variables while abrupt changes in rainfall trends and patterns show a coefficient of variation of 0.75 as a major factor required in smart city design. The result of the MLR water demand model predictor shows demography explanatory variables to be of greater influence than inter-seasonal rainfall fluctuation on the city water usage. Hydrologists and urban managers would find usefulness in the study towards decision-making in designing risk mitigation against drought for a smart city.