Water network |
Application category |
Reference |
Application |
Location |
Size: Pipes in model / [area
\(\mathbf{k}\mathbf{m}^{\mathbf{2}}\)]
|
Classification by size |
Type |
Deviations
from simple MLP |
Inputs (Number) |
Outputs (Number) |
Computational saving |
Accuracy |
Water distribution systems |
Optimisation |
(Sayers et al., 2019) |
Design |
TLN, GOY, MOD, BIN |
8, 30, 317, 454 |
S, S, I, I |
Benchmark |
2 hidden layers |
Diameters * |
Rating of the network (1) |
Not reported |
Not reported |
|
|
(Dini & Tabesh, 2019) |
Renovation planning |
TLN and Ahar,
Azerbaijan |
8 and 192 |
S, M |
Benchmark and Real case |
|
Diameters
* |
Nodal pressure* and chlorine concentration * |
Not reported |
Not
reported |
|
|
(Dini & Tabesh, 2017) |
Model calibration |
TLN and Ahar,
Azerbaijan |
8 and 192 |
S, M |
Benchmark and Real case |
|
Observed
residual chlorine * |
Wall Decay coefficient (1) |
58x faster (98.3%) |
Average error (3.85%) |
|
|
(Andrade et al., 2016) |
Design |
HAN and Maricopa, Arizona |
34 and
1090 |
S, L |
Benchmark and Real case |
Comparison of ANNs varying
number of inputs and outputs |
Diameters and Chlorine dosing rates |
Chlorine concentration. (HAN): 3; (Maricopa): 9 |
Not reported |
NSE
(~90%) |
|
|
(Bi & Dandy, 2014) |
Design |
(I) NYT, (II) modified NYT and (III)
Jilin |
21, 21, and 34 |
S, S, S |
(I) Benchmark, (II) modified
benchmark, and (III) synthetic network |
|
Diameters and Chlorine
dosing rates (I & II: 22; III: 35) |
Pressures at some nodes (I & II:
4; III: 5) and residual chlorine at one node (I & II: 1; III: 7) |
(I
& II) 91%; (III) 93%, 88%, and 77% |
MSE (Not reported, 0.001 as
one stopping criteria) |
|
|
(Broad et al., 2010) |
Operation |
Wallan, Australia |
2097;(Sk:
1376) |
L (L) |
Real case |
|
Trigger levels (45) and Chlorine rates
(5) |
Pressure Head at critical node (1), Chlorine residual (1), energy
value (1), or Total chlorine dosed (1) |
99% |
NSE
(~0.6 for the full model, ~0.98 for
skeletonized model) |
|
|
(Behzadian et al., 2009)
|
Sensor placement
|
Anytown; Mahalat, Iran
|
41, and 1814;(Sk: 217)
|
S, L (M)
|
Benchmark and Real case
|
|
Available sensors
|
Sampling design accuracy (1)
|
8x and 25x faster
(87% and 96%)
|
Pareto similarity: 93%
|
|
|
(Salomons et al., 2007) |
Operation |
Haifa-A, Israel |
126 |
M |
Modified real case |
|
Pumping status (13), Valve settings (1), DMA
demands (6), Storage levels (9) |
Power consumption (5), pressures (4),
future storage levels (9) |
25x faster (96%) |
RMSE (0.481%)
~5 cm averaged over all tanks |
|
|
(Martínez et al., 2007) |
Operation |
Valencia, Spain |
772 |
L |
Modified real case |
|
Pumping status (6), Valve settings (10), DMA
demands (6), Storage levels (2) |
Power consumption (6), flow rates (3),
pressures (4), future storage levels (2) |
94x faster (99%) |
RMSE
(1.30%) |
|
|
(Broad et al., 2005a) |
Design |
NYT |
21 |
S |
Benchmark |
|
Diameters and Chlorine dosing rate (22) |
Four pressure nodes (1) or
Chlorine concentration (1) |
700x faster (99.85%) |
RMSE (0.05 -
0.250) |
|
Real-time |
(Pasha & Lansey, 2014) |
Warm solutions for pump
scheduling |
Modified Anytown |
37 |
S |
Modified Benchmark |
SVM |
Pump
combination, demand multiplier, initial tank levels |
Energy and final
tank levels |
84.25% |
NSE (0.99) |
|
|
(Rao & Alvarruiz, 2007; Rao & Salomons, 2007) |
Real-time pump
scheduling |
Modified AnyTown |
41 |
S |
Modified Benchmark |
|
Number
of operating pumps (1), aggregated demand (1), and tank levels (3) |
Power consumption (1), pressures (3), new tank levels (3) |
10-fold
(90%) |
RMSE (1.65%) |
|
Uncertainty analysis |
(Yoon et al., 2020) |
Seismic risk assessment |
A-city, South Korea |
85 |
S |
Anonymous real case |
15 layers - Deep
neural network |
Components’ state (218) |
Network performance (1) |
99% |
<5% |
|
|
(Beh et al., 2017)
|
Planning under deep uncertainty
|
Adelaide, Australia
|
NA
|
L
|
Real case
|
Combination of 4 MLPs
|
Supply augmentation options (9) and Uncertain variables: Population and
climate change scenarios (2)
|
(I) PV of cost (II) PV of Greenhouse gases (III) Reliability (IV)
Vulnerability
|
>99%
|
Relative error (+-5%)
NSE (~0.94, 0.95, 0.78, and 0.84)
|
|
System state estimation |
(Lima et al., 2018) |
Nodal pressure
estimation at near real-time |
Campos do Conde II and Cambuí, Brazil |
153 and 167 |
M, M |
Real case |
|
Pressure in sensors Steady State:
(3) - Extended (24h): 96. Cambuí: (4) |
Pressure in nodes Steady State:
(118) - Extended (24h): 2832. Cambuí: Steady (154 and 4) |
Not reported |
Relative error (<1%) and (<4%) |
|
|
(Meirelles et al., 2017) |
Calibration with estimated pressures |
Campos do Conde II, Brazil and C-Town |
153 and 429 |
M, I |
Real case
and Benchmark |
|
Pressure in sensors Steady State: (3) - Extended
(24h): 96. C-Town: 5 MLPs, one per DMA. |
Pressure in nodes Steady
State: (118) - Extended (24h): 2832 |
Not reported |
Average error (0.15
m) Max. Error (13.83 m) |