forecast and prediction analysis of solid waste …€¦ · number 780028 18130 1865 320 275 12 200...

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International Journal of Civil Engineering and Technology (IJCIET)

Volume 9, Issue 6, June 2018, pp. 738–749, Article ID: IJCIET_09_06_084

Available online at http://www.iaeme.com/ijciet/issues.asp?JType=IJCIET&VType=9&IType=6

ISSN Print: 0976-6308 and ISSN Online: 0976-6316

© IAEME Publication Scopus Indexed

FORECAST AND PREDICTION ANALYSIS OF

SOLID WASTE GENERATION RATES USING

STATISTICAL MODELS IN SALEM CITY

J. Sankar

Research Scholar, Department of Civil Engineering,

Karpagam Academy of Higher Education, Coimbatore, Tamilnadu, India

Dr. N. Balasundaram

Professor, Department of Civil Engineering,

Karpagam Academy of Higher Education, Coimbatore, Tamilnadu, India

D. Roopa

Assistant Professor, Department of Civil Engineering,

Gnanamani College of Engineering, Namakkal, Tamilnadu, India

ABSTRACT

The prediction of municipal solid waste generation plays a vital role in solid waste

management. Achieving the anticipated prediction accuracy with regard to the

generation trends is facing many challenges, in addition to population growth and

urbanization of the rural areas of the district. A proper method of solid wastes

generation (SWG) rate required for solid waste management (either for a short-term

or a long-term say for five years or a long-term period of say twenty years) for any

town or city is not readily available. Hence short-term planning, SWG rates forecast

using statistical model has been proposed in this study considering the population and

solid wastes data of case study area of Salem City Municipal corporation of Tamil

Nadu.

Key words: environment, municipal solid waste, population growth, SWG rates,

urbanization.

Cite this Article: J. Sankar, Dr. N. Balasundaram, D. Roopa, Forecast and Prediction

Analysis of Solid Waste Generation Rates using Statistical Models in Salem City,

International Journal of Civil Engineering and Technology, 9(6), 2018, pp. 738–749.

http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=9&IType=6

1. INTRODUCTION

Urbanization continues to make solid waste management a significant public health and

environmental concern in urban areas of many developing states in India. In most of the

developing nations, there is a lack of both human and financial resources, which results in

poor planning of solid waste management and operation. The lack of research and

J. Sankar, Dr. N. Balasundaram, D. Roopa


development activities in developing states that lead to an improper selection of technology

concerning the physical and climatic conditions. In general, the solid waste management is

given low priority due to inadequate funding from the recognised authority.

The study area Salem city municipal corporation is located southwest of the state capital,

Chennai, in 110N 78.16°E at an average elevation of 278 m (912 ft). Salem is the sixth largest

city in Tamil Nadu by population, and it has a population of 8,26,267 as per 2011 census. The

solid waste generated per day is 400tonnes approximately. The study area has an extent of

91.34 sq km which has been divided into four zones namely Ammapet (zone 1) Hasthampatty

(zone 2) Suramangalam (zone 3) and Kondalampatty (zone 4) in which it has 60 divisions or

wards.

2. DATA OF SOLID WASTE MANAGEMENT IN SALEM CITY

A detailed field survey was conducted on solid waste management in Salem Municipal

Corporation to assesses the quality and quantity from (2011 -12 to 2015 – 16) and has been

collected and used in this study and are given in table 1.

The total population of the city is obtained by interpolation method of using census data

of the town for the year 2001 and 2011. The solid wastes for the city and zone wise are

obtained from SCMC.

