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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue II, February 2026
Population Growth Forecasting and Clean Water Demand Analysis
for Denpasar City Toward 2030
I Gusti Bagus Wijaya Kusuma1*, I Gusti Ngurah Made Parama Widya2
1University of Udayana, Faculty of Engineering, Indonesia
2PT Ken Parama Ekawijaya, Jakarta, Indonesia
*Corresponding Author
DOI: https://doi.org/10.51583/IJLTEMAS.2026.15020000076
Received: 24 February 2026; Accepted: 02 March 2026; Published: 17 March 2026
ABSTRACT
Water is poised to become the most critical natural resource of the third millennium, influencing both economic
stability and human survival, particularly as developing nations face rapid population growth. This surge in
population and industrial activity is driving up water demand while simultaneously depleting available resources.
The resulting gap between high demand and dwindling supply poses significant challenges for water distribution.
This study outlines a methodology for forecasting population growth and water demand in the Denpasar area
over a ten-year period.
Three projection models—linear, quadratic, and exponential—were evaluated to identify the most accurate
solution. The findings indicate that the quadratic method provides the most reliable forecast for Denpasar's future
water needs. Current projections reveal that existing water infrastructure can only meet 41.63% of total demand.
Consequently, it is imperative for the Denpasar municipal government to implement more robust strategies to
address this substantial supply deficit
Keywords: Population trends, sustainability, freshwater reserves, demand, forecasting
INTRODUCTION
In the third millennium, water has emerged as a paramount natural resource. Its significance spans both economic
stability and the fundamental sustenance of human life. Projections suggest that within the next two generations,
developing nations will face acute water crises as the global population is expected to surge toward three billion.
In Indonesia, rapid demographic growth and expanding industrialization have driven a steady rise in clean water
demand. Paradoxically, this escalating need coincides with the depletion of existing water sources, including
both groundwater and surface water. These multifaceted challenges regarding water availability directly impact
current supply levels.
Rapid population growth presents a critical challenge for major urban centers, including Denpasar. An expanding
population necessitates a corresponding increase in clean water provision. A key indicator of success in meeting
these needs is the robustness of long-term planning systems. In this context, this study examines the operational
planning at the Regional Water Utility (RWU) Denpasar Water Treatment Plant, specifically focusing on the
production and distribution infrastructure required to meet the community's water demands over a ten-year
projection period.
Production
To serve its customer base across Denpasar, RWU Denpasar manages a total water resource capacity of 815
liters per second (lps), which is derived from the following sources:
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Tukad Ayung III Water Treatment Plant: 385 lps capacity.
Deep Wells (8 units): 180 lps capacity.
Water Purchase from RWU Badung: 150 lps.
Water Purchase from RWU Gianyar: 25 lps.
Water Purchase from PTTB: 75 lps.
Distribution System
To ensure a continuous 24-hour supply with consistent pressure, RWU Denpasar utilizes three strategically
located reservoirs across the service area. The transmission and distribution network spans 1,050,019 meters of
piping to deliver water from sources to consumers.
This infrastructure is categorized into main pipes and service pipes. The main pipes—comprising primary,
secondary, and tertiary lines—are responsible for transporting water throughout the distribution zones, while
service pipes manage the final delivery to individual customers. These pipes are integrated into a network system
designed to provide equitable and reliable water supply.
THEORETICAL FRAMEWORK
Curvilinear trend analysis—encompassing linear, quadratic, and exponential models—is employed to forecast
population growth and clean water requirements.
Linear Trend
To simplify the calculation of trend equations, a coded year (X) is utilized in place of the actual year (t). The
fundamental formula is X = t – ť, where ť represents the mean of the initial and final years of the study period.
The linear trend equation is defined as follows:
Yt = a + bx (1)
Variable Definitions:
Yt: The calculated trend value for a specific timeframe.
a: The intercept value of Yt when the time variable x=0,
representing the baseline value at t
b: The gradient or slope of the trend line, indicating the
rate of change per unit of time.
x: The coded time interval, defined as = t – ť
The Method of Least Squares
The Least Squares method is employed to determine the most accurate line of best fit for a given time series.
