The document discusses water demand forecasting and population forecasting methods. It describes calculating total annual water volume, average daily flow rates, and per capita demand. Population forecasting methods covered include arithmetic increase, geometric increase, incremental increase, and graphical methods. Factors affecting per capita demand and reasons for selecting a design period are also outlined.

1. WATER DEMAND
AND
POPULATION
FORECASTING
SHIVANGI SOMVANSHI
ASSISTANT PROFESSOR
AMITY UNIVERSITY

2. WATER DEMAND
 While planning a water supply scheme, it is
necessary to find out not only the total yearly
water demand but also to assess the required
average rates of flow and the variations in
these rates. The following quantities are therefore
generally assessed and recorded.
 1. Total annual volume in Litres or million litres.
(1MLD = 106L/d)
 Annual Average rate of flow in litres per day , i.e.
V/365
 Annual average rate of flow in litre per day per
person (l/c/d), called per capita demand.

3. CLASSES OF WATER DEMAND

4. CLASSES OF WATER DEMAND

5. WATER REQUIREMENT FOR
DIFFERENT USES

6. PER CAPITA DEMAND
 It is the annual average amount of daily water
required by one person and includes the
domestic use, industrial and commercial uses,
public use, wastes etc. It may therefore be
expressed as:
Per capita demand (q) in l/d/h or l/c/d =
Total yearly water requirement of the city in
litres (V) / 365 X Design population

7. FACTORS AFFECTING PER
CAPITA DEMAND
 Size of the city
 Climatic conditions
 Types of habitat of people
 Industrial and commercial activities
 Quality of water supplies
 Pressure in the distribution system
 Development of sewage facilities

8. DESIGN PERIOD
Design period may be defined as:
“It is the number of years in future for which the
given facility is available to meet the demand.”
Or
“The number of years in future for
which supply will be more than demand.”

9. Why Design period is provided ?
Design period is provided because
 It is very difficult or impossible to provide
frequent extension.
 It is cheaper to provide a single large unit
rather to construct a number of small units.

10. POPULATION
FORECASTING
Design of water supply and sanitation scheme is based on the projected
population of a particular city, estimated for the design period. Any
underestimated value will make system inadequate for the purpose
intended; similarly overestimated value will make it costly. Changes in
the population of the city over the years occur, and the system should
be designed taking into account of the population at the end of the
design period. Factors affecting changes in population are:
 increase due to births
 decrease due to deaths
 increase/ decrease due to migration
 increase due to annexation.
The present and past population record for the city can be obtained
from the census population records. After collecting these population
figures, the population at the end of design period is predicted using
various methods as suitable for that city considering the growth pattern
followed by the city.

11. POPULATION
FORECASTING
 Arithmetic Increase method
 Geometric Increase Method
 Incremental Increase Method
 Decrease Rate of Increase Method
 Simple Graphical Method
 Comparitive Graphical Method

12. ARITHMETIC INCREASE
METHOD
 This method is suitable for large and old city with
considerable development. If it is used for small,
average or comparatively new cities, it will give lower
population estimate than actual value. In this method
the average increase in population per decade is
calculated from the past census reports. This increase
is added to the present population to find out the
population of the next decade. Thus, it is assumed that
the population is increasing at constant rate.
 Hence, dP/dt = C i.e., rate of change of population with
respect to time is constant.

14. GEOMETRIC INCREASE METHOD

16. INCREMENTAL INCREASE
METHOD

18. GRAPHICAL METHOD
 In this method, the populations of last few decades are correctly
plotted to a suitable scale on graph. The population curve is smoothly
extended for getting future population. This extension should be done
carefully and it requires proper experience and judgment. The best
way of applying this method is to extend the curve by comparing with
population curve of some other similar cities having the similar
growth condition.

19. COMPARATIVE GRAPHICAL METHOD

21. 21
EXTRAPOLATION
TECHNIQUES
 Real Estate Analysts - faced with a difficult task
 long-term projections for small areas such as
 Counties
 Cities and/or
 Neighborhoods
 Reliable short-term projections for small areas
 Reliable long-term projections for regions countries
 Forecasting task complicated by:
 Reliable, Timely and Consistent information

22. 22
SOURCES OF FORECASTS
 Public and Private Sector Forecasts
 Forecasts may be based on large quantities
of current and historical data

23. 23
PROJECTIONS ARE
IMPORTANT
 Comprehensive plans for the future
 Community General Plans for
 Residential Land Uses
 Commercial Land Uses
 Related Land Uses
 Transportation Systems
 Sewage Systems
 Schools

24. 24
PROJECTIONS VS.
FORECASTS
 The distinction between projections and
forecasts are important because:
 Analysts often use projections when they
should be using forecasts.
 Projections are mislabeled as forecasts
 Analysts prepare projections that they know will
be accepted as forecasts without evaluating the
assumptions implicit in their analytic results.

25. 25
PROCEDURE
 Using Aggregate data from the past to project the
future.
 Data Aggregated in two ways:
 total populations or employment without identifying the
subcomponents of local populations or the economy
 I.e.: age or occupational makeup
 deals only with aggregate trends from the past without
attempting to account for the underlying demographic
and economic processes that caused the trends.
 Less appealing than the cohort-component techniques or
economic analysis techniques that consider the
underlying components of change.

26. 26
WHY USE AGGREGATE DATA?
 Easier to obtain and analyze
 Conserves time and costs
 Disaggregated population or employment data
often is unavailable for small areas

27. 27
EXTRAPOLATION: A TWO STAGE PROCESS
 Curve Fitting -
 Analyzes past data to identify overall trends of
growth or decline
 Curve Extrapolation -
 Extends the identified trend to project the future

28. 28
ASSUMPTIONS AND
CONVENTIONS
 Graphic conventions Assume:
 Independent variable: x axis
 Dependent variable: y axis
 This suggests that population change (y axis)
is dependent on (caused by) the passage of
time!
 Is this true or false?

29. 29
Assumptions and Conventions
 Population change reflects the change in aggregate
of three factors:
 births
 deaths
 migration
 These factors are time related and are caused by
other time related factors:
 health levels
 economic conditions
 Time is a proxy that reflects the net effect of a large
number of unmeasured events.

30. 30
Caveats
 The extrapolation technique should never be used to
blindly assume that past trends of growth or decline
will continue into the future.
 Past trends observed, not because they will always
continue, but because they generally provide the best
available information about the future.
 Must carefully analyze:
 Determine whether past trends can be expected to
continue, or
 If continuation seems unlikely, alternatives must be
considered

31. 31
Alternative Extrapolation
Curves
 Linear
 Geometric
 Parabolic
 Modified Exponential
 Gompertz
 Logistic

32. 32
Linear Curve
 Formula: Yc = a + bx
 a = constant or intercept
 b = slope
 Substituting values of x yields Yc
 Conventions of the formula:
 curve increases without limit if the b value > 0
 curve is flat if the b value = 0
 curve decreases without limit if the b value < 0

33. 33
Linear Curve

34. 34
Geometric Curve
 Formula: Yc = abx
 a = constant (intercept)
 b = 1 plus growth rate (slope)
 Difference between linear and geometric curves:
 Linear: constant incremental growth
 Geometric: constant growth rate
 Conventions of the formula:
 if b value > 1 curve increases without limit
 b value = 1, then the curve is equal to a
 if b value < 1 curve approaches 0 as x increases

35. 35
Geometric Curve

36. 36
Parabolic Curve
 Formula: Yc = a + bx + cx2
 a = constant (intercept)
 b = equal to the slope
 c = when positive: curve is concave upward
when = 0, curve is linear
when negative, curve is concave downward
growth increments increase or decrease as the x variable
increases
 Caution should be exercised when using for long
range projections.
 Assumes growth or decline has no limits

37. 37
Parabolic Curve

38. 38
Modified Exponential Curve
 Formula: Yc = c + abx
 c = Upper limit
 b = ratio of successive growth
 a = constant
 This curve recognizes that growth will
approach a limit
 Most municipal areas have defined areas
 i.e.: boundaries of cities or counties

39. 39
Modified Exponential Curve

40. 40
Gompertz Curve
 Formula: Log Yc = log c + log a(bx)
 c = Upper limit
 b = ratio of successive growth
 a = constant
 Very similar to the Modified Exponential Curve
 Curve describes:
 initially quite slow growth
 increases for a period, then
 growth tapers off
 very similar to neighborhood and/or city growth
patterns over the long term

41. 41
Gompertz Curve

42. 42
Logistic Curve
 Formula: Yc = 1 / Yc-1 where Yc-1 = c + abX
 c = Upper limit
 b = ratio of successive growth
 a = constant
 Identical to the Modified Exponential and Gompertz
curves, except:
 observed values of the modified exponential curve and the
logarithms of observed values of the Gompertz curve are
replaced by the reciprocals of the observed values.
 Result: the ratio of successive growth increments of the
reciprocals of the Yc values are equal to a constant
 Appeal: Same as the Gompertz Curve

43. 43
Logistic Curve