# Imperfect data and hydraulic modelling of urban drainage networks

Carole Delenne, Yassine Belghaddar, Nanée Chahinian

► To cite this version:

Carole Delenne, Yassine Belghaddar, Nanée Chahinian. Imperfect data and hydraulic modelling of urban drainage networks. ECSQARU 2023 - 17th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty. Special session on AI and heterogeneous data, Sep 2023, Arras, France. hal-04403952

HAL Id: hal-04403952

https://inria.hal.science/hal-04403952v1

Submitted on 26 Jan 2024

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# Imperfect data and hydraulic modelling of urban drainage networks

EVERYTHING YOU ALWAYS WANTED TO KNOW ABOUT DRAINAGE WATER NETWORKS BUT WERE AFRAID TO ASK

Carole Delenne, in collaboration with: Yassine Belghaddar, Nanée Chahinian ... and many others !
# Le Cycle Naturel de l'eau
(Le grand cycle)

## Natural Water Cycle

<figure><img src="image_2.png" /></figure>
# Flood Risk

<figure><img src="image_3.png" /></figure>

Risk = combination of hazards and issues

<figure><img src="image_4.png" /></figure>

Floods from 1995 to 2015*:

* 2,3 billion people affected

* 157000 victims

*United Nations report “The Human Cost of Weather Related disasters”, 2015

<figure><img src="image_5.png" /></figure>

(c) Sylvain Thomas/AFP

<figure><img src="image_6.png" /><figcaption>Hazard: river overflow in the major bed</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Issues: people, economical activities...</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>risk: flood of severity depending on the hazard (extent of the flood) and the issue (vulnerability)</figcaption></figure>

Adapted from

"Les inondations : aléa, enjeu, risque" © Agence française pour la biodiversité / Réalisation Matthieu Nivesse (d'après OIEau), 2018
Natural vs Urban Environments

© Philadelphia water

<figure><img src="image_9.png" /></figure>

Urban water
Networks:

<figure><img src="image_10.png" /></figure>

► Channels and canalised rivers

<figure><img src="image_11.png" /></figure>

► Drainage: wastewater / stormwater

<figure><img src="image_12.png" /></figure>

► (Water supply)

<figure><img src="image_13.png" /></figure>

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Stormwater and wastewater networks

<figure><img src="image_19.png" /></figure>

<figure><img src="image_20.png" /></figure>

► Generated and planned by man

► Follows roads and ditches without loops (branched networks)

► Transport water from built areas to treatment plants or river/sea

► Closed conducts or open channels but mostly free surface flows
-> gravity flow (few pumps)
# Urban flood modelling

<figure><img src="image_21.png" /></figure>

**Drainage networks play a crucial role during urban floods:**

* normally acting as collectors

* they can overflow downstream when saturated.

<figure><img src="image_22.png" /></figure>

**Not taken into account in most urban flood models**

**whereas:**

<figure><img src="image_23.png" /></figure>

* well established models for free surface flows and

<figure><img src="image_24.png" /></figure>

* coupling between free surface and underground network models exists

<figure><img src="image_25.png" /></figure>

**But crucial lack of information about drainage network (even in France)**

* pieces of information: incomplete, imprecise, and even sometimes contradictory
<figure><img src="image_26.png" /></figure>

# Water network representation
# Geographic Information Systems

<figure><img src="image_27.png" /></figure>

## Drainage networks represented by:

* Points: network's nodes, i.e. manholes, devices...

* Polylines: network's pipes

* Polygons: surfaces such as retention basins

<figure><img src="image_28.png" /></figure>

<figure><img src="image_29.png" /></figure>

## Classical format=shapefile .shp

* Composed of 6 files, including: Geometry as set of vertices (shp), Attribute data file (dbf), Geographical projection (prj)...