This mathematical approach (often associated with the Chi-square principle) functions by minimizing the sum
of the squared differences between the actual observed values (Y) and the predicted trend values (Yt). By
applying this principle, the Least Squares technique ensures that the value of Σ (Y-Yt) is kept at its absolute
minimum, resulting in the most precise estimation possible.
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With a =
n
Y
and b =
2
X
XY
(2)
where n represents the total number of data pairs within the observed set.
Quadratic Trend Analysis
The mathematical expression for the quadratic trend is defined as follows:
Yt = a + bx + cx2 (3)
where:
X = t – ť = defined as the temporal distance between the
specific year of interest and the
calculated average year.
The least squares approach produces the following results:
a =
2
2
4
224
XXn
XYXXY
(3.a)
b =
2
X
XY
(3.b)
c =
2
2
4
22
XXn
YXYXn
(3.c)
Exponential Trend Analysis
The general mathematical expression for the exponential trend model is defined as follows:
Yt = a e b (t-ť) (4)
Yt = a e b X
where e ≈ 2,71828
The coefficients of the equation, denoted as a and b, are determined using the following formulas:
Ln Yt = ln a + bX
Then: a = anti ln
n
lnY
(4.a)
b =
2
X
XlnY
(4.b)
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To determine the most accurate projection among the three methods previously discussed, the model that yields
the minimum sum of squared residuals or the smallest deviation value ∑(Y-Yt)2 will be selected.
METHODOLOGY
To further investigate the aforementioned issues, statistical methods are employed as analytical tools, given the
quantitative (numerical) nature of data regarding population figures and clean water requirements. These
methods facilitate the processing of numerical data, rigorous analysis, and the formulation of objective
conclusions.
A scientific approach consisting of the following stages is utilized to resolve the research problems statistically:
Problem Identification: Precisely defining and understanding the core issues, where quantitative
information proves most beneficial.
Fact-Finding: Gathering relevant and detailed facts that directly correspond to the subject of the study.
Primary Data Collection: Acquiring the most recent original data, as essential information is often
unavailable in existing secondary sources.
Data Classification: Categorizing data based on specific characteristics and organizing them into
systematic groups.
Data Presentation: Summarizing information through tables, diagrams, and descriptive measures,
following these analytical steps:
a. Analyzing population data collected from the field.
b. Evaluating clean water demand.
c. Comparing population trends with water production trends.
d. Strategic planning for raw water requirements.
e. Planning production equipment for intake units.
Given that this research focuses on long-term projections, time series analysis was selected as the primary
analytical framework. This method provides precise metrics for informed decision-making, future forecasting,
and strategic planning.
