Emulation of a Detailed Urban Drainage Simulator to Be Applied for Short-Term Predictions View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2019

AUTHORS

Mahmood Mahmoodian , J. A. Torres-Matallana , Ulrich Leopold , Georges Schutz , Francois Clemens

ABSTRACT

The challenge of this study is to investigate on applicability of a data-driven Gaussian Process Emulator (GPE) technique to develop a surrogate model for a computationally expensive and detailed urban drainage simulator. The novelty is the consideration of (short) time series for the simulation inputs and outputs. Such simulation setup is interesting in applications such as Model Predictive Control (MPC) in which numerous, fast and frequent simulation results are required. Here, an emulator is developed to predict a storage tank’s volume in a small case study in Luxembourg. Three main inputs are considered as the GPE’s parameters: initial volume in the tank, the level in which the outlet pump of the tank must start to work, and the time series of expected rainfall in the upcoming 2 h. The output of interest is the total volume of the storage tank for the next 24 h. A dataset of 2000 input-output scenarios were produced using different possible combinations of the inputs and running the detailed simulator (InfoWorks® ICM). 80% of the dataset were applied to train the emulator and 20% to validate the results. Distributions of Nash-Sutcliffe efficiency and Volumetric Efficiency are presented as indicators for quantification of the emulation error. Based on the preliminary results, it can be concluded that the introduced technique is able to reduce the simulations runtime significantly while imposing some inevitable accuracy cost. More investigation is required to validate the more generic applicability of this technique for multiple outputs and interactions between different urban drainage components. More... »

PAGES

592-596

Book

TITLE

New Trends in Urban Drainage Modelling

ISBN

978-3-319-99866-4
978-3-319-99867-1

From Grant

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-99867-1_102

DOI

http://dx.doi.org/10.1007/978-3-319-99867-1_102

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1106473423


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": "Luxembourg Institute of Science and Technology", 
          "id": "https://www.grid.ac/institutes/grid.423669.c", 
          "name": [
            "Luxembourg Institute of Science and Technology (LIST)", 
            "Technical University of Delft"
          ], 
          "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": "Wageningen University & Research", 
          "id": "https://www.grid.ac/institutes/grid.4818.5", 
          "name": [
            "Luxembourg Institute of Science and Technology (LIST)", 
            "Wageningen University"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Torres-Matallana", 
        "givenName": "J. A.", 
        "id": "sg:person.012006117764.55", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012006117764.55"
        ], 
        "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 (LIST)"
          ], 
          "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"
          ], 
          "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": [
            "Technical University of Delft", 
            "Deltares"
          ], 
          "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.jcp.2016.02.046", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018731497"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jcp.2016.02.046", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018731497"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jcp.2016.02.046", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018731497"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jcp.2016.02.046", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018731497"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1201/b12728", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095904112"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019", 
    "datePublishedReg": "2019-01-01", 
    "description": "The challenge of this study is to investigate on applicability of a data-driven Gaussian Process Emulator (GPE) technique to develop a surrogate model for a computationally expensive and detailed urban drainage simulator. The novelty is the consideration of (short) time series for the simulation inputs and outputs. Such simulation setup is interesting in applications such as Model Predictive Control (MPC) in which numerous, fast and frequent simulation results are required. Here, an emulator is developed to predict a storage tank\u2019s volume in a small case study in Luxembourg. Three main inputs are considered as the GPE\u2019s parameters: initial volume in the tank, the level in which the outlet pump of the tank must start to work, and the time series of expected rainfall in the upcoming 2 h. The output of interest is the total volume of the storage tank for the next 24 h. A dataset of 2000 input-output scenarios were produced using different possible combinations of the inputs and running the detailed simulator (InfoWorks\u00ae ICM). 80% of the dataset were applied to train the emulator and 20% to validate the results. Distributions of Nash-Sutcliffe efficiency and Volumetric Efficiency are presented as indicators for quantification of the emulation error. Based on the preliminary results, it can be concluded that the introduced technique is able to reduce the simulations runtime significantly while imposing some inevitable accuracy cost. More investigation is required to validate the more generic applicability of this technique for multiple outputs and interactions between different urban drainage components.", 
    "editor": [
      {
        "familyName": "Mannina", 
        "givenName": "Giorgio", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-319-99867-1_102", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.3790860", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": {
      "isbn": [
        "978-3-319-99866-4", 
        "978-3-319-99867-1"
      ], 
      "name": "New Trends in Urban Drainage Modelling", 
      "type": "Book"
    }, 
    "name": "Emulation of a Detailed Urban Drainage Simulator to Be Applied for Short-Term Predictions", 
    "pagination": "592-596", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-319-99867-1_102"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "f5281f5b69f330374daf2e522487f0a91c1ee18e2b05a715f18bf84ddbe3ea00"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1106473423"
        ]
      }
    ], 
    "publisher": {
      "location": "Cham", 
      "name": "Springer International Publishing", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-319-99867-1_102", 
      "https://app.dimensions.ai/details/publication/pub.1106473423"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2019-04-15T16:57", 
    "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/v90/0000000001_0000000264/records_8675_00000605.jsonl", 
    "type": "Chapter", 
    "url": "http://link.springer.com/10.1007/978-3-319-99867-1_102"
  }
]
 

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.10.1007/978-3-319-99867-1_102'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-99867-1_102'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-99867-1_102'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-99867-1_102'


 

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

112 TRIPLES      23 PREDICATES      29 URIs      20 LITERALS      8 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-319-99867-1_102 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N3c63da967d2e4d56a0f4c27ed3af8beb
4 schema:citation https://doi.org/10.1016/j.jcp.2016.02.046
5 https://doi.org/10.1201/b12728
6 schema:datePublished 2019
7 schema:datePublishedReg 2019-01-01
8 schema:description The challenge of this study is to investigate on applicability of a data-driven Gaussian Process Emulator (GPE) technique to develop a surrogate model for a computationally expensive and detailed urban drainage simulator. The novelty is the consideration of (short) time series for the simulation inputs and outputs. Such simulation setup is interesting in applications such as Model Predictive Control (MPC) in which numerous, fast and frequent simulation results are required. Here, an emulator is developed to predict a storage tank’s volume in a small case study in Luxembourg. Three main inputs are considered as the GPE’s parameters: initial volume in the tank, the level in which the outlet pump of the tank must start to work, and the time series of expected rainfall in the upcoming 2 h. The output of interest is the total volume of the storage tank for the next 24 h. A dataset of 2000 input-output scenarios were produced using different possible combinations of the inputs and running the detailed simulator (InfoWorks® ICM). 80% of the dataset were applied to train the emulator and 20% to validate the results. Distributions of Nash-Sutcliffe efficiency and Volumetric Efficiency are presented as indicators for quantification of the emulation error. Based on the preliminary results, it can be concluded that the introduced technique is able to reduce the simulations runtime significantly while imposing some inevitable accuracy cost. More investigation is required to validate the more generic applicability of this technique for multiple outputs and interactions between different urban drainage components.
9 schema:editor Na8436b7978a94ca699f2ccc1e1e31780
10 schema:genre chapter
11 schema:inLanguage en
12 schema:isAccessibleForFree false
13 schema:isPartOf N7e8b85beac294546babade5313bedc04
14 schema:name Emulation of a Detailed Urban Drainage Simulator to Be Applied for Short-Term Predictions
15 schema:pagination 592-596
16 schema:productId N28eddcc25d5340b09181ae8ef67aaa22
17 N4d7ec40e3c4d4ef782684e008e2643d7
18 N89e0cce13ffb4323977e656cdd9d8233
19 schema:publisher N768a6ce3d5864531b799a82cfa96ff1d
20 schema:sameAs https://app.dimensions.ai/details/publication/pub.1106473423
21 https://doi.org/10.1007/978-3-319-99867-1_102
22 schema:sdDatePublished 2019-04-15T16:57
23 schema:sdLicense https://scigraph.springernature.com/explorer/license/
24 schema:sdPublisher N11d63f13ed444f69b7b1c552077358fd
25 schema:url http://link.springer.com/10.1007/978-3-319-99867-1_102
26 sgo:license sg:explorer/license/
27 sgo:sdDataset chapters
28 rdf:type schema:Chapter
29 N11d63f13ed444f69b7b1c552077358fd schema:name Springer Nature - SN SciGraph project
30 rdf:type schema:Organization
31 N2380077c79d44f1290650a4cb77eb5dd schema:name RTC4Water
32 rdf:type schema:Organization
33 N28eddcc25d5340b09181ae8ef67aaa22 schema:name dimensions_id
34 schema:value pub.1106473423
35 rdf:type schema:PropertyValue
36 N3c63da967d2e4d56a0f4c27ed3af8beb rdf:first sg:person.016103460004.14
37 rdf:rest Nd2383ccee9ef4f92bf7404f68fd37b59
38 N4d7ec40e3c4d4ef782684e008e2643d7 schema:name doi
39 schema:value 10.1007/978-3-319-99867-1_102
40 rdf:type schema:PropertyValue
41 N607658d16b154359a8ee2e300d6d31a2 rdf:first sg:person.011517660707.69
42 rdf:rest rdf:nil
43 N66e0929f4d4a4aa899e3144aa16d1bb3 rdf:first sg:person.014333162025.76
44 rdf:rest N607658d16b154359a8ee2e300d6d31a2
45 N768a6ce3d5864531b799a82cfa96ff1d schema:location Cham
46 schema:name Springer International Publishing
47 rdf:type schema:Organisation
48 N7e8b85beac294546babade5313bedc04 schema:isbn 978-3-319-99866-4
49 978-3-319-99867-1
50 schema:name New Trends in Urban Drainage Modelling
51 rdf:type schema:Book
52 N89e0cce13ffb4323977e656cdd9d8233 schema:name readcube_id
53 schema:value f5281f5b69f330374daf2e522487f0a91c1ee18e2b05a715f18bf84ddbe3ea00
54 rdf:type schema:PropertyValue
55 Na8436b7978a94ca699f2ccc1e1e31780 rdf:first Nff694c2a4d074a0ab27e9d4fa662d486
56 rdf:rest rdf:nil
57 Nb2a7c0994dd44c61bdcd9ecf7428918a rdf:first sg:person.016260226137.52
58 rdf:rest N66e0929f4d4a4aa899e3144aa16d1bb3
59 Nd2383ccee9ef4f92bf7404f68fd37b59 rdf:first sg:person.012006117764.55
60 rdf:rest Nb2a7c0994dd44c61bdcd9ecf7428918a
61 Nff694c2a4d074a0ab27e9d4fa662d486 schema:familyName Mannina
62 schema:givenName Giorgio
63 rdf:type schema:Person
64 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
65 schema:name Information and Computing Sciences
66 rdf:type schema:DefinedTerm
67 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
68 schema:name Artificial Intelligence and Image Processing
69 rdf:type schema:DefinedTerm
70 sg:grant.3790860 http://pending.schema.org/fundedItem sg:pub.10.1007/978-3-319-99867-1_102
71 rdf:type schema:MonetaryGrant
72 sg:person.011517660707.69 schema:affiliation https://www.grid.ac/institutes/grid.6385.8
73 schema:familyName Clemens
74 schema:givenName Francois
75 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011517660707.69
76 rdf:type schema:Person
77 sg:person.012006117764.55 schema:affiliation https://www.grid.ac/institutes/grid.4818.5
78 schema:familyName Torres-Matallana
79 schema:givenName J. A.
80 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012006117764.55
81 rdf:type schema:Person
82 sg:person.014333162025.76 schema:affiliation N2380077c79d44f1290650a4cb77eb5dd
83 schema:familyName Schutz
84 schema:givenName Georges
85 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014333162025.76
86 rdf:type schema:Person
87 sg:person.016103460004.14 schema:affiliation https://www.grid.ac/institutes/grid.423669.c
88 schema:familyName Mahmoodian
89 schema:givenName Mahmood
90 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016103460004.14
91 rdf:type schema:Person
92 sg:person.016260226137.52 schema:affiliation https://www.grid.ac/institutes/grid.423669.c
93 schema:familyName Leopold
94 schema:givenName Ulrich
95 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016260226137.52
96 rdf:type schema:Person
97 https://doi.org/10.1016/j.jcp.2016.02.046 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018731497
98 rdf:type schema:CreativeWork
99 https://doi.org/10.1201/b12728 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095904112
100 rdf:type schema:CreativeWork
101 https://www.grid.ac/institutes/grid.423669.c schema:alternateName Luxembourg Institute of Science and Technology
102 schema:name Luxembourg Institute of Science and Technology (LIST)
103 Technical University of Delft
104 rdf:type schema:Organization
105 https://www.grid.ac/institutes/grid.4818.5 schema:alternateName Wageningen University & Research
106 schema:name Luxembourg Institute of Science and Technology (LIST)
107 Wageningen University
108 rdf:type schema:Organization
109 https://www.grid.ac/institutes/grid.6385.8 schema:alternateName Deltares
110 schema:name Deltares
111 Technical University of Delft
112 rdf:type schema:Organization
 




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


...