<figure><img src="image_30.png" /></figure>

## Associated attributes

<figure><img src="image_31.png" /></figure>

### 2 main issues:

* 1 shapefile = 1 type of geometry only => e.g. no real connection between nodes and pipes

* Attributes: missing, false, contradictory...
# Geographic Information Systems

<figure><img src="image_32.png" /></figure>

Topology not displayed directly but may be built:

<figure><img src="image_33.png" /></figure>

* Lines: "From" and "To" nodes for connectivity

<figure><img src="image_34.png" /></figure>

* Polygons: "Right" and "left" polygons for adjacency

<figure><img src="image_35.png" /></figure>

<figure><img src="image_36.png" /></figure>

dc: disconnected, ec: externally connected, po: partially overlapping, eq: equals, ntppi: (non) tangential proper part (inverse)

<figure><img src="image_37.png" /></figure>

## Network topology

<figure><img src="image_38.png" /></figure>

* often not reported in stormwater/sewer open access data sources

<figure><img src="image_39.png" /></figure>

* missing data on elevation/slope renders it difficult to infer
# Some attempts to address data limitations

* Node and polyline overlays

* Duplicate data

* Missing attributes and metadata

* Approximative geolocalisation

* Contradictory data

* Multi-format data

* ...
# Wastewater network knowledge

<figure><img src="image_40.png" /></figure>

## Lack of information prevents from hydraulic modelling

* Algorithms exist to determine the optimal configuration of the network

[Walter, 1995; Afshar, 2007; Moeini et Afshar, 2012; Swamee & Sharma, 2013].

* But optimal configuration ≠ real network

<figure><img src="image_41.png" /></figure>

## Emergence of a new research theme at HSM through several projects since 2014

<figure><img src="image_42.png" /></figure>
# Mapping using manhole cover detection

## Local project « THUB » (2014 N. Chahinian)

* Acquisition of 5cm resolution images of Gigean and Prades-Le-Lez

## Method

* pre-treatment: road detection, vegetation and shadow filter

* Manhole cover detection: circular filter based on the radiometric difference between object and its environment (Bhattacharrya coefficient [Niigaki2012])

<figure><img src="image_43.png" /></figure>

<figure><img src="image_44.png" /></figure>

## Meeting with ICAR team (LIRMM):

* Comparison with a learning-based method (SVM) developed for grave detection (Pasquet et al., 2015 JURSE)

<figure><img src="image_45.png" /><figcaption>Grid</figcaption></figure>

<figure><img src="image_46.png" /></figure>

<figure><img src="image_47.png" /></figure>
# Cart'Eaux project

Chercheur d'avenir 2015 (HSM, LIRMM + LISAH and TETIS)

<figure><img src="image_48.png" /></figure>

## Difficulties of manhole cover detection

<figure><img src="image_49.png" /></figure>

small objects, slightly contrasted, confusions

<figure><img src="image_50.png" /></figure>

<figure><img src="image_51.png" /></figure>

## Deep Learning

<figure><img src="image_52.png" /></figure>

Learning database:

* Prades le Lez: 5cm resolution images in natural colours (RGB)

* Only 605 covers => database augmentation
 env 20 000 covers and 50 000 others)

<figure><img src="image_53.png" /></figure>

AlexNet:

* Convolutional Neural Network CNN

* Parameters adaptation to small objects

* 5 layers

* 40x40 sliding window over the whole image (4 pixels step)

<figure><img src="image_54.png" /></figure>

<figure><img src="image_55.png" /></figure>

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<figure><img src="image_57.png" /></figure>
# Manhole cover detection: Validation

<figure><img src="image_58.png" /></figure>

Gigean's image: 100 covers

<figure><img src="image_59.png" /></figure>

Comparison detection / truth: a=%common surface

* True Positive (TP): $a > 50\%$

* False Positive (FP): $a \le 50\%$

* False Negative (FN): non detected cover

<figure><img src="image_60.png" /></figure>

$$ \text{Precision} = \frac{\text{TP}}{\text{TP} + \text{FP}} \quad \text{Total detected} $$

$$ \text{Recall} = \frac{\text{TP}}{\text{TP} + \text{FN}} \quad \text{Total true} $$

<figure><img src="image_61.png" /><figcaption>ROC curve: precision vs recall for different acceptation thresholds</figcaption></figure>

Different boosts of the network

J. Pasquet et al. IEEE J.STARS, 2016 <hal-01275684>.

B. Commandre et al. ISPRS workshop Hanover <hal-01556762>.

Carole Delenne - ECSQARU 2023 special session on AI and heterogeneous data

## Still lots of False Positives

* Best rate of detection with threshold of 0.26

* Recall = 60% — Precision = 47%

* Perspective: check the presence of manhole cover on streetview

<figure><img src="image_62.png" /></figure>
# Network mapping from detected nodes

<figure><img src="image_63.png" /></figure>

## Based on “business” cost functions

* slope

* length

* angle between pipes

* + penalties if a pipe goes out the street or through buildings

<figure><img src="image_64.png" /></figure>

## Stochastic version

<figure><img src="image_65.png" /></figure>

The darker the line, the more likely it is to be a pipe.