RESULTS
Population Forecasting
Table 1. Population of Denpasar City
Year
Population
2022
1,031,000
2023
1,053,000
2024
1,075,000
2025
1,097,000
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Projections for the Year of 2030
Linear Trend Analysis
Table 2. Calculation of the Linear Trend Equation
(t)
(X)
Population (Y)
XY
X2
2022
- 1,5
1,031,000
-557,136
2.25
2023
- 0,5
1,053,000
-186,636
0.25
2024
0,5
1,075,000
191,277,6
0.25
2025
1,5
1,097,000
585,345
2.25
∑X = 0
∑Y= 4,256,000
∑XY=110,000
∑X2= 5
then a =
n
Y
=
=1,064,000
b =
2
X
XY
=
= 22,000
Quadratic Trend Analysis
Table 3. Calculation of the Quadratic Trend Equation
(t)
(X)
Population (Y)
XY
X2
X2Y
X4
2022
-1,5
1,031,000
-1,546,500
2.25
2,319,750
5.0625
2023
-0,5
1,053,000
-526,500
0.25
263,250
0.0625
2024
0,5
1,075,000
537,500
0.25
268,750
0.0625
2025
1,5
1,097,000
1,645,500
2.25
2,468,250
5.0625
∑X
= 0
∑Y
= 4,256,000
∑XY
=110,000
∑X2
= 5
∑X 2Y
=5,320,000
∑X4
=10.25
then a =
2
2
4
224
XXn
XYXXY
=
= 1,064,000
b =
2
X
XY
=
= 22,000
c =
2
2
4
22
XXn
YXYXn
=
= 0
Consequently, the quadratic trend equation is formulated as follows:
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Yt = 1,064,000 + 22,000 X
Yt = 1,064,000 + 22,000 (t-ť)
for t = 2030
Y2030 = 1,064,000 + 22,000 (2030-2023.5)
Y2030 = 1,207,000
Exponential Trend Analysis
Quadratic Trend Analysis
Table 3. Calculation of the Quadratic Trend Equation
(t)
(X)
Population (Y)
XY
X2
X2Y
X4
2022
-1,5
1,031,000
-1,546,500
2.25
2,319,750
5.0625
2023
-0,5
1,053,000
-526,500
0.25
263,250
0.0625
2024
0,5
1,075,000
537,500
0.25
268,750
0.0625
2025
1,5
1,097,000
1,645,500
2.25
2,468,250
5.0625
∑X
= 0
∑Y
= 4,256,000
∑XY
=110,000
∑X2
= 5
∑X 2Y
=5,320,000
∑X4
=10.25
then a =
2
2
4
224
XXn
XYXXY
=
= 1,064,000
b =
2
X
XY
=
= 22,000
c =
2
2
4
22
XXn
YXYXn
=
= 0
Consequently, the quadratic trend equation is formulated as follows:
Yt = 1,064,000 + 22,000 X
Yt = 1,064,000 + 22,000 (t-ť)
for t = 2030
Y2030 = 1,064,000 + 22,000 (2030-2023.5)
Y2030 = 1,207,000
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Exponential Trend Analysis
Table 4. Estimating the Exponential Trend Model
(t)
(X)
Population (Y)
Ln Y
X ln Y
X2
2022
-1.5
1,031,000
13.8460398
-20.7690596
2.25
2023
-0.5
1,053,000
13.8671538
-6.9335769
0.25
2024
0.5
1,075,000
13.8878312
6.94391561
0.25
2025
1.5
1,097,000
13.9080897
20.86213461
2.25
∑X = 0
∑Y= 4,256,000
∑lnY=55.51
∑XlnY = 0.103
∑X2= 5
then a = anti ln
n
lnY
= anti ln
= 1,063,716
b =
2
X
XlnY
=
= 0.021
e ≈ 2.71828
Consequently, the exponential trend equation is formulated as follows:
Yt = 1,063,716 e bX
Yt = 1,063,716 e b(t-ť)
for t = 2030
Y2030 = 1,063,716 * 2.71828 0.021(2030-2023.5)
= 1,219,290
The Sum of Squared Residuals ∑ (Y- Yt)2 for Each Trend Model shown in Table 5.
Table 5. Calculated Values Across Different Trend Models
(t)
Linear Trend
Quadratic Trend
Exponential Trend
2022
0
0
72,250
2023
0
0
155,692
2024
0
0
3,152
2025
0
0
572,068
Sum
0
0
803,163
Based on the calculated values above, the model with the lowest value of ∑(Y-Yt)2 among all utilized trends was
selected. A minimum value of 0 was achieved through the quadratic trend, indicating it as the most accurate
model for this dataset. Consequently, the population projections for Denpasar will be estimated using the
quadratic trend approach, as detailed in Table 6.