Table 1 Population and Quantity of Waste Generation of SCMC from 2011 to 2016

year

Zone-1 Zone-2 Zone-3 Zone-4

Total of all the

zones

Popul

ation

waste

generate

d

(tonnes/

day)

Populat

ion

waste

generate

d

(tonnes/

day)

Populat

ion

waste

generate

d

(tonnes/

day)

Populat

ion

waste

generate

d

(tonnes/

day)

Populat

ion

waste

generate

d

(tonnes/

day)

2011-

12

20022

9 88.01 181374 82.01 223982 105.42 151287 65.07 756872 340.51

2012-

13

21516

7 101.12 184139 92.54 228013 112.65 152709 70.24 780028 376.55

2013-

14

21990

0 112.14 186594 99.72 231472 121.31 153012 74.97 790978 408.14

2014-

15

22463

2 121.3 189050 104.41 234995 129.88 154235 78.65 802912 434.24

2015-

16

22743

4 134.18 191354 109.07 236596 139.32 156005 82.06 811389 464.63

Figure 1 Represents the quantity of solid waste generation the SCMC

0

100

200

300

400

500

2011-12 2012-13 2013-14 2014-15 2015-16

Qu

anti

ty o

f so

lid w

aste

ge

ner

atio

n (

ton

s/d

ay)

Year

Forecast and Prediction Analysis of Solid Waste Generation Rates using Statistical Models in

Salem City


3. SOLID WASTE GENERATION RATES

Using the data of population and solid waste generated of SCMC for the years 2011 to 2016,

the waste generation rates have been calculated for the period 2011 to 2016 and represented in

table 2.

Table 2 Population and Quantity of Waste Generation of SCMC from 2011 to 2016

Year

ZONE-1 ZONE-2 ZONE-3 ZONE-4

AMMAPET HASTHAMPATHY SURAMANGLAM KONDALAMPATTY

DATA WG

R DATA

WG

R DATA

WG

R DATA

WG

R

Populati

on

WG

(Ton

s/

day)

(kg/

capit

a/

day)

Populati

on

W

G

(To

ns/

day

)

(kg/

capit

a/

day)

Populati

on

WG

(Ton

s/

day)

(kg/

capit

a/

day)

Populati

on

W

G

(To

ns/

day

)

(kg/

capit

a/day

)

2011-12 200229 88.0

1 0.44 181374

82.

01 0.44 223982

105.4

2 0.47 151287

65.

07 0.43

2012-13 215167 101.

12 0.47 184139

92.

54 0.46 228013

112.6

5 0.49 152709

70.

24 0.46

2013-14 219900 112.

14 0.51 186594

99.

72 0.5 231472

121.3

1 0.52 153012

74.

97 0.49

2014-15 224632 121.

3 0.54 189050

104

.41 0.54 234995

129.8

8 0.55 154235

78.

65 0.51

2015-16 227434 134.

18 0.59 191354

109

.07 0.56 236596

139.3

2 0.59 156005

82.

06 0.53

Average value of SWG rate 0.55 Average value

of SWG rate 0.53

Average value of

SWG rate 0.52

Average value

of SWG rate 0.49

The solid waste does not only comprise residential waste but also waste from different

other sources which are listed below in the table 3 along with waste generation rate.

Table 3 Different Components of Solid Waste Generation of SCMC from 2011 to 2016

Year Data/ Source Residen

tial

Commer

cial Street

Hot

els

Institutio

ns

Mark

ets

Me

at

Sta

lls

Cine

ma

Hall

s

Functi

on

Halls

Total

Quantity of

solid waste

(tons /day)

2011-

12

Number 756872 18125 1861 312 271 11 150 25 49

432.03

Quantity of

solid waste

(tons /day)

340.51 13 36.72 10.6 5.42 18.81 1.5 0.16 5.31

WGR

(kg/item) 0.45 0.7 19.85 33.9 19.92 1710

10.

1 6.4 108.16

2012-

13

Number 780028 18130 1865 320 275 12 200 29 54

472.95

Quantity of

solid waste

(tons /day)

376.55 14 37.05 10.8

8 5.49 20.4

2.2

9 0.19 6.1

WGR

(kg/item) 0.48 0.71 19.87 34 19.96 1787

11.

45 6.55 112.96

2013-

14

Number 790978 18146 1875 350 279 12 220 29 60

508.96

Quantity of

solid waste

(tons /day)

408.14 14.5 37.39 13.5

1 5.64 20.4

2.6

7 0.19 6.52

WGR

(kg/item) 0.52 0.79 19.94 38.6 20.02 1963

12.

