A Hybrid Surrogate Modelling Strategy for Simplification of Detailed Urban Drainage Simulators View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-12

AUTHORS

Mahmood Mahmoodian, Juan Pablo Carbajal, Vasilis Bellos, Ulrich Leopold, Georges Schutz, Francois Clemens

ABSTRACT

Urban drainage modelling typically requires development of highly detailed simulators due to the nature of various underlying surface and drainage processes, which makes them computationally too expensive. Application of such simulators is still challenging in activities such as real-time control (RTC), uncertainty quantification analysis or model calibration in which numerous simulations are required. The focus of this paper is to present a rather simple hybrid surrogate modelling (or emulation) strategy to simplify and accelerate a detailed urban drainage simulator (UDS). The proposed surrogate modelling strategy includes: a) identification of the variables to be emulated; b) development of a simplified conceptual model in which every component contributing to the variables identified in step (a) is replaced by a function; c) definition of these functions, either based on knowledge about the mechanisms of the simulator, or based on the data produced by the simulator; and finally, d) validation of the results produced by the surrogate model in comparison with the original detailed simulator. Herein, a detailed InfoWorks ICM simulator was selected for surrogate modelling. The case study area was a small urban drainage network in Luxembourg. An emulator was developed to map the rainfall time series, as input, to a storage tank volume and combined sewer overflow (CSO) in the case study network. The results showed that the introduced strategy provides a reliable method to simplify the simulator and reduce its run time significantly. For the specific case study, the emulator was approximately 1300 times faster than the original detailed simulator. For quantification of the emulation error, an ensemble of 500 rainfall scenarios with 1 month duration was generated by application of a multivariate autoregressive model for conditional simulation of rainfall time series. The results produced by the emulator were compared to the ones produced by the simulator. Finally, as an indicator of the emulation error, distributions of Nash-Sutcliffe efficiency (NSE) between the emulator and simulator results for prediction of storage tank volume and CSO flow time series were presented. More... »

PAGES

1-16

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11269-018-2157-4

DOI

http://dx.doi.org/10.1007/s11269-018-2157-4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1110448863