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Table 6. Population Projection Results
Year
Linear Trend
Quadratic Trend
Exponential Trend
2026
1,119,000
1,119,000
1,121,053
2027
1,141,000
1,141,000
1,144,844
2028
1,163,000
1,163,000
1,169,140
2029
1,185,000
1,185,000
1,193,951
2030
1,207,000
1,207,000
1,219,290
2031
1,229,000
1,229,000
1,245,165
2032
1,251,000
1,251,000
1,271,590
2033
1,273,000
1,273,000
1,298,576
2034
1,295,000
1,295,000
1,326,134
2035
1,317,000
1,317,000
1,354,278
Clean Water Production Forecasting
Table 7. Water Production Levels in Denpasar City
Year
Volume (m3)
2022
29,839,429
2023
31,500,000
2024
32,800,000
2025
34,000,000
Linear Trend Analysis
Table 8. Estimating the Linear Trend Equation for Water Production
(t)
(X)
Volume (m3) (Y)
XY
X2
2022
-1.5
29,839,429
-44,759,143.5
2.25
2023
-0.5
31,500,000
-15,750,000
0.25
2024
0.5
32,800,000
16,400,000
0.25
2025
1.5
34,000,000
51,000,000
2.25
∑Y = 128,139,429
∑XY= 6,890,857
∑X2= 5
Then a =
n
Y
=
= 32,034,857.25
b =
2
X
XY
=
= 1,378,171.3
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Consequently, the resulting linear trend equation is expressed as follows:
Yt = 32,034,857.25+ 1,378,171.3 (X)
= 32,034,857.25+ 1,378,171.3 (t-ť)
for t = 2030
Yt = 32,034,857.25+ 1,378,171.3 (2030-2023.5)
= 40,992,970.7
Quadratic Trend Formulation
Table 9. Calculation of the Quadratic Trend Model Parameters
Year (t)
(X)
Volume (m3) (Y)
XY
X2
X4
X2Y
2022
-1.5
29,839,429
-44,759,143.5
2.25
5.0625
67,138,715
2023
-0.5
31,500,000
-15,750,000
0.25
0.0625
7,875,000
2024
0.5
32,800,000
16,400,000
0.25
0.0625
8,200,000
2025
1.5
34,000,000
51,000,000
2.25
5.0625
76,500,000
0
∑Y= 128,139,429
∑XY= 6,890,857
∑X2=5
∑X4=
10.25
∑X2Y=
159,713,715
therefore a =
2
24
224
XXn
XYXXY
=
= 32,178,785.69
b =
2
X
XY
= 1,378,171
c =
2
24
22
XXn
YXYXn
=115,143
Consequently, the quadratic trend equation is formulated as follows:
Yt = 32,178,785.69 + 1,378,171 (X) -115,143 (X)2
Yt = 32,178,785.69 + 1,378,171 (t-ť) -115,143 (t-ť)2
for t = 2030
Y2030 = 32,178,785.69 + 1,378,171 (2030-2023.5) -
115,143 (2030-2023.5)2
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= 36,272,105.4
Exponential Trend Analysis
Table 10. Calculations for the Exponential Trend Line Equation
Year (t)
(X)
Volume (m3) (Y)
Ln Y
X Ln Y
X2
2022
-1.5
29,839,429
17.21
-25.8170118
2.25
2023
-0.5
31,500,000
17.27
-8.63274905
0.25
2024
0.5
32,800,000
17.31
8.652969537
0.25
2025
1.5
34,000,000
17.34
26.01280662
2.25
0
∑Y= 128,139,429
∑ln Y
=69.12
∑X ln Y
=0.216
∑X 2 =5
therefore a = anti ln
n
lnY
= 31,997,309
b =
2
X
XlnY
= 0.043
e ≈ 2,72
Consequently, the exponential trend equation is formulated as follows:
Yt = 31,997,309e0.043(X)
= 31,997,309e0.043(t-ť)
for t = 2030
Y2030 = 31,997,309 * 2.720.043(2030-2023,5)
= 42,371,311.73
Following the same methodology used for population forecasting, the quadratic trend was selected for projecting
water demand based on the calculated values of ∑(Y-Yt)2.