14 6.55 108.66



2014-

15

Number 802912 18151 1900 378 283 12 300 30 63

539.58

Quantity of

solid waste

(tons /day)

434.24 14.99 38.17 15.0

1 5.85 20.4

3.7

8 0.2 6.94

WGR

(kg/item) 0.54 0.82 20.09 39.7 20.71 1995

12.

6 6.66 110.15

2015-

16

Number 811389 18155 1987 405 287 12 352 31 65

579.95

Quantity of

solid waste

(tons /day)

464.63 15 40.16 18.0

8 6.01 24.01

4.5

5 0.21 7.3

WGR

(kg/item) 0.57 0.83 20.21

44.6

4 20.94

2000.

83

12.

92 6.77 112.3

4. FORECAST OF SOLID WASTE GENERATION FROM 2016 TO 2021

USING STATISTICAL MODELS

The solid waste generation (SWG) rates required for solid waste management in the study

area of Salem City Municipal corporation (SCMC) have been forecast for the period of next

five years say 2017 to 2021(five years period is considered as short-term planning period)

using statistical models such as,

"Double Exponential Smoothing" (DES) mathematical model.

―Double Moving Average‖ (DMA) statistical model.

4.1. Double Exponential Smoothing (DES) Statistical Model

Exponential smoothing is a simple thumb rule method for smoothing time series data

adopting the exponential window function. Whereas in the simple moving average method the

past measurements are weighted equally, exponential functions are adopted to assign

exponentially decreasing weights over time.

The double exponential smoothing model which is to project N years where N is five

years in the forecasting solid waste management parameters using the previous data for less

than or equal to N years of the same parameters.

The double exponential smoothing is the process which means change over time, which is

an accepted statistical time series modelling as explained below.

The model considered in this study is

Yt= u1+u2t+εt

Where = u1 and u2 are the parameters and εt is a random component with mean zero and

variance‗‘, t is the period.

We can estimate the parameters with respect to time using the above model as given

below.

E(Yt) = u1+u2 T

This can be seen with the simplification procedure.

If simple exponential smoothing were applied to the observations from the linear process,

we could obtain at the end of the period T.

ST = αYt + (1-α)ST-1

The forecasting value for the period ‗T+h' made at the end of the period is

( ) ( )


Salem City


(

)

(

) (

)

In this work, solid waste generation (SWG) rates for the existing data of 2011 to 2016 and

also forecast from 2017 to 2021 for all the zones (1 to 4) have been discussed, as illustrated

below:

Solid waste generation rates of zones 1 to 6.

Solid waste generation rates for the period 2011 to 2016

Forecasting solid waste generation rates for the period 2017 to 2021.

The population and waste generation rates for all the zones of SCMC have been calculated

and same has been illustrated in table 4 and shown in the fig 2.

Table 4 Forecast Residential Waste Generation from 2016 to 2021 of SCMC.

Year

ZONE-1 ZONE-2 ZONE-3 ZONE-4

AMMAPET HASTHAMPATHY SURAMANGLAM KONDALAMPATTY

DATA WG

R DATA

WG

R DATA

WG

R DATA

WG

R

Popul

ation

WG

(Ton

s/

day)

(kg/

capi

ta/

day)

Populat

ion

WG

(Ton

s/

day)

(kg/

capi

ta/

day)

Populat

ion

WG

(Ton

s

/day)

(kg/

capi

ta

/day

)

Populat

ion

WG

(Ton

s

/day)

(kg/

capi

ta/

day)

2016

-17

23110

9

140.

97 0.61 193604

112.

29 0.58 240446

149.

08 0.62 156005

93.9

1

0.60

2

2017

-18

23254

6

152.

45 0.65 195044

117.

03 0.6 241391

159.

32 0.66 157805

97.9

9

0.62

1

2018

-19

23364

6

161.

22 0.69 196902

126.

