Water consumption and different parameters

Water consumption and different parameters

Creation of Input Scenarios

To cover all possible scenarios, four scenarios are created based on the categories of factors mentioned in details in previous section of the thesis that are (table 5.1): Table 5.1: Scenarios adopted for WCP analysis Scenarios Parameters Data Sets Variation Scenario One Socio-Economic Parameters Household size, number of female and male, monthly income, the four categories of household age, the four categories of education level and car habits. vary according to each house Scenario Two Physical Characteristics of Housing Units Total area of the house, building area of the house, garden area, number of rooms and frequency of garden watering. Scenario Three Indoor Habits Frequency of washing dish per day, washing clothes (laundry) per week, using toilet per day and shower for male and female per week. Scenario Four Climatic Factors mean precipitation and mean temperature constant for all houses The three first scenarios are human related wile the climatic factors are site dependants and does not vary with houses, and by consequence only the first 3 categories are used for the statistical analysis and numerical techniques in this thesis.

.Correlation Analysis and Matrix

Correlation analysis is conducted to see if there is any predictive relationship between water consumption and the three scenarios and the inter-relationship between all parameters.

Scenario1

To evaluate the correlation, the G.DE Landsheere, 1979 (Belhassen et al., 2016) scale is used (table 5.2). The relationship between socio-economic parameters and per capita total water usage is demonstrated in table 5.3, table 5.4 and figures 5.1. Water consumption is very strongly correlated with monthly income. Also, it’s strongly correlated with number of females, household size, two categories of education level HGS and UNIV. it has medium correlation with the two age categories AG1 and AG3, primary school and car numbers. Furthermore, WCP has very week correlation between number of males, the two age categories AG2 and AG4, medium school in education level and the frequency of washing cars. In addition, results of table demonstrate an intercorrelation between variables. For example, a strong relationship between number of females and monthly income, between university education level and household size, etc.