Tabel 11. Calculated Values Across Different Trend Models
Year
Linear Trend
Quadratic Trend
Exponential Trend
(Y-Yt)2
(Y-Yt)2
(Y-Yt)2
2022
16,427,882,144
169,740,249
155,650,413,596,621
2023
23,786,399,367
1,527,681,001
116,973,363,666,758
2024
5,784,682,460
1,527,671,620
90,543,265,189,205
2025
10,427,309,842
169,769,434
69,146,251,209,925
Sum
(Y-Yt)2 = 56,426,273,812
(Y-Yt)2 = 3,394,862,303
(Y-Yt)2= 432,313,293,662,508
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Table 12. Predicted Output for Clean Water Production
DISCUSSION
Clean Water Production Relative to Residential Demand in Denpasar City
To determine the water requirements for the community, a standard urban consumption rate of (160
liter/capita/day) is utilized. The projected production and service levels are planned according to the
following parameters:
Domestic Service: Targeted to cover 80% of the total population, encompassing residential households
and public hydrants.
Non-domestic Service: Established at 23% of the domestic service volume, which includes government
and social institutions, industrial and commercial sectors, ports, and other facilities.
Water Loss: Accounted for at 20% of the combined domestic and non-domestic consumption.
Based on the aforementioned criteria, the following table illustrates the projected water requirements for
Denpasar City for the year of 2030.
Table 13. Clean Water Demand Projections for Denpasar City in 2030
Based on the data presented above, the total production requirement for the Denpasar Municipal Water Utility
(RWU) is estimated at 238,696.32 m³/day for the 2030 projection. In contrast, the projected clean water
production for the same year is calculated at 36,272,105.44 m³/year (approximately 99,375.63 m³/day).
Year
Linear Trend
Quadratic Trend
Exponential Trend
2026
35,480,286
34,904,569.44
42,315,422
2027
36,858,457
35,591,882.44
42,315,422
2028
38,236,628
36,048,909.44
42,315,422
2029
39,614,799
36,275,650.44
42,315,422
2030
40,992,971
36,272,105.44
42,315,422
2031
42,371,142
36,038,274.44
42,315,422
2032
43,749,313
35,574,157.44
42,315,422
2033
45,127,485
34,879,754.44
42,315,422
2034
46,505,656
33,955,065.44
42,315,422
2035
47,883,827
32,800,090.44
42,315,422
Description
Unit
Total water demand
Remarks
Total Population
Projection 1,207,000
capita
m3/h
193,120
Demand = 0,160 m3/h
Domestic Service
Requirements
m3/h
154,496
Domestic Service: Targeted to cover 80% of the
total population
Non-Domestic Service
Requirements
m3/h
44,417.6
Non-domestic Service: Established at 23% of
the domestic service volume
Estimated Water Loss
m3/h
39,782.7
Accounted for at 20% of the combined domestic
and non-domestic consumption
Aggregate Water
Demand
m3/h
238,696.32
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Consequently, the capacity of Denpasar’s water production to meet the total community demand is calculated as
follows:
Service Coverage Percentage =
= 41,63 %
This indicates that the city of Denpasar can only fulfill 41.63% of its aggregate clean water requirements. This
calculation highlights a significant gap between current water treatment capacity and future urban demand.
To address the escalating demand driven by population growth, shifting consumption patterns, and limited water
sources, the Denpasar Water Utility (RWU) is considering the development of new deep wells (boreholes)
alongside continued water purchases from neighboring utilities.
Currently, the primary water sources utilized by the Denpasar Municipal Water Utility to serve its customers
include:
The Ayung III Belusung Water Treatment Plant.
Deep boreholes (groundwater wells).
Water purchases from the Badung and Gianyar Regional Water Utilities, as well as PT. TB.
However, long-term reliance on deep wells poses a severe environmental risk. Extensive groundwater extraction
can lead to gradual land subsidence, which threatens the safety of the ecosystem and future human generations.
While these impacts may not be immediately apparent, they represent a significant legacy burden.