02 0.64 242471

169.

73 0.7 158455

101.

73

0.64

2

2019

-20

23499

6

169.

20 0.72 199242

133.

49 0.67 244571

178.

54 0.73 160555

106.

61

0.66

4

2020

-21

23519

6

178.

75 0.76 200276

140.

19 0.7 245141

188.

76 0.77 161419

110.

57

0.68

5

The quantity of solid waste generation from 2016 to 2021

Solid waste generation (SWG)rates of the case study city(SCMC) as a whole (but not zonal

wise) is illustrated in the table 5 by using Double exponential smoothing (DES) method and

same been shown in the fig 2.

Table 5 Forecast Waste Generation Quantity for the Years 2016 To 2021

Year t (years) Solid waste

xt(tons/day) txt t2 S

T S

T(2)

2016-17 1 432.53 432.53 1 110.36 -213.5

2017-18 2 473.2 946.4 4 146.64 -177.5

2018-19 3 508.98 1526.94 9 182.87 -141.5

2019-20 4 539.41 2157.64 16 218.52 105.47

2020-21 5 579.53 2897.65 25 254.62 -69.46

15 2533.65 7961.16 55



Table 6 Forecast Waste Generation Quantity

Year Forecast waste to be generated

(tons/day)

2016-17 618.92

2017-18 656.19

2018-19 700.9

2019-20 739.24

2020-21 780.25

Figure 2 Represents the Quantity of Forecast Solid Waste Generation in the SCMC from 2016 to 2021.

Based on the data of solid waste generated in SCMC (vide table 2) during the period 2011

to 2016, forecast solid waste generation rates for different components have been calculated

using ―Double Exponential Smoothing‖ model and presented in table 7.

Table 7 Forecast Solid Waste Generation Rates of Different Components of Solid Waste in SCMC

from 2016 To 2021

Year Data/ Source Residen

tial

Commer

cial Street

Hot

els

Institutio

ns

Mark

ets

Meat

Stalls

Cine

ma

Halls

Functi

on

Halls

Total

Quantity

of solid

waste

(tons

/day)

2016-

17

Number 821164 18186 2000 420 300 13 398 33 70

618.92

Quantity of

solid waste

(tons /day)

496.25 15.28 40.84 19.0

5 6.76 26.89 5.55 0.31 7.99

WGR

(kg/item) 0.6 0.84 20.42

45.3

6 20.03 2068 13 7.08 114.14

2017-

18

Number 826786 18200 2100 437 305 13 437 39 74

656.19

Quantity of

solid waste

(tons /day)

526.78 15.65 43.37 21.4

5 7.51 26.89 5.78 0.23 8.53

WGR

(kg/item) 0.64 0.86 20.65

49.0

8 25.75 2068 13.2 5.9 115.27

2018-

19

Number 831474 18250 2240 500 298 14 493 49 79

700.9

Quantity of

solid waste

(tons /day)

558.69 15.88 46.79 26.2

2 8.07 29.56 6.07 0.44 9.23

WGR

(kg/item) 0.67 0.87 20.89

52.4

5 29.21 2111 13.6 9.07 116.84

0

100

200

300

400

500

600

700

800

900

2016-17 2017-18 2018-19 2019-20 2020-21

quan

tity

oo

f so

lid

was

te

gen

erat

ion t

ons/

day

years


Salem City


2019-

20

Number 839364 18300 2300 516 305 14 578 57 82

739.24

Quantity of

solid waste

(tons /day)

587.83 16.29 48.62 27.6

9 10.32 29.56 8.63 0.52 9.78

WGR

(kg/item) 0.7 0.89 21.14

53.6

8 33.86 2111 14.9 9.08 119.27

2020-

21

Number 842032 18350 2400 525 309 15 661 60 89

780.25

Quantity of

solid waste

(tons /day)

618.27 16.69 51.34 28.7

8 11.75 32.23 9.89 0.58 10.72

WGR

(kg/item) 0.73 0.91 21.39

54.1

8 38.03 2149 15 9.67 120.45

From table 2 and 4 average SWG rates for the city considered as a whole for the years

2011 to 2016 and also forecast SWG rates obtained by "Double Exponential Smoothing"

statistical model for the years 2016 to 2021 are given below.

