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  • How Qatium sets up your network from your data
  • Normalizing your data
  • Review network topology
  • Inferring hydraulic data
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Inferring hydraulic data

Once your network is cleaned, organized, and connected, there’s one final step before it’s ready for simulation: inferring hydraulic data. This means we fill in any missing information needed to simulate how water behaves in your system.

Sometimes, datasets don’t include all the details—like pipe diameters, elevation levels, or demand values—and that’s okay. We've built a complete guide for you to add as many asset information as possible but, if it's not possible, Qatium fills in those gaps.

Here’s what happens during this step.

We complete missing elevations

If some of your assets don’t include elevation values, we automatically find and assign them. Elevation is essential for calculating pressures and flow directions, so this step ensures accurate results.

 
 

We identify or create supply sources

If your dataset doesn’t include a defined source, we identify where it should be—or create one—so the network has a clear starting point for flow.

We also calculate head elevations for those sources to ensure the system can simulate pressure and flow properly.

 
 

We fill in missing pipe diameters

If some pipes don’t have a diameter defined, we estimate it. Qatium can use a default value or infer the diameter based on neighboring pipes to keep things realistic.

See how we infer pipe's properties

 
 

We calculate how flow should behave

To simulate water movement, we need to know how assets relate to each other in terms of flow direction and importance. We assign asset levels—for example, identifying which pipes are arterial—to help define flow paths.

See more about network levels

 
 

We complete and infer demand data

If you’ve defined zones with demand information (like daily consumption for a district), we assign those values to the matching junctions.

If some demand values are missing altogether, we estimate them based on your network structure and best practices—so your simulations include realistic water usage.

 
 

We infer roughness for pipes

The equation (H-W, D-W or C-M) is inferred from roughness values (if available). If not, we’ll apply the default value: Hazen-Williams (H-W), roughness = 148.

Qatium can also infer roughness from Material and Installation date, if available.

Roughness based on material

If the installation date can’t be found, Qatium will infer the roughness based on material only.

Material

HW

CM

DW

AC: Asbestos Cement

140

0.011

0.10000

ARO: Abrasion Resistant Outerwrap

148

0.0148

0.14800

CI: Cast Iron

130

0.012

0.80000

COPP: Cooper

130

0.011

0.10000

CWOSMJ: Concrete Without Sheet Metal Jacket

140

0.011

0.18

CWSMJ: Concrete With Sheet Metal Jacket

135

0.013

0.36000

DI: Ductile Iron

130

0.012

0.30000

DIL: Lined Ductile Iron

130

0.0148

0.25000

FE: Iron

148

0.0148

0.14800

GI: Galvanized Iron

120

0.016

0.15000

GIL: Lined Galvanized Iron

120

0.0148

0.15000

HDPE: High Density Polyethylene

150

0.009

0.01000

L: Lead

140

0.011

0.01000

LDPE: Low Density Polyethylene

150

0.009

0.01000

MDPE: Medium Density Polyethylene

150

0.009

0.01000

NA: Not available

150

0.0148

0.14800

PC: Prestressed Concrete

150

0.011

0.18000

PE: Polyethylene

150

0.009

0.01000

PE80: Polyethylene 80

150

0.009

0.01000

PE100: Polyethylene 100

150

0.009

0.01000

PVC: Polyvinyl Chloride

150

0.009

0.05000

PVCO: Orientated PVC

150

0.0148

0.05000

RC: Reinforced Concrete

140

0.0148

0.18

S: Steel

150

0.0148

0.10000

SI: Spun Iron

150

0.0148

0.60000

SS: Stainless Steel

150

0.011

0.14800

UNK: Unknown

148

0.0148

0.14800

UPVC: Unplasticized PVC

150

0.009

0.05000

 
 

Roughness based on installation date

The value calculated from the material will then be amended based on the pipe’s age, i.e. the number of years since its installation date.

Depending on the head loss formula applied, this is how this works:

H-W

InferredRoughness = Roughness(material)*(1-Age/180)
Value range: [50 ;+∞]

D-W

InferredRoughness = Roughness(material)/(1-Age/180)
Value range: [0.05 ; 50]

C-M

InferredRoughness = Roughness(material)/(1-Age/180)
Value range: [0 ; 0.05]

What if the resulting roughness value is out of range?

Qatium would use the value of the range limit for the corresponding headloss formula.
E.g. For H-W, if the value calculated is 40, the inferred value would be 50.

 
 
 
 

With all hydraulic values inferred, your network is now fully simulation-ready. From elevations and demands to supply and flow paths, every detail is in place—so you can start exploring your system, running scenarios, and making data-driven decisions in Qatium.

deducing hydro values

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