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URBAN WATER DEMAND FORECASTING
USING ARTIFICIAL NEURAL NETWORKS:
A CASE STUDY OF BANGKOK.
BY
VICTOR SHINDE
CONTENTS





Need for water demand forecasting
Description of ANN
Study area description
Results of the study
Conclusions
INTRODUCTION

Need for water demand forecasting
• Water is a finite resource.
• Expanding the capacity of a water distribution system.
• Improving the reliability of supply.
• Effecting demand management instruments.
• Procurement of investment.
DESCRIPTION OF ANN

Why use ANN?
• Accounts for non linearity between inputs and outputs
• Uses a universal function to convert inputs to output,
for all types of problems.
• More realistic forecasts.
DESCRIPTION OF ANN

A network which mimics the human brain.
Connection links each having some weight ‘w’
O
U
T
P
U
T
I
N
P
U
T
Input layer
Hidden Layer Output layer
x1
w1j
w2j
x2
j
w3j
x3
yj = f(x1w1j+x2w2j+x3w3j)
f = Transfer function
= (1/(1+e-t)
DESCRIPTION OF ANN

Network Training
I
N
P
U
T
Output
Desired Output
Compares
Cost function E =
Input layer
Hidden Layer Output layer
 (Y
o
 Yd ) 2
STUDY AREA DESCRIPTION

Main study area
Metropolitan Waterworks Authority (MWA) responsibility
area – Bangkok Metropolis, Nontaburi & Samut Prakarn

Secondary study areas
Hanoi (Vietnam) and Chiang Mai (Thailand)
STUDY AREA DESCRIPTION

MWA responsibility area (2007 statistics)
• Population : 7.86 Million
• Population served: 7.36 Million (93.6%)
• Average Daily production : 5.52 MCM
• Non Revenue Water : 30.32 %

Secondary study areas
Hanoi
Chiang Mai
0
Temperature ( C)
Maximum
Minimum
Rainfall (mm)
Population (Million)
GDP (USD)
32
14
1682
3.4
40 Billion
31
19
1081
1.66
3.3 Billion
OBJECTIVES OF THE STUDY

Overall objective
To develop ANN models to forecast the water demand for
MWA – Bangkok.

Specific objectives
●
To forecast the short term and long term water demand for MWA.
●
To identify the factors most crucial in determining the short term and
long term water demands for MWA.
●
To compare the factors influencing long term demand for Bangkok,
Hanoi and Chiang Mai
SCOPE OF THE STUDY


Demand
Short term (ST) demand
Long term (LT) demand
Data used
ST Demand
LT Demand
– Daily demand, 1,2 & 3 days lead
– Monthly demand, 1,2 & 6 months lead
– Historical demand (sales), Rainfall, RH, Mean Temp
– Historical demand (sales), Population, GPP, Household
connections, Education status, Rainfall, RH, Max Temp
For comparing the cities (Bangkok, Hanoi and Chiang Mai) – Same as LT Demand,
only production in lieu of sales & Mean Temp instead of Max Temp

Software used
– ANN NeuroSolutions
SCOPE OF THE STUDY

Methodology for both ST and LT demand models
Data Collection and Analysis
• Correlation Matrix
• Pruning &
Construction
Input Selection
Model Training & Testing for 1st set
Architectures
• MLP
• GFF
• RBF
Transfer functions
• Hyperbolic tan
• Sigmoid
Learning Rules
• Backward Descend
• Conjugate Gradient
Sensitivity analysis
Omission of least sensitive variables
Training & Testing for 2nd set
150 ST Models – 15 sets
88 LT Models – 6 sets
60 Comparison models
RESULTS