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Delft University of Technology", 
          "id": "https://www.grid.ac/institutes/grid.5292.c", 
          "name": [
            "Luxembourg Institute of Science and Technology, Belvaux, Luxembourg", 
            "Delft University of Technology, Delft, The Netherlands"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Mahmoodian", 
        "givenName": "Mahmood", 
        "id": "sg:person.016103460004.14", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016103460004.14"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Swiss Federal Institute of Aquatic Science and Technology", 
          "id": "https://www.grid.ac/institutes/grid.418656.8", 
          "name": [
            "Swiss Federal Institute of Aquatic Science and Technology, Eawag, D\u00fcbendorf, Switzerland"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Carbajal", 
        "givenName": "Juan Pablo", 
        "id": "sg:person.0774355534.14", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0774355534.14"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "National Technical University of Athens", 
          "id": "https://www.grid.ac/institutes/grid.4241.3", 
          "name": [
            "CH2M, Swindon, UK", 
            "National Technical University of Athens, Athens, Greece"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Bellos", 
        "givenName": "Vasilis", 
        "id": "sg:person.015143750055.31", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015143750055.31"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Luxembourg Institute of Science and Technology", 
          "id": "https://www.grid.ac/institutes/grid.423669.c", 
          "name": [
            "Luxembourg Institute of Science and Technology, Belvaux, Luxembourg"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Leopold", 
        "givenName": "Ulrich", 
        "id": "sg:person.016260226137.52", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016260226137.52"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "RTC4Water, Belval, Luxembourg"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Schutz", 
        "givenName": "Georges", 
        "id": "sg:person.014333162025.76", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014333162025.76"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Deltares", 
          "id": "https://www.grid.ac/institutes/grid.6385.8", 
          "name": [
            "Delft University of Technology, Delft, The Netherlands", 
            "Deltares, Delft, The Netherlands"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Clemens", 
        "givenName": "Francois", 
        "id": "sg:person.011517660707.69", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011517660707.69"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/j.jhydrol.2004.08.010", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000322002"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jconhyd.2016.01.004", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002292935"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10898-004-0570-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004246579", 
          "https://doi.org/10.1007/s10898-004-0570-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11269-011-9852-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006572371", 
          "https://doi.org/10.1007/s11269-011-9852-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2514/2.1570", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008038063"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.watres.2013.03.051", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009163907"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/2011wr011527", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009578628"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/2013wr015119", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009670205"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/1573062x.2013.820332", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013300927"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11269-013-0468-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015382397", 
          "https://doi.org/10.1007/s11269-013-0468-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/1573062x.2013.806560", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018259715"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/2015wr016967", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019297026"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11269-016-1337-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021950555", 
          "https://doi.org/10.1007/s11269-016-1337-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2514/1.36043", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025694783"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ress.2005.11.025", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029305638"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.envsoft.2013.12.018", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029383520"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jhydrol.2016.04.056", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039384126"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.watres.2007.07.038", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043901869"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.envsoft.2012.12.008", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044827661"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.envsoft.2004.02.004", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046986068"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/b978-0-444-59520-1.50123-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047505811"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11269-016-1474-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049772366", 
          "https://doi.org/10.1007/s11269-016-1474-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11269-016-1474-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049772366", 
          "https://doi.org/10.1007/s11269-016-1474-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1061/(asce)0733-9496(1999)125:1(3)", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1057605726"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1137/s1064827502418768", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062884061"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/003754977502400606", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1063683617"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/003754977502400606", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1063683617"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2118/99446-pa", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1068963581"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2166/hydro.2009.151", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069134738"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2166/wst.2010.382", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069144061"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2166/wst.2011.058", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069144481"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2166/wst.2013.253", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069146214"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2166/wst.2002.0066", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1075017091"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2166/wst.2002.0628", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1075027503"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/acs.est.6b04267", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1079402564"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.envsoft.2017.02.006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1083891686"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.envsoft.2017.02.030", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084525934"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/uksim.2013.137", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093834834"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/uksim.2013.137", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093834834"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jclepro.2018.01.139", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1100726273"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11269-018-1959-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101551230", 
          "https://doi.org/10.1007/s11269-018-1959-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11269-018-1959-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101551230", 
          "https://doi.org/10.1007/s11269-018-1959-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11269-018-1959-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101551230", 
          "https://doi.org/10.1007/s11269-018-1959-8"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2018-12", 
    "datePublishedReg": "2018-12-01", 
    "description": "Urban drainage modelling typically requires development of highly detailed simulators due to the nature of various underlying surface and drainage processes, which makes them computationally too expensive. Application of such simulators is still challenging in activities such as real-time control (RTC), uncertainty quantification analysis or model calibration in which numerous simulations are required. The focus of this paper is to present a rather simple hybrid surrogate modelling (or emulation) strategy to simplify and accelerate a detailed urban drainage simulator (UDS). The proposed surrogate modelling strategy includes: a) identification of the variables to be emulated; b) development of a simplified conceptual model in which every component contributing to the variables identified in step (a) is replaced by a function; c) definition of these functions, either based on knowledge about the mechanisms of the simulator, or based on the data produced by the simulator; and finally, d) validation of the results produced by the surrogate model in comparison with the original detailed simulator. Herein, a detailed InfoWorks ICM simulator was selected for surrogate modelling. The case study area was a small urban drainage network in Luxembourg. An emulator was developed to map the rainfall time series, as input, to a storage tank volume and combined sewer overflow (CSO) in the case study network. The results showed that the introduced strategy provides a reliable method to simplify the simulator and reduce its run time significantly. For the specific case study, the emulator was approximately 1300 times faster than the original detailed simulator. For quantification of the emulation error, an ensemble of 500 rainfall scenarios with 1 month duration was generated by application of a multivariate autoregressive model for conditional simulation of rainfall time series. The results produced by the emulator were compared to the ones produced by the simulator. Finally, as an indicator of the emulation error, distributions of Nash-Sutcliffe efficiency (NSE) between the emulator and simulator results for prediction of storage tank volume and CSO flow time series were presented.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s11269-018-2157-4", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.3790860", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1136873", 
        "issn": [
          "0920-4741", 
          "1573-1650"
        ], 
        "name": "Water Resources Management", 
        "type": "Periodical"
      }
    ], 
    "name": "A Hybrid Surrogate Modelling Strategy for Simplification of Detailed Urban Drainage Simulators", 
    "pagination": "1-16", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "d224dc7be82fb3fc5ddff4f9d3bfe4ece0eeb61e46640f222fbbd76c6bfc44af"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s11269-018-2157-4"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1110448863"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s11269-018-2157-4", 
      "https://app.dimensions.ai/details/publication/pub.1110448863"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T08:21", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000290_0000000290/records_34913_00000000.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs11269-018-2157-4"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s11269-018-2157-4'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s11269-018-2157-4'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11269-018-2157-4'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11269-018-2157-4'