Table 7 Annual Average Solid Waste Generation Rates (Kg/Capita/Day) From 2011 To 2021

Year Yearly average SWG rate

(Kg/capita/day) Year

Annual average SWG rate

(Kg/capita/day)

2011-12 0.45 2016-17 0.60

2012-13 0.48 2017-18 0.64

2013-14 0.52 2018-19 0.67

2014-15 0.54 2019-20 0.70

2015-16 0.57 2020-21 0.73

Average 0.51 Average 0.67

Considering the observations mentioned above, Percentage increase solid waste

generation rates can be calculated as given in table 8.

Table 8 Percentage increase solid waste generation

Years Yearly average

SWG rate of Forecast years

Forecast yearly

average SWG rate

Percentage Increase

of SWG rate for data

2011-12 0.45 2016-17 0.60 33.33

2012-13 0.48 2017-18 0.64 33.33

2013-14 0.52 2018-19 0.67 28.85

2014-15 0.54 2019-20 0.70 29.63

2015-16 0.57 2020-21 0.73 28.07

Total 2.56 - 3.34 153.21

Average 2.56/5 =0.51 - 3.34/5=0.66 153.21/5 =30.64

% increase

of average

SWG

100(0.66 – 0.51) / 0.51 = 29

30.64

Based on the observations given in the above table, it can be concluded that the %

increase of average SWG rate of the case study yearly data is 29 to 31. It can also be found

that population is more than, SWG rates which is higher than city's population. The

percentage increase of average solid waste generation rates for the components of solid waste

during the period 2011 to 2016 and also for the forecast period of 2016 to 2021 in the case

study city (SCMC) are given below as a tabular form.



Table 9 Percentage Increase in Average SWG Rate for Different Components from 2011 To 2016 and

2016 To 2021.

Component of the

solid waste

The yearly average of the

SWG rate forperiod 2007

to2011

The yearly average of the

forecast SWG rate for the

period2012 to 2016

Percentage increase in the

average SWG rate for the

periods 2011 to 2016and2016

to 2021

Residential 0.51 0.54 5.88

Commercial 0.77 0.87 12.98

Street Sweeping 19.99 20.76 3.85

Hotels 38.18 50.95 33.45

Institutions 20.31 29.37 44.6

Markets 1891.16 2101.69 11.13

Meat Stalls 11.84 13.94 17.73

Cinema Halls 6.56 8.72 32.92

Function Halls 110.44 117.81 6.67

Based on the above observations from the above table the conclusions about the

percentage increase of SWG rate as follows:

The percentage increase of SWG rate in the residential area is 0.51 to 0.54, due to high SWG

rate from 2011 to 2016.

The percentage increase in SWG rate in the commercial establishment is 12.98. This increase

is due to expected commercial activity increase as the city population and industrial activity in

the case study increases and is seen in table 2 and 4

The percentage increase of SWG rate of hotels is 33.35 due to increase in population and

development in the hotel business.

The percentage increase of SWG rate in institutions is 44.6 indicating the development of the

education system.

As the percentage increase of SWG markets increases, the trend of the SWG rate also

increases. As the population grows there is a demand for the markets also to expand.

The meat stalls also increase as there is an increase in the population and dependency on meat.

The percentage of cinema halls is 32.92 as population increases.There is a need for the

increase in places of the entertainment for the people.

The percentage of increase in the function halls is also due to the increase in population

increase, and there is a need for the rise in it.

4.2. Double moving average method for SCMC from 2016 to 2021

As per established statistically times series methods, the method which gives "least mean

square error" is considered as reliable forecasting method for solid waste management in the

case study city (SCMC) for the period 2016 to 2021. Double exponential smoothing has been

proposed because of its least mean square error is less. ―Double moving average‖ DMA

statistical model has been considered in this study.