Short term demand, 1 day lead
Input Selection : Zhang et al. (2006), Msiza et al. (2007)
Water Sales Max Temp Min Temp
Water Sales
1
0.38
0.55
Max Temp
0.38
1
0.78
Min Temp
0.55
0.78
1
Rainfall
0.39
-0.06
0.42
RH
0.31
0.19
0.7
Evaporation
0.36
0.94
0.79
Mean Temp
0.52
0.91
0.96
Rainfall
0.39
-0.06
0.42
1
0.85
-0.16
0.27
RH
0.31
0.19
0.7
0.85
1
0.17
0.54
Evaporation Mean Temp
0.36
0.52
0.94
0.91
0.79
0.96
-0.16
0.27
0.17
0.54
1
0.88
0.88
1
Selected Variables: Mean Temperature, Rainfall and RH
RESULTS
Observed vs. Predicted Demand for Training Set
Demand (MCM)
3.9
3.7
3.5
Observed
3.3
Predicted
3.1
2.9
1
11
21
31
Exemplars
Observed vs. Predicted Demand for Testing Set
Demand (MCM)
4.1
3.9
3.7
Observed
3.5
Predicted
3.3
3.1
1
11
21
31
Exemplars
41
51
61
41
51
61
RESULTS
Model Architecture Hidden
Layers
ST-1A(1)
MLP
1
ST-1A(2)
MLP
1
ST-1A(3)
MLP
1
ST-1A(4)
MLP
1
ST-1A(5)
MLP
2
ST-1A(6)
MLP
2
ST-1A(7)
MLP
2
ST-1A(8)
MLP
2
ST-1A(9)
MLP
3
ST-1A(10)
MLP
3
ST-1A(11)
MLP
3
PE's
13
11
17
17
20 &15
21 &15
12&15
12&15
20,10&5
17,12 &8
17,12 &9
1 O  Di
AARE =  i
x100
N i 1 Oi
N
Transfer
function
tanh
Sigmoid
tanh
Sigmoid
tanh
Sigmoid
tanh
Sigmoid
tanh
tanh
Sigmoid
Learning AARE RMSE
Rule
% MCM
BD
1.19 0.054
BD
1.19 0.054
CG
1.18 0.052
CG
1.15 0.05
BD
1.15 0.051
BD
1.19 0.055
CG
1.19 0.054
CG
1.2 0.055
BD
1.12 0.05
CG
1.13 0.049
CG
1.15 0.052
1
RMSE = (
N
N
2
(
O