 

This table displays all metadata directly associated to this object as RDF triples.

229 TRIPLES      21 PREDICATES      63 URIs      17 LITERALS      5 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s11269-018-2157-4 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N57a090c51fe94c46b41467389641be3e
4 schema:citation sg:pub.10.1007/s10898-004-0570-0
5 sg:pub.10.1007/s11269-011-9852-8
6 sg:pub.10.1007/s11269-013-0468-z
7 sg:pub.10.1007/s11269-016-1337-3
8 sg:pub.10.1007/s11269-016-1474-8
9 sg:pub.10.1007/s11269-018-1959-8
10 https://doi.org/10.1002/2013wr015119
11 https://doi.org/10.1002/2015wr016967
12 https://doi.org/10.1016/b978-0-444-59520-1.50123-8
13 https://doi.org/10.1016/j.envsoft.2004.02.004
14 https://doi.org/10.1016/j.envsoft.2012.12.008
15 https://doi.org/10.1016/j.envsoft.2013.12.018
16 https://doi.org/10.1016/j.envsoft.2017.02.006
17 https://doi.org/10.1016/j.envsoft.2017.02.030
18 https://doi.org/10.1016/j.jclepro.2018.01.139
19 https://doi.org/10.1016/j.jconhyd.2016.01.004
20 https://doi.org/10.1016/j.jhydrol.2004.08.010
21 https://doi.org/10.1016/j.jhydrol.2016.04.056
22 https://doi.org/10.1016/j.ress.2005.11.025
23 https://doi.org/10.1016/j.watres.2007.07.038
24 https://doi.org/10.1016/j.watres.2013.03.051
25 https://doi.org/10.1021/acs.est.6b04267
26 https://doi.org/10.1029/2011wr011527
27 https://doi.org/10.1061/(asce)0733-9496(1999)125:1(3)
28 https://doi.org/10.1080/1573062x.2013.806560
29 https://doi.org/10.1080/1573062x.2013.820332
30 https://doi.org/10.1109/uksim.2013.137
31 https://doi.org/10.1137/s1064827502418768
32 https://doi.org/10.1177/003754977502400606
33 https://doi.org/10.2118/99446-pa
34 https://doi.org/10.2166/hydro.2009.151
35 https://doi.org/10.2166/wst.2002.0066
36 https://doi.org/10.2166/wst.2002.0628
37 https://doi.org/10.2166/wst.2010.382
38 https://doi.org/10.2166/wst.2011.058
39 https://doi.org/10.2166/wst.2013.253
40 https://doi.org/10.2514/1.36043
41 https://doi.org/10.2514/2.1570
42 schema:datePublished 2018-12
43 schema:datePublishedReg 2018-12-01
44 schema:description Urban drainage modelling typically requires development of highly detailed simulators due to the nature of various underlying surface and drainage processes, which makes them computationally too expensive. Application of such simulators is still challenging in activities such as real-time control (RTC), uncertainty quantification analysis or model calibration in which numerous simulations are required. The focus of this paper is to present a rather simple hybrid surrogate modelling (or emulation) strategy to simplify and accelerate a detailed urban drainage simulator (UDS). The proposed surrogate modelling strategy includes: a) identification of the variables to be emulated; b) development of a simplified conceptual model in which every component contributing to the variables identified in step (a) is replaced by a function; c) definition of these functions, either based on knowledge about the mechanisms of the simulator, or based on the data produced by the simulator; and finally, d) validation of the results produced by the surrogate model in comparison with the original detailed simulator. Herein, a detailed InfoWorks ICM simulator was selected for surrogate modelling. The case study area was a small urban drainage network in Luxembourg. An emulator was developed to map the rainfall time series, as input, to a storage tank volume and combined sewer overflow (CSO) in the case study network. The results showed that the introduced strategy provides a reliable method to simplify the simulator and reduce its run time significantly. For the specific case study, the emulator was approximately 1300 times faster than the original detailed simulator. For quantification of the emulation error, an ensemble of 500 rainfall scenarios with 1 month duration was generated by application of a multivariate autoregressive model for conditional simulation of rainfall time series. The results produced by the emulator were compared to the ones produced by the simulator. Finally, as an indicator of the emulation error, distributions of Nash-Sutcliffe efficiency (NSE) between the emulator and simulator results for prediction of storage tank volume and CSO flow time series were presented.
