Assessment of sustainability index for rural water management using ANN
The current study proposes a sustainability index (SI) measure based on artificial neural networks (ANN) and globally accepted parameters. Some of the available methods for SI measurement are multi-criteria analysis, external costs, energy analysis, and ecological footprint methods. However, validit...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
R. Raghavendra Kumar [verfasserIn] Gaurav Kumar [verfasserIn] Rajiv Gupta [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2022 |
|---|
| Schlagwörter: |
|---|
| Übergeordnetes Werk: |
In: Water Supply - IWA Publishing, 2021, 22(2022), 2, Seite 1421-1433 |
|---|---|
| Übergeordnetes Werk: |
volume:22 ; year:2022 ; number:2 ; pages:1421-1433 |
| Links: |
Link aufrufen |
|---|
| DOI / URN: |
10.2166/ws.2021.346 |
|---|
| Katalog-ID: |
DOAJ038306727 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | DOAJ038306727 | ||
| 003 | DE-627 | ||
| 005 | 20230308021011.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 230227s2022 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.2166/ws.2021.346 |2 doi | |
| 035 | |a (DE-627)DOAJ038306727 | ||
| 035 | |a (DE-599)DOAJa5ad139bbf004718a129704bd86d6015 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 050 | 0 | |a TD201-500 | |
| 050 | 0 | |a TC401-506 | |
| 100 | 0 | |a R. Raghavendra Kumar |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Assessment of sustainability index for rural water management using ANN |
| 264 | 1 | |c 2022 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 520 | |a The current study proposes a sustainability index (SI) measure based on artificial neural networks (ANN) and globally accepted parameters. Some of the available methods for SI measurement are multi-criteria analysis, external costs, energy analysis, and ecological footprint methods. However, validity remains a concern due to a system's needs, criteria, and requirements. Generally, sustainability is assessed in economic, environmental, and social issues, which varies across regions and countries. Most of the studies accept sub-indices but to a limited extent. Therefore, the proposed study develops an SI evaluation method based on the idea of multi-sustainability incorporating operations, institutions, risks, and climate factors besides economic, environmental, and social issues. All these issues might not be applicable to a single project but may help to develop a complete index when applied. The present study considered different scenarios in building a method to calculate SI using ANN. The results obtained by the ANN model for various input parameters helped to identify the best water conservation strategy. Sensitivity analysis was