D
)
 i i )
i 1
(Threshold static)x = (n/N) x 100
0.50%
30.30
36.36
25.76
28.79
30.30
28.79
30.30
33.33
30.30
27.27
34.85
1
2
Threshold static
1%
2%
3%
4%
56.06 78.79 92.42 96.97
54.55 78.79 92.42 96.97
54.55 81.82 92.42 96.97
54.55 81.82 95.45 100
59.09 80.30 93.94 98.48
56.06 78.79 92.42 96.97
57.58 80.30 92.42 96.97
54.55 78.79 90.91 96.97
59.09 81.82 92.42 100
53.03 80.30 93.94 100
57.58 78.79 90.91 96.97
Zhang et al. (2008)
Adamowski (2008)
Ghiassi et al. (2007)
Jain et al. (2000)
5%
100
100
100
100
100
100
100
100
100
100
100
RESULTS
SA for ST-1A models
Observed vs. Predicted Demand for Testing Set
0.12
3.85
3.65
3.55
0.08
3.45
0.06
Observed
0.04
3.35
Predicted
0.02
3.25
Demand (MCM)
0.10
3.75
3.65
3.55
Observed
3.45
Predicted
3.35
3.25
11
21
31
41
Testing Exemplars
Model Architecture Hidden
Layers
ST-1B(12)
MLP
1
ST-1B(13)
MLP
1
ST-1B(14)
MLP
1
ST-1B(15)
MLP
1
ST-1B(16)
MLP
2
ST-1B(17)
MLP
2
ST-1B(18)
MLP
2
ST-1B(19)
MLP
2
51
61
PE's Transfer
function
18
tanh
16
Sigmoid
14
tanh
13
Sigmoid
12&8 tanh
12&8 Sigmoid
12&8 tanh
12&8 Sigmoid
RH
1
Rainfall
3.15
Mean
Temp
0.00
HWD
Standard
Deviation(MCM)
Demand
(MCM)
Observed vs. Predicted Demand for ST-1B(16)
3.15
1
11
21
31
41
51
61
Exemplars
Learning AARE RMSE
Rule
% MCM
BD
1.23 0.056
BD
1.17 0.055
CG
1.26 0.057
CG
1.2
0.054
BD
1.17 0.053
BD
1.22 0.056
CG
1.23 0.056
CG
1.21 0.056
0.50%
31.82
34.85
27.27
28.79
33.33
31.82
30.30
33.33
Input Variables: HWD, Mean Temp & Rainfall
1%
54.55
60.61
53.03
54.55
59.09
57.58
54.55
56.06
Threshold static
2%
3%
78.79 90.91
80.30 92.42
80.30 90.91
77.27 90.91
78.79 90.91
78.79 90.91
80.30 92.42
78.79 93.94
4%
96.97
96.97
96.97
96.97
96.97
96.97
96.97
96.97
5%
98.48
98.48
100
100
100
98.48
98.48
100
RESULTS
Input Variables: Only HWD
Model
Architecture Hidden
Layers
ST-1C(20)
MLP
1
ST-1C(21)
MLP
1
ST-1C(22)
MLP
1
ST-1C(23)
MLP
1
ST-1C(24)
MLP
2
ST-1C(25)
MLP
2
ST-1C(26)
MLP
2
ST-1C(27)
MLP
2
PE's Transfer
function
42
tanh
42
Sigmoid
40
tanh
42
Sigmoid
15&12 tanh
15&12 Sigmoid
15&12 tanh
15&12 Sigmoid
Learning AARE
Rule
%
BD
1.31
BD
1.27
CG
1.32
CG
1.34
BD
1.29
BD
1.26
CG
1.29
CG
1.28
RMSE
MCM
0.058
0.057
0.058
0.059
0.058
0.056
0.058
0.058
0.50%
23.96
29.17
21.88
26.04
28.13
25.00
27.08
29.17
1%
48.96
55.21
48.96
46.88
50.00
56.25
50.00
52.08
Threshold static
2%
3%
78.13 91.67
78.13 91.67
80.21 91.67
77.08 91.67
77.08 92.71
78.13 91.67
78.13 91.67
77.08 91.67
4%
96.88
96.88
96.88
96.88
96.88
97.92
96.88
96.88
5%
98.96
100
100
98.96
100
100
100
100
Input Variables: HWD -1, HWD -2.
Model
Architecture Hidden
Layers
ST-1D(29)
MLP
1
ST-1D(30)
MLP
1
ST-1D(31)
MLP
1
ST-1D(32)
MLP
1
ST-1D(33)
MLP
2
ST-1D(34)
MLP
2
ST-1D(35)
MLP
2
ST-1D(36)
MLP
2
PE's Transfer
function
24
tanh
28
Sigmoid
28
tanh
28
Sigmoid
15&11 tanh
16 &12 Sigmoid
16 &12 tanh
15&12 Sigmoid
Learning AARE RMSE
Rule
% MCM
BD
1.33
0.058
BD
1.24
0.056
CG
1.32
0.057
CG
1.35
0.059
BD
1.32
0.059
BD
1.26
0.057
CG
1.35
0.06
CG
1.36
0.06
0.50%
23.40
25.53
23.40
24.47
27.66
30.85
23.40
24.47
1%
46.81
52.13
47.87
46.81
50.00
54.26
47.87
47.87
Threshold static
2%
3%
76.60 91.49
77.66 89.36
77.66 91.49
76.60 92.55
75.53 93.62
77.66 89.36
77.66 91.49
76.60 91.49
4%
96.81
97.87
96.81
96.81
96.81
96.81
96.81
96.81
5%
98.94
100
100