45 schema:genre research_article
46 schema:inLanguage en
47 schema:isAccessibleForFree false
48 schema:isPartOf sg:journal.1136873
49 schema:name A Hybrid Surrogate Modelling Strategy for Simplification of Detailed Urban Drainage Simulators
50 schema:pagination 1-16
51 schema:productId N42f7404cccdf4d74a8e403e0e5b47624
52 N4a4b87ffc091416cb950618c664c1596
53 N88577c63467d448998428112c0d9db81
54 schema:sameAs https://app.dimensions.ai/details/publication/pub.1110448863
55 https://doi.org/10.1007/s11269-018-2157-4
56 schema:sdDatePublished 2019-04-11T08:21
57 schema:sdLicense https://scigraph.springernature.com/explorer/license/
58 schema:sdPublisher N6cc5e8568dca4cae83b20c6da4ed12a3
59 schema:url https://link.springer.com/10.1007%2Fs11269-018-2157-4
60 sgo:license sg:explorer/license/
61 sgo:sdDataset articles
62 rdf:type schema:ScholarlyArticle
63 N05ddebced8e84edb9df4ee2f779f58e9 schema:name RTC4Water, Belval, Luxembourg
64 rdf:type schema:Organization
65 N0bff9e7abd2a4949a1026c07f781c5df rdf:first sg:person.014333162025.76
66 rdf:rest N735acc7c5a7c4448b06bf858a0bf7290
67 N42f7404cccdf4d74a8e403e0e5b47624 schema:name readcube_id
68 schema:value d224dc7be82fb3fc5ddff4f9d3bfe4ece0eeb61e46640f222fbbd76c6bfc44af
69 rdf:type schema:PropertyValue
70 N4a4b87ffc091416cb950618c664c1596 schema:name doi
71 schema:value 10.1007/s11269-018-2157-4
72 rdf:type schema:PropertyValue
73 N52d68f041dc549b5b3d673c8fa3ca24e rdf:first sg:person.0774355534.14
74 rdf:rest N60ec70a0d1864579b549177d3c785422
75 N57a090c51fe94c46b41467389641be3e rdf:first sg:person.016103460004.14
76 rdf:rest N52d68f041dc549b5b3d673c8fa3ca24e
77 N60ec70a0d1864579b549177d3c785422 rdf:first sg:person.015143750055.31
78 rdf:rest Nc9aa2d4426434bf9ad5a886e9c72beea
79 N6cc5e8568dca4cae83b20c6da4ed12a3 schema:name Springer Nature - SN SciGraph project
80 rdf:type schema:Organization
81 N735acc7c5a7c4448b06bf858a0bf7290 rdf:first sg:person.011517660707.69
82 rdf:rest rdf:nil
83 N88577c63467d448998428112c0d9db81 schema:name dimensions_id
84 schema:value pub.1110448863
85 rdf:type schema:PropertyValue
86 Nc9aa2d4426434bf9ad5a886e9c72beea rdf:first sg:person.016260226137.52
87 rdf:rest N0bff9e7abd2a4949a1026c07f781c5df
88 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
89 schema:name Information and Computing Sciences
90 rdf:type schema:DefinedTerm
91 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
92 schema:name Artificial Intelligence and Image Processing
93 rdf:type schema:DefinedTerm
94 sg:grant.3790860 http://pending.schema.org/fundedItem sg:pub.10.1007/s11269-018-2157-4
95 rdf:type schema:MonetaryGrant
96 sg:journal.1136873 schema:issn 0920-4741
97 1573-1650
98 schema:name Water Resources Management
99 rdf:type schema:Periodical
100 sg:person.011517660707.69 schema:affiliation https://www.grid.ac/institutes/grid.6385.8
101 schema:familyName Clemens
102 schema:givenName Francois
103 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011517660707.69
104 rdf:type schema:Person
105 sg:person.014333162025.76 schema:affiliation N05ddebced8e84edb9df4ee2f779f58e9
106 schema:familyName Schutz
107 schema:givenName Georges
108 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014333162025.76
109 rdf:type schema:Person
110 sg:person.015143750055.31 schema:affiliation https://www.grid.ac/institutes/grid.4241.3
111 schema:familyName Bellos
112 schema:givenName Vasilis
113 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015143750055.31