To mitigate this, dependency on external water purchases and groundwater can be reduced by constructing new
surface water treatment plants or optimizing the production capacity of existing facilities.
Table 14. Clean Water Production Projections
Year
Clean Water Production Forecast
m3/year
m3/s
l/s
2026
34,904,569.44
1.10681664
1,106.82
2027
35,591,882.44
1.12861119
1,128.61
2028
36,048,909.44
1.14310342
1,143.10
2029
36,275,650.44
1.15029333
1,150.29
2030
36,272,105.44
1.15018092
1,150.18
2031
36,038,274.44
1.14276619
1,142.77
2032
35,574,157.44
1.12804913
1,128.05
2033
34,879,754.44
1.10602976
1,106.03
2034
33,955,065.44
1.07670806
1,076.71
2035
32,800,090.44
1.04008404
1,040.08
By incorporating a multiplication factor ranging from 1.0 to 1.15, a value of 1.11 was selected as the initial
planning baseline.
This factor is intended to compensate for potential pipe leakages and operational water usage within the treatment
facilities. Consequently, the peak raw water requirements are presented in Table 15.
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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue II, February 2026
Table 15. Clean Water Demand Projections for Denpasar City
CONCLUSION
Based on the analysis presented above, the following conclusions can be drawn:
1. Demographic and Demand Modeling: Population growth and clean water requirements in Denpasar are
most accurately represented and projected using a quadratic trend equation.
2. Supply-Demand Deficit: Current projections indicate that Denpasar's Water Treatment Plants will only be
capable of fulfilling 41.63% of the total estimated clean water demand.
3. Strategic Resource Management: To meet future water requirements, the Regional Water Utility (RWU)
must prioritize the optimization of existing facilities and negotiate new raw water source agreements. These
measures are essential to reduce dependency on deep-well extraction and external water purchases.
4. Capacity Expansion: In response to rapid population growth in the Denpasar area, it is imperative to expand
production capacity to ensure the long-term fulfillment of public water needs.
REFERENCES
1. Parwita, I. G. L. M., Dharma, I. G. B. S., Yekti, M. I., Pariartha, I. P. G. S., & Tarigan, Z. J. H. (2024).
Sustainable clean water supply management in the South Bali, Indonesia. Water Conservation &
Management (WCM), 8(1), 10–19. doi.org
2. Suryatmaja, I. B., Kurniari, K., & Nada, I. M. (2021). Comparison of clean water demand and availability
in Denpasar City. Jurnal Ilmiah Kurva Teknik, 10(1), 45–52. doi.org
3. Wandari, A., & Junaidi, A. (2021). Perspectives on population growth and clean water supply in
Denpasar City. Proceedings of the International Conference on Industrial, Sustainable and Strategic
Technology (ICIST 2020), 156–162. Atlantis Press.
4. BPS-Statistics of Denpasar City. (2023). Denpasar City in Figures 2023. BPS Denpasar.
5. Ministry of Public Works and Housing. (2022). Regulation No. 27/PRT/M/2016 regarding the
optimization of drinking water supply systems (SPAM). Jakarta: PUPR.
6. Sari, A. N., & Rahmawati, D. (2021). Forecasting urban water demand: A comparative study of
projection methods. Journal of Water Resources Engineering, 12(2), 88–97.
7. Walpole, R. E., & Myers, R. H. (1995). Ilmu peluang dan statistik untuk insinyur dan ilmuwan (R. K.
Sembiring, Terj.; Ed. ke-4). ITB Press.
Year
Raw Water Requirement (l/s)
Peak Demand (l/s)
2026
61,470.4
68,232.14
2027
62,678.93
69,573.62
2028
63,887.47
70,915.09
2029
65,096
72,256.56
2030
66,304.53
73,598.03
2031
67,513.07
74,939.5
2032
68,721.6
76,280.98
2033
69,930.13
77,622.45
2034
71,138.67
78,963.92
2035
72,347.2
80,305.39