98.94
100
100
98.94
98.94
RESULTS
Input Variables: HWD -1, HWD -2 & HWD -3.
Model Architecture Hidden
Layers
ST-1E(37)
MLP
1
ST-1E(38)
MLP
1
ST-1E(39)
MLP
1
ST-1E(40)
MLP
1
ST-1E(41)
MLP
2
ST-1E(42)
MLP
2
PE's Transfer
function
21
tanh
21
Sigmoid
19
tanh
21
Sigmoid
16 &12 tanh
16 &12 Sigmoid
Learning AARE RMSE
Threshold static
Rule
% MCM
0.50% 1%
2%
3%
4%
5%
BD
1.28 0.056 23.08 48.35 79.12 93.41 97.80 98.90
BD
1.24 0.055 27.47 56.04 75.82 90.11 97.80 98.90
CG
1.29 0.057 28.57 48.35 79.12 92.31 97.80 98.90
CG
1.26 0.055 25.27 48.35 81.32 93.41 97.80 100
BD
1.28 0.057 26.37 47.25 78.02 92.31 97.80 98.90
BD
1.22 0.055 29.67 53.85 79.12 90.11 96.70 100
Input Variables: HWD -1 through HWD -7
Model Architecture Hidden
Layers
ST-1F(43)
MLP
1
ST-1F(44)
MLP
1
ST-1F(45)
MLP
2
ST-1F(46)
MLP
2
PE's Transfer
function
10
tanh
9
Sigmoid
16&11 tanh
18&12 Sigmoid
Learning AARE RMSE
Threshold static
Rule
% MCM
0.50% 1%
2%
3%
4%
BD
1.27 0.056 23.26 50.00 79.07 93.02 97.67
BD
1.26 0.055 24.42 55.81 77.91 93.02 98.84
BD
1.31 0.057 19.77 47.67 76.74 91.86 97.67
BD
1.22 0.055 30.23 53.49 81.40 91.86 97.67
5%
100
100
100
100
Master model for seven consecutive day forecast
Input variables: HWD, Rainfall, Mean Temperature & RH
Model
Architecture Hidden
Layers
PE's
Transfer Learning
function Rule
MM-1
MLP
1
10
tanh
BD
MM-2
MLP
1
8
Sigmoid
BD
MM-3
MLP
1
14
tanh
CG
MM-4
MLP
1
8
Sigmoid
CG
MM-5
MLP
2
9&7
tanh
BD
MM-6
MLP
2
8&7
Sigmoid
BD
MM-7
MLP
2
10 & 7
tanh
CG
MM-8
MLP
2
8&7
Sigmoid
CG
Date
Observed
Demand
MCM
28-Jun-08
29-Jun-08
30-Jun-08
1-Jul-08
2-Jul-08
3-Jul-08
4-Jul-08
3.502
3.481
3.433
3.410
3.391
3.413
3.471
13-Aug-08
14-Aug-08
15-Aug-08
16-Aug-08
17-Aug-08
18-Aug-08
19-Aug-08
3.406
3.416
3.425
3.410
3.388
3.371
3.391
20-Jul-08
21-Jul-08
22-Jul-08
23-Jul-08
24-Jul-08
25-Jul-08
26-Jul-08
3.450
3.432
3.450
3.425
3.460
3.510
3.452
Forecasted
Demand
MCM
TEST
3.508
3.489
3.475
3.434
3.431
3.426
3.431
TEST
3.333
3.334
3.332
3.358
3.358
3.349
3.352
TEST
3.320
3.323
3.321
3.359
3.357
3.345
3.347
AARE
RMSE
AARE
RMSE
AARE
RMSE
AARE
RMSE
AARE
RMSE
AARE
RMSE
AARE
RMSE
AARE
RMSE
ARE
Error for seven consecutively forecasted seven days
D+1
D+2
D+3
D+4
D+5
D+6
D+7
Average
1.32
1.66
1.87
2.00
1.97
2.02
1.96
1.83
0.058 0.076 0.083 0.084 0.086 0.086 0.081
0.08
1.29
1.64
1.87
2.03
1.98
2.05
2.01
1.84
0.059 0.076 0.084 0.086 0.087 0.087 0.083
0.080
1.31
1.65
1.88
2.04
2.02
2.08
2.04
1.86
0.058 0.076 0.084 0.085 0.088 0.088 0.085
0.080
1.32
1.65
1.89
2.03
2.00
2.07
2.04
1.86
0.059 0.076 0.085 0.085 0.088 0.087 0.085
0.081
1.17
1.52
1.80
1.83
1.82
1.83
1.85
1.69
0.053 0.068 0.078 0.076 0.076 0.077 0.076
0.072
1.30
1.61
1.84
1.97
1.95
2.03
1.99
1.81
0.059 0.075 0.082 0.083 0.085 0.086 0.082
0.079
1.33
1.63
1.87
2.06
2.01
2.03
1.96
1.84
0.058 0.075 0.083 0.086 0.088 0.086 0.081
0.080
1.32
1.64
1.87
2.01
1.99
2.04
2.03
1.84
0.060 0.076 0.084 0.084 0.087 0.086 0.084
0.080
Avg ARE
%
%
SAMPLE - 1
0.17
0.22
1.22
0.72
0.72
1.16
0.37
1.17
SAMPLE - 2
2.15
2.39
2.72
1.64
1.51
0.90
0.64
1.16
SAMPLE - 2
3.79
3.20
3.74
3.34
1.92
2.99
4.70
3.04
RMSE
Avg RMSE
MCM
MCM
0.006
0.008
0.042
0.025
0.039
0.013
0.041
0.025
0.073
0.082
0.093
0.051
0.030
0.022
0.039
0.056
0.131
0.110
0.129
0.066
0.104
0.165
0.105
0.116
RESULTS