114 rdf:type schema:Person
115 sg:person.016103460004.14 schema:affiliation https://www.grid.ac/institutes/grid.5292.c
116 schema:familyName Mahmoodian
117 schema:givenName Mahmood
118 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016103460004.14
119 rdf:type schema:Person
120 sg:person.016260226137.52 schema:affiliation https://www.grid.ac/institutes/grid.423669.c
121 schema:familyName Leopold
122 schema:givenName Ulrich
123 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016260226137.52
124 rdf:type schema:Person
125 sg:person.0774355534.14 schema:affiliation https://www.grid.ac/institutes/grid.418656.8
126 schema:familyName Carbajal
127 schema:givenName Juan Pablo
128 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0774355534.14
129 rdf:type schema:Person
130 sg:pub.10.1007/s10898-004-0570-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004246579
131 https://doi.org/10.1007/s10898-004-0570-0
132 rdf:type schema:CreativeWork
133 sg:pub.10.1007/s11269-011-9852-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006572371
134 https://doi.org/10.1007/s11269-011-9852-8
135 rdf:type schema:CreativeWork
136 sg:pub.10.1007/s11269-013-0468-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1015382397
137 https://doi.org/10.1007/s11269-013-0468-z
138 rdf:type schema:CreativeWork
139 sg:pub.10.1007/s11269-016-1337-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021950555
140 https://doi.org/10.1007/s11269-016-1337-3
141 rdf:type schema:CreativeWork
142 sg:pub.10.1007/s11269-016-1474-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049772366
143 https://doi.org/10.1007/s11269-016-1474-8
144 rdf:type schema:CreativeWork
145 sg:pub.10.1007/s11269-018-1959-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101551230
146 https://doi.org/10.1007/s11269-018-1959-8
147 rdf:type schema:CreativeWork
148 https://doi.org/10.1002/2013wr015119 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009670205
149 rdf:type schema:CreativeWork
150 https://doi.org/10.1002/2015wr016967 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019297026
151 rdf:type schema:CreativeWork
152 https://doi.org/10.1016/b978-0-444-59520-1.50123-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047505811
153 rdf:type schema:CreativeWork
154 https://doi.org/10.1016/j.envsoft.2004.02.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046986068
155 rdf:type schema:CreativeWork
156 https://doi.org/10.1016/j.envsoft.2012.12.008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044827661
157 rdf:type schema:CreativeWork
158 https://doi.org/10.1016/j.envsoft.2013.12.018 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029383520
159 rdf:type schema:CreativeWork
160 https://doi.org/10.1016/j.envsoft.2017.02.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1083891686
161 rdf:type schema:CreativeWork
162 https://doi.org/10.1016/j.envsoft.2017.02.030 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084525934
163 rdf:type schema:CreativeWork
164 https://doi.org/10.1016/j.jclepro.2018.01.139 schema:sameAs https://app.dimensions.ai/details/publication/pub.1100726273
165 rdf:type schema:CreativeWork
166 https://doi.org/10.1016/j.jconhyd.2016.01.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002292935
167 rdf:type schema:CreativeWork
168 https://doi.org/10.1016/j.jhydrol.2004.08.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000322002
169 rdf:type schema:CreativeWork
170 https://doi.org/10.1016/j.jhydrol.2016.04.056 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039384126
171 rdf:type schema:CreativeWork
172 https://doi.org/10.1016/j.ress.2005.11.025 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029305638
173 rdf:type schema:CreativeWork
174 https://doi.org/10.1016/j.watres.2007.07.038 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043901869
175 rdf:type schema:CreativeWork
176 https://doi.org/10.1016/j.watres.2013.03.051 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009163907
177 rdf:type schema:CreativeWork
178 https://doi.org/10.1021/acs.est.6b04267 schema:sameAs https://app.dimensions.ai/details/publication/pub.1079402564
179 rdf:type schema:CreativeWork
180 https://doi.org/10.1029/2011wr011527 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009578628
181 rdf:type schema:CreativeWork
182 https://doi.org/10.1061/(asce)0733-9496(1999)125:1(3) schema:sameAs https://app.dimensions.ai/details/publication/pub.1057605726
183 rdf:type schema:CreativeWork
184 https://doi.org/10.1080/1573062x.2013.806560 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018259715
185 rdf:type schema:CreativeWork
186 https://doi.org/10.1080/1573062x.2013.820332 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013300927
187 rdf:type schema:CreativeWork
188 https://doi.org/10.1109/uksim.2013.137 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093834834
189 rdf:type schema:CreativeWork
190 https://doi.org/10.1137/s1064827502418768 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062884061
191 rdf:type schema:CreativeWork
192 https://doi.org/10.1177/003754977502400606 schema:sameAs https://app.dimensions.ai/details/publication/pub.1063683617
193 rdf:type schema:CreativeWork
194 https://doi.org/10.2118/99446-pa schema:sameAs https://app.dimensions.ai/details/publication/pub.1068963581
195 rdf:type schema:CreativeWork
196 https://doi.org/10.2166/hydro.2009.151 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069134738
197 rdf:type schema:CreativeWork
198 https://doi.org/10.2166/wst.2002.0066 schema:sameAs https://app.dimensions.ai/details/publication/pub.1075017091
199 rdf:type schema:CreativeWork
200 https://doi.org/10.2166/wst.2002.0628 schema:sameAs https://app.dimensions.ai/details/publication/pub.1075027503
201 rdf:type schema:CreativeWork
202 https://doi.org/10.2166/wst.2010.382 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069144061
203 rdf:type schema:CreativeWork
204 https://doi.org/10.2166/wst.2011.058 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069144481
205 rdf:type schema:CreativeWork
206 https://doi.org/10.2166/wst.2013.253 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069146214
207 rdf:type schema:CreativeWork
208 https://doi.org/10.2514/1.36043 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025694783
209 rdf:type schema:CreativeWork
210 https://doi.org/10.2514/2.1570 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008038063
211 rdf:type schema:CreativeWork
212 https://www.grid.ac/institutes/grid.418656.8 schema:alternateName Swiss Federal Institute of Aquatic Science and Technology
213 schema:name Swiss Federal Institute of Aquatic Science and Technology, Eawag, Dübendorf, Switzerland
214 rdf:type schema:Organization
215 https://www.grid.ac/institutes/grid.423669.c schema:alternateName Luxembourg Institute of Science and Technology
216 schema:name Luxembourg Institute of Science and Technology, Belvaux, Luxembourg
217 rdf:type schema:Organization
218 https://www.grid.ac/institutes/grid.4241.3 schema:alternateName National Technical University of Athens
219 schema:name CH2M, Swindon, UK
220 National Technical University of Athens, Athens, Greece
221 rdf:type schema:Organization
222 https://www.grid.ac/institutes/grid.5292.c schema:alternateName Delft University of Technology
223 schema:name Delft University of Technology, Delft, The Netherlands
224 Luxembourg Institute of Science and Technology, Belvaux, Luxembourg
225 rdf:type schema:Organization
226 https://www.grid.ac/institutes/grid.6385.8 schema:alternateName Deltares
227 schema:name Delft University of Technology, Delft, The Netherlands
228 Deltares, Delft, The Netherlands
229 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...