Département Gestion des eaux urbaines
Logiciels
Sewage Pattern Generator (SPG)
SPG est un bibliothèque logicielle R qui fournit des fonctions pour:
- simuler des modèles de flux et de substances à haute résolution temporelle (réseaux complexes d'égouts, y compris les stations de pompage)
- évaluer et optimiser les configurations d'échantillonnage pour faciliter la collecte d'échantillons composites représentatifs
L'objectif principal de SPG est de modéliser efficacement des variations réalistes à court terme des flux et des substances dans les égouts et d'évaluer la pertinence des différentes configurations d'échantillonnage. Outre les réseaux complexes d'égouts, cette bibliothèque logicielle est également particulièrement utile pour optimiser l'échantillonnage au niveau des effluents des établissements individuels (par exemple hôpital, école, prison), où les variations à court terme sont en général les plus élevées et où la collecte fiable d'échantillons moyens représentatifs sur une période de temps est un défi. L'accent est mis sur les produits chimiques dans les égouts, par exemple les produits pharmaceutiques ou les drogues illicites qui entrent généralement dans le système d'égouts par les chasses d'eau. Dans la mesure où les chasses d'eau sont des impulsions distinctes de courte durée, les «variations à court terme» à un emplacement potentiel d'échantillonnage nécessite la modélisation des flux de polluants dynamiquement avec une résolution temporelle de 1-2 minutes.
Différents scénarios (par exemple la distribution de substance à travers des sous-captages) et des configurations d'échantillonnage peuvent être évalués à un coût de calcul relativement faible. Les processus d'advection et de dispersion sont pris en compte.
Téléchargements (v1.01)
- R-Package SPG (Windows ZIP file)
- R-Package SPG (Mac tar.gz file)
- Help on how to install R and the SPG package [PDF file]
- Example (R file)
- Exercise [PDF file]
- Source code (GitHub)
Tous les feedbacks seront appréciés par les auteurs.
La bibliothèque logicielle R SPG a été développée dans le cadre du projet SEWPROF - un nouveau paradigme en matière d’usage de drogues et de d'évaluation des risques pour la santé humaine: Le profilage des égouts au niveau communautaire.
Sustainable Network Infrastructure Planning (SNIP)
SNIP is a python based software package with an ArcGIS interface to determine the optimal degree of centralisation for wastewater infrastructure systems. Based on GIS input data an optimal separate sewer system is designed based on different sewer design criteria.
SNIP is thoroughly explained in: Eggimann, S., Truffer, B., Maurer, M. (2015): To connect or not to connect? Modelling the optimal degree of centralisation for wastewater infrastructures. Water Research, 84, 218-231. Link.
Downloads
Github (Python Source-Code & ArcToolbox)
Example Data (Small test dataset)
Installation Guide (Help on how to use SNIP)
Continuous Assimilation of Integrating Rain Sensors (CAIRS)
CAIRS is a framework to reconstruct rain fields by assimilating signals of fundamentally different rain sensors.
In particular, the integration characteristics of sensors are explicitly considered. For example, non-recording standard rain gauges integrate over time and deliver information such as the daily rainfall sums. The rain-induced attenuation of micro wave links (MWL) can be used to measure the path-integrated intensities—an example of a sensor with spatial integration.
Also sensor signals with different scales (e.g. continuous, binary) can be assimilated. Furthermore, CAIRS is formulated continuously in time and space, to deal in a natural way with signals with measured on irregular time-intervals.
The mathematical model is described in Scheidegger and Rieckermann (2014). The basic functionality and application is explained in this tutorial.
Reference
Scheidegger, A. and Rieckermann, J. (2014) "Bayesian assimilation of rainfall sensors with fundamentally different integration characteristics" in WRaH Proceedings, Washington, DC. Link.
Download
CAIRS is implemented as package for Julia.
Adaptive Monte Carlo Markov chain sampler (adaptMCMC)
R package that provides an implementation of the generic adaptive Monte Carlo Markov chain (MCMC) sampler proposed by Vihola (2011). MCMC samplers are often used to perform Bayesian inference.
Reference
Vihola, M., 2011. Robust adaptive Metropolis algorithm with coerced acceptance rate. Statistics and Computing. doi:10.1007/s11222-011-9269-5.
Download
Sewer deterioration model (SWIP-SDM)
Introduction
Predictions of the structural condition of urban drainage networks enables us to estimate future investment costs under various rehabilitation strategies. This facilitates proactive, far-sighted sewer asset management, which, in turn, contributes to a better balance between expenses and system performance.
Many sewer deterioration models have been formulated to predict the physical aging of pipes in terms of discrete sewer condition states. However, in many cases there is not sufficient information available to calibrate them. A severe issue is that historical asset data is not available. Specifically, the lack of the following information often hinders us to calibrate sewer deterioration models:
- (i) Condition records of sewer pipes which have been replaced by new ones. The corresponding records are discarded from the database and replaced by the new information.
- (ii) Condition ratings of renovated or repaired sewer pipes from the time before such action. The repaired or renovated pipes are reassessed. On the basis of this reassessment, the condition rating prior to repair or renovation is overwritten by a new (typically better) condition rating. Thus, such records should be excluded from the analysis. The condition of such pipes is the result of not only aging but also rehabilitation.
When a sewer deterioration model is calibrated without explicitley accounting for effect (i) and (ii) the predictions suffer a ‘survival selection bias’ (Scheidegger et al., 2011).
The SWIP-SDM represents a simple probabilistic sewer deterioration model and provides the possibility to infer the deterioration parameters from data lacking historical information as defined by case (i) and (ii). Details of the model are given in Egger et al. (2013).
Implementation
The R code provided below allows for model calibration and forecasting of condition states of the network. Model calibration is done by Bayesian inference (Egger et al., 2013). The likelihood functions described in Egger et al. (2013) (Eq. 1 and 6) are extended so that also condition data from two subsequent inspections per pipe can be used for parameter inference.
Model files
SDM.zip contains the required R code. Look at SDM_inference.R to do parameter inference and at SDM_forecast.R to predict future condition states for a given model parameters.
Example data
For demonstration additionally two datasets are provided. Both contain the condition ratings of a sewer network which underwent substantial rehabilitation in form of pipe replacements in the last 30 years. Each row of the datasets represents one pipe with one or two observed condition states. Dataset dataset_example_complete.RData contains the condition records of all pipes ever constructed, i.e. it includes also pipes that have been replaced in the past. The data set dataset_example_incomplete.RData is more realistic. It contains only those pipes that are still in service, i.e. the data set lacks historical data of pipes replaced in the past (according to case (i) defined above).
Matching parameters for a log-normal prior distribution are contained in prior_complete.RData and prior_incomplete.RData respectively.
The development of SWIP-SDM was part of the National Research Project 61 (NRP 61) http://www.nfp61.ch/E/Pages/home.aspx funded by the Swiss National Science Foundation (SNF) (project number 406140_125901/1).
References
Egger, C., Scheidegger, A., Reichert, P. and Maurer, M. (2013) Sewer deterioration modeling with condition data lacking historical records. Water Research 47(17), 6762-6779.
Scheidegger, A., Hug, T., Rieckermann, J. and Maurer, M. (2011) Network condition simulator for benchmarking sewer deterioration models. Water Research 45(16), 4983-4994
Urban Water Infrastructure Model (UWIM)
The urban water infrastructure model (UWIM) is a conceptual model that describes the water infrastructure of a settlement quantitatively in terms of generic input parameters, such as size of catchment area, number of buildings.
For more information see the project description.
UWIM is licensed under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