Best fit models for Short term Demand
Lead Period
Input variables
Architecture
• MLP - 3 layers
HWD, Rainfall,
• tanh transfer function
1 day
Mean Temp, RH • 20, 10 & 5 PE's
• Back Descend Rule
• MLP - 2 layers
• Sigmoid transfer function
2 day
HWD-1, HWD-2 • 16 & 10 PE's
• Back Descend Rule
• MLP - 2 layers
• tanh transfer function
3 day
HWD-1
• 13 & 6 PE's
• Back Descend Rule
• MLP - 3 layers
HWD, Rainfall,
• tanh transfer function
7 day consecutive
Mean Temp, RH • 8, 7 & 7 PE's
• Backward Descend Rule
Accuracy
98.88%
98.53%
98.35%
98.51%
RESULTS

Best fit models for Long term Demand
Lead Period
1 month
2 month
6 month
Input variables
Population, GPP, Education
status, Household connections
HWD, Rainfall, Max Temp
Population, GPP, Education
status, Household connections
HWD, Rainfall, RH
Population, GPP, Education
status, Household connections
HWD, Rainfall, Max Temp
Architecture
Accuracy
• MLP - 3 layers
• Sigmoid transfer function
98.88%
• 20, 10 & 8 PE's
• Conjugate Gradient Rule
• GFF - 1 layer
• tanh transfer function
98.53%
• 11 PE's
• Conjugate Gradient Rule
• MLP - 1 layer
• tanh transfer function
98.35%
• 10 PE's
• Backward Descend Rule
RESULTS
Factors influencing ST & LT Demand

Sensitivity Analysis
• Standardize all data
Y=
xx





x & σ are the mean and standard deviation
• Thus a standardized value of ‘zero’ represents the mean of the sample
• Increases and decreases the input variables between the
standardized -1 and +1
• Presents the trend of change in the demand.
RESULTS
Factors influencing ST & LT Demand
RH
Rainfall
RH
Rainfall
Mean
Temp
0.00
0.0
Max Temp
0.02
0.5
Household
Connections
0.04
1.0
Education
Status
0.06
1.5
Population
0.08
2.0
GPP
0.10
2.5
HWD
Standard Deviation (MCM)
Sensitivity indices for LT Demand
0.12
HWD
Standard Deviation(MCM)
Sensitivity indices for ST-Demand
RESULTS
Demand models for Bangkok, Hanoi & Chiang Mai

20 models prepared for each city using MLP & GFF
Input Variables: Population, GPP, Household connections,
Education status, HWD, Rainfall, Max Temperature, RH

Best fit model results, AARE
• Bangkok – 1.06%
• Hanoi – 2.18 %
• Chiang Mai – 1.26%

Sensitivity analysis to determine influencing variables
RESULTS
Bangkok
Hanoi
RH
Rainfall
Mean Temp
Households
Education
Population
Chiang Mai
GPP
7
6
5
4
3
2
1
0
Sensitivity indices of Input parameters for best models of the
three study areas
HWD
Percentage change in Demand
Demand models for Bangkok, Hanoi & Chiang Mai
CONCLUSIONS

ANN can provide the MWA with a powerful instrument to forecast the demands.
Forecasting accuracy will be over 98% for both ST & LT.
Advantages for MWA
• Schedule pumping operations
• Reduce detention time to improve water quality
• Monthly revenues can be estimated upto 6 months in advance
• Diversions, Basin transfers can be planned in dry years

Factors Influencing MWA sales demand
ST Demand : Historical water demand
LT Demand : Education status & Household connections

Comparison of factors influencing production demands of Bangkok, Hanoi and
Chiang Mai
Bangkok
: HH connections, GPP and Education
Hanoi
: Education status, Mean Temperature and Population
Chiang Mai : HH connections, Mean Temperature & Rainfall
(This information could prove vital for goverments, international
agencies and funding organizations)