Pg 1 1 Executive Summary The main purpose of this report is to learn how to use different methods to estimate the future population of a determined services area. Analyze the accuracy and precision of each method to determine which method represents more accurately the data given. The methods to be employed to predict the population are: linear, quadratic, exponential and logistic growth regressions. We concluded based on several parameters: sum of the mean squared errors and from both F and t tests; that the best model to represent this population is the logistic model (d=0) predicting a population of 42,116 people by the end of 2034. We are told that the capita daily consumption creases constantly by 2% annually, hence we developed an equation predict the per capita water consumption by the end of 2034: 126.11 gpcd. This gives as a design parameter of 3.54 MGD that the water must produce to keep up with the future demand and avoid an expensive expansion, construction of another facility or from buying water from other water plants. Theory Water demand forecasting is a tool used by utility managers to predict the amount of water the service area by a particular water plant would need. This water demand must account for domestic, industrial, commercial, public, leakage and wastage use. In this assignment we will only worry about the water demand pertaining the domestic area, the housing area. To determine the amount of water needed we need to use several databases to collected from several sources. One of those sources is from the department of transportation (DOT), the DOT has divided areas into traffic analysis zones (TAZ) which is a group of census block that has at least one major main road going through it or touching the zone boundary, the data is collected on population or housing and employment for the traffic demand model. Housing and population data is collect per TAZ, using the most recent data available from the DOT or other sources such as the regional building permit data and population estimates from the Municipalities Offices of Budget. Several methods area employed to forecast water demand, but we will do a per capita water demand forecast only. A per capita water demand forecast assumes each individual uses the same average amount of water annually (q) and to that average annual amount of
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Executive Summary
The main purpose of this report is to learn how to use different methods to estimate
the future population of a determined services area. Analyze the accuracy and precision of
each method to determine which method represents more accurately the data given. The
methods to be employed to predict the population are: linear, quadratic, exponential and
logistic growth regressions.
We concluded based on several parameters: sum of the mean squared errors and from
both F and t tests; that the best model to represent this population is the logistic model (d=0)
predicting a population of 42,116 people by the end of 2034.
We are told that the capita daily consumption creases constantly by 2% annually,
hence we developed an equation predict the per capita water consumption by the end of 2034:
126.11 gpcd. This gives as a design parameter of 3.54 MGD that the water must produce to
keep up with the future demand and avoid an expensive expansion, construction of another
facility or from buying water from other water plants.
Theory
Water demand forecasting is a tool used by utility managers to predict the amount of
water the service area by a particular water plant would need. This water demand must
account for domestic, industrial, commercial, public, leakage and wastage use. In this
assignment we will only worry about the water demand pertaining the domestic area, the
housing area.
To determine the amount of water needed we need to use several databases to
collected from several sources. One of those sources is from the department of transportation
(DOT), the DOT has divided areas into traffic analysis zones (TAZ) which is a group of
census block that has at least one major main road going through it or touching the zone
boundary, the data is collected on population or housing and employment for the traffic
demand model.
Housing and population data is collect per TAZ, using the most recent data available
from the DOT or other sources such as the regional building permit data and population
estimates from the Municipalities Offices of Budget.
Several methods area employed to forecast water demand, but we will do a per capita
water demand forecast only. A per capita water demand forecast assumes each individual
uses the same average amount of water annually (q) and to that average annual amount of
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water consume we will have to adjust for future water demand, in our case we will simply
assume there is a 2% increase in annual water demand. That average amount of water
consume per capita is simply multiplies by the total population of the area (N) to be serviced
to give us the total system water demand (Q):
𝑄! = 𝑁! · 𝑞! where t represents the calendar year
In other to calculate the population in the year 2034 we will use several modeling
methods: simple linear regression, quadratic regression, exponential and logistic growth
regressions.
In a simple linear equation model we assume population, the dependent variable, is
only dependent on a single parameter, the year which is the independent variable (Soon 2004
p. 335). This model is represented by the following equation:
P(t)=a+m·t
In the above equation P represents the population at year t, and a and m are constant
to be determined by the statistical program, excel, that will be determined given the data
collected over a period of time.
Another arithmetic linear regression that can be applied is explain in Reynolds and
Richards book in which we normalize the values by using the natural log for the dependent
variable, in some cases where the population vs. time graph expresses a s-curve this method
tends to give a better function to represent them. It yields a function with the following