The moving average method approach may be used to forecast the future observations

from a time series which has a linear trend. Suppose time series model is

Yt = Yt= u1+u2t+εt

Where u1 and u2 are unknown parameters andtis an uncorrelated error component with

mean ‗0‘ and variance‗‘ and ―t ― is a time period.


Salem City


At time t, the N- period simple moving average is

MT = YT + YT-1+ ………+YT-N+1

N

The expected values of MT, assuming that observations came from the linear trend process is

E(MT) = E (YT ) – (N-1) (u2)

2

M is an unbiased estimation. The expected value of the Simple moving average M T lays

behind the level of the process at time T, T E(Y ) ,by an amount equals to 2 (N-1)/2(b ) .

Consider a moving average of the moving averages called a double moving average,

MT (2)

= MT + MT+1+ ……..+ MT+N+1

N

This is reference relation between double moving average and simple moving average,

It has to be solved that

E [ MT (2)

] = E(YT) – (N-1)/2(b2)

By solving these equations we get the values of u1 and u2

u1= 2E(MT) – E [ MT (2)

] - u2T

u2 = 2/N-1[E(MT) – E [ MT (2)

]]

The future forecast of h period is

Y(T)

T+H = 2M(T) - MT(2)

+ h [ 2(N-1)( MT - MT (2)

)]

Table 10 Forecast Waste Generation Quantity of SCMC during 2016-17

year

the quantity

of waste

generation

Moving(MT)

double

moving

(MT2)

years

the quantity

of waste

generation

2011-12 340.51 - - 2016-17 470.35

2012-13 376.55 358.53 - 2017-18 478.27

2013-14 408.14 392.07 375.28 2018-19 485.43

2014-15 434.24 421.19 406.63 2019-20 492.59

2015-16 464.63 449.43 435.31 2020-21 499.75

Figure 5 Represents the quantity of forecast solid waste generation in the SCMC from 2016 to 2021.

450

460

470

480

490

500

510

2016-17 2017-18 2018-19 2019-20 2020-21

Qu

anti

ty o

f so

lid w

aste

ge

ner

atio

n (

ton

s/d

ay)

years



4.3. Comparison of forecast waste generation of statistical models

The forecast quantity of waste generation is done by using Double exponential smoothing and

double moving average method and both the methods are compared where the percentage

increase in quantity of waste generation is 36.34 for the period 2011-16 and for 2016-21 it

was found to be 24.58 in DES model and 6.25 in DMA model. A detail comparison has

shown in table 11.

Table 11 Comparative Differences of Statistical Models

SCMC data of Solid waste

generation of SCMC

The forecast quantity of solid waste generation

Double Exponential Smoothing model Double Moving

Average model

Year

quantity of waste

generation(tons/da

y)

Year quantity of waste

generation(tons/day)

quantity of waste

generation(tons/day)

2011-12 340.51 2016-17 496.25 470.35

2012-13 376.55 2017-18 526.78 478.27

2013-14 408.14 2018-19 558.69 485.43

2014-15 434.24 2019-20 587.83 492.59

2015-16 464.63 2020-21 618.27 499.75

Difference

quantity of

waste

generation for

five years

period

124.12

Difference

quantity of waste

generation for

five years period

122.02 29.4

% Increase of

waste quantity

generation for

five years

period

36.45

% Increase of

waste quantity

generation for

five years period

24.58 6.25

% Increase for

five years 115.74

% Increase for

five years 38.42 8.71

5. CONCLUSIONS

Solid waste generation rates have been successfully calculated using the previous year

generation rate and expenditure made towards to the generation for successful solid waste

management.

For the forecast of solid waste management, two statistical models have been used namely

Double exponential method (DES) and Double moving average method (DMA) and

differences between two ways have been found out.

Solid waste generation rates have been calculated for all the SCMC.It is observed that zone 3

has high waste generation rates due to the high population in that zone.

Based on the observation it can be concluded that the % of increase of solid waste generation

rate in the city is increased from 29 to 31.

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