The demand of water is the amount required for a given purpose, for example liter per person per day, or mm per crop. ... The demand of water is made up authorized consumption by domestic and non-domestic consumers and water losses.

1. Q.1 Explain different types of water demand?
Answer- Water Demand is the measureof the total amount of water used by the
customers within the water system. Thereare several things that can influence the
amount of water demanded of your system. Oneof the most important jobs of a water
systemis to continually meet this demand without interruption, rain or shine.
i) Total annual volume (V) in liters or million liters.
ii) Annual average rate of draft in liters per day, i.e V/365
VARIOUS TYPES OF WATER DEMAND:-
i) Domestic water demand
ii) Industrial and commercial water demand
iii) Demand for public uses
iv) Fire demand
v) Water required compensating losses in wastes and thefts.
DOMESTIC WATER DEMAND:- This includes the water required in private buildings for
drinking, cooking, bathing, lawn sprinkling, Gardening, sanitary purposes etc. This
amount varies according to the living conditions of the consumers on an average this
domestic consumption under normal conditions in a Indian city is expected to be
around 135 litres /day/person as per Id:1172,1971. The total domestic consumption
generally amounts to 50-60% of the total water consumption.
INDUSTRIAL AND COMMERCIAL WATER DEMAND:- This includes the quantity of
water required to be supplied to offices, Factories, different industries, hospitals,
hostels, etc. This will vary considerably with the nature of the city and with the number
and types of industries and commercial establishment there is no direct relation of this
consumption with the population and hence the actual requirements for all industries

2. should be estimated. The water requirements for buildings other than residences as
per is standards are as follows.
DEMAND FOR PUBLIC USES:- This includes the quantity of water required for public
parks, gardening, washing and sprinkling on roads, usein public fountains etc.
PURPOSE WATER CONSUMPTION
FIREDEMAND:- Firedemand is the amount of water considered necessary to controla
major developing fire in a specific building. The minimum needed fire flow for any
single building is 500 gpm for 2 hours and the maximum needed fire flow is 12000 gpm
for 4 hours.

3. WATER REQUIRED COMPENSATING LOSSES IN WASTES AND THEFTS:-All the water
which goes in the distribution pipe does not reach the consumers. Somewater is
wasted in the pipe line due to leakage, defective pipe joints, faulty valves and fittings.
In somecases, quantity of water is lost due to unauthorized and illegal connections.
While estimating the total quantity of water some allowances for these losses and
wastages should be done. While estimating the total quantity of water of a town;
allowance of 15% of total quantity of water is made to compensate for losses, thefts
and wastageof water.
1. Losses dueto unauthorised and illegal connections.
2. Losses dueto, continuous wastageof water.
3. Losses dueto defective pipe joints, cracked and broken pipes, faulty valves and
fittings.
4. Loses and wastes.
PER CAPITA DEMAND:- ------------------ litres/day P x365
 If ‘Q’ is the total quantity of water required by various purposes by a town per year
and ‘p’ is population of town, then per capita demand will be Q Per capita demand.
Q.2 Enlist different methods used for population forecast. Explain
any one in detail?
Answer- Forecasting is theprocess of making predictions of the future based on past
and presentdata and most commonly by analysis of trends. A commonplaceexample
might be estimation of somevariable of interest at some specified future
date. Prediction is a similar, but moregeneral term. Both might refer to formal
statistical methods employing time series, cross-sectionalor longitudinal data, or
alternatively to less formaljudgmental methods. Usage can differ between areas of
application: for example, in hydrology theterms "forecast" and "forecasting" are

4. sometimes reserved for estimates of values at certain specific future times, while the
term "prediction" is used for more general estimates, such as the number of times
floods will occur over a long period.
CATEGORIES OF FORECASTING METHODS:-
 Qualitative vs. quantitative methods.
 Average approach.
 Naïve approach.
 Drift method.
 Seasonal naïve approach.
 Time series methods.
 Causal / econometric forecasting methods.
 Judgmental methods.
 Artificial intelligence methods.
TIME SERIES METHODS:-
A time series is a series of data points indexed (or listed or graphed) in time order.
Most commonly, a time series is a sequence taken at successiveequally spaced points
in time. Time series are very frequently plotted via line charts. Time series are used
in statistics, signal processing, pattern recognition, econometrics, mathematical
finance, weather forecasting, earthquakeprediction, electroencephalography, control
engineering, astronomy, communications engineering, and largely in any domain of
applied science and engineering which involves temporal measurements.
METHODS FOR ANALYSIS:-
DOMAIN BASED METHOD:-
1. Frequency Domain:- In physics, electronics, controlsystems engineering,
and statistics, the frequency domain refers to the analysis of mathematical
functions or signals with respect to frequency, rather than time. Put simply,
a time-domain graph shows how a signalchanges over time, whereas a
frequency-domain graph shows how much of the signal lies within each given

5. frequency band over a rangeof frequencies. A frequency-domain representation
can also include information on the phaseshift that must be applied to
each sinusoid in order to be able to recombine the frequency components to
recover the original time signal.
A given function or signal can be converted between the time and frequency
domains with a pair of mathematical operators called transforms. An example is
the Fourier transform, which converts a time function into a sum or integral
of sine waves of different frequencies, each of which represents a frequency
component. The "spectrum" of frequency components is the frequency-domain
representation of the signal. The inverseFourier transform converts the
frequency-domain function back to the time function. A spectrum analyzer is a
tool commonly used to visualize electronic signals in the frequency domain.
2. TIME DOMAIN METHOD:-Timedomain refers to the analysis of mathematical
functions, physicalsignals or time series of economic or environmentaldata,
with respect to time. In the time domain, the signal or function's value is known
for all real numbers, for the case of continuous time, or at various separate
instants in the case of discretetime. An oscilloscopeis a tool commonly used to
visualizereal-world signals in the time domain. A time-domain graph shows how
a signal changes with time, whereas a frequency-domain graph shows how
much of the signal lies within each given frequency band over a range of
frequencies.
Autocorrelation, also known as serialcorrelation, is the correlation of
a signal with a delayed copy of itself as a function of delay. Informally, itis the
similarity between observations as a function of the time lag between them. The
analysis of autocorrelation is a mathematical tool for finding repeating patterns,
such as the presence of a periodic signal obscured by noise, or identifying
the missing fundamentalfrequency in a signalimplied by
its harmonic frequencies. Itis often used in signal processing for analyzing
functions or series of values, such as time domain signals.
Cross correlation is a measurement that tracks the movements of two variables
or sets of data relative to each other. In its simplest version, it can be described
in terms of an independent variable, X, and two dependent variables, Y and Z. If
independent variableX influences variable Y and the two are positively
correlated, then as the value of X rises so will the value of Y. If the same is true
of the relationship between X and Z, then as the value of X rises, so will the value
of Z. Variables Y and Z can be said to be cross correlated because their behaviour
is positively correlated as a resultof each of their individual relationships to
variable X. Cross correlation can also occur with sets and time series of data.

6. PARAMETER BASED METHODS:-
1. The Paramedic Method:- Thebasic idea is that there is a set of fixed parameters
that determine a probability model. Parametric methods are often those for
which we know that the population is approximately normal, or we can
approximate using a normal distribution after we invoke the central limit
theorem.
2. The Non Paramedic Method:- Nonparametric statistics is the branch
of statistics that is not based solely on parameterized families of probability
distributions (common examples of parameters are the mean and variance).
Nonparametric statistics is based on either being distribution-freeor having a
specified distribution but with the distribution's parameters unspecified.
Nonparametric statistics includes both descriptive statistics and statistical
inference.