<figure><img src="image_66.png" /></figure>

N. Chahinian et al. CEUS, 2019 <hal-02275903>.

B. Commandre et al., ICUD Conference on Urban Drainage, 2017.

**But the pipes' characteristics are still unknown!**
# Pipes characteristics

<figure><img src="image_67.png" /></figure>

► **CIFRE PhD Y. Belghaddar**
(Berger-Levrault, UM, USMBA)
**“Data fusion for urban networks mapping”**

<figure><img src="image_68.png" /><figcaption>Example:
Prediction of missing diameters</figcaption></figure>

<figure><img src="image_69.png" /></figure>

## 3 Axes

* Implementation of a generic model for merging multi-source data

* images, texts, tabular databases...

* Geographical data fusion

* Use of belief theory for element matching from different maps

* Missing data completion

* Use of Graph Neural Networks: Account for network's structure => better prediction of minority classes

<figure><img src="image_70.png" /></figure>

<figure><img src="image_71.png" /></figure>

Recall for each class (TP/NbClass)
30% of the database for learning, 70% for the test.
## Megadata, Linked Data and Data Mining for Wastewater Networks

<figure><img src="image_72.png" /></figure>

### Objectives:

* Take advantage of web metadata to provide network geometry and "ancillary" data that can be used by network managers.

* Creation of a corpus relating to the history of the network

* Keep a record of breakdowns and work carried out on the network

<figure><img src="image_73.png" /></figure>

### Challenges

* Translate unstructured textual information into quantitative data and structured knowledge of the network.

* Transform approximate information into uncertain knowledge.

<figure><img src="image_74.png" /></figure>
# MeDo Project

## Approach and first results

<figure><img src="image_75.png" /></figure>

<figure><img src="image_76.png" /></figure>

## First results

* Good classification performance (F-score >0.90)

* Detects certain entities e.g. works, network elements (F-score > 0.63).

* Very little quantitative data (geometry, flow rate)

<figure><img src="image_77.png" /></figure>

<figure><img src="image_78.png" /></figure>

STARWARS: further work and applications in French, English and Italian
## Transform information into data

<figure><img src="image_79.png" /></figure>

### Objective: Integrate information into a geographic database

Demo on: http://webmedo.msem.univ-montp2.fr/?page_id=12

<figure><img src="image_80.png" /></figure>

La Métropole va procéder à des travaux d'extension du réseau d'eaux pluviales avenue de la Colline dans le quartier Cévennes, à partir du Jeudi 20 octobre 2009.

Ces travaux, qui débutent pour une durée de 3 semaines, consistent à réaliser une extension de la conduite d'évacuation des eaux pluviales de l'avenue de la Colline, notamment pour accorder le nouveau bâtiment du lycée Jules Ferry. Cette conduite s'arrête actuellement au-dessus de la rue Favier et elle sera prolongée jusqu'à la rue des Eglantiers.

(source: www...)

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<table><thead><tr><td>Shape</td><td>ID</td><td>Date_install</td><td>Source</td><td>Fiabilité</td></tr></thead><tbody><tr><td>Polyline</td><td>2369</td><td>NULL</td><td>M3M</td><td>1</td></tr><tr><td>Polyline</td><td>458</td><td>07/11/2009</td><td>Doc_xxx</td><td>2</td></tr></tbody></table>

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2 Projects lead par Salem Benferhat (CRIL)

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<figure><img src="image_84.png" /></figure>

**ANR CROQUIS:**
“Collecting, Representing, Completing, merging and Querying heterogeneous and Uncertainly Stewater and stormwater network data”

► Partners: Lens (CNRS), I3S/Inria Wimmics, HSM (IRD)

<figure><img src="image_85.png" /></figure>

**Staff-Exchange STARWARS:**
“Stormwater and Wastewater networkS heterogeneous data AI-driven management”

► Partners: Lens (CNRS, Univ. Artois), Afrique du Sud (Cape Town Univ.), Vietnam (Can Tho Univ.), Maroc (USMBA Fès), Italy (CNR Pise), Montpellier (UM, IRD)

<figure><img src="image_86.png" /></figure>
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AUTUMN SCHOOL IA2

AI and the Management of Heterogeneous Information and Data

October 30 - November 03, 2023

Le Lazaret, Sète (France)

https://sites.google.com/view/autumn-school-ia/

One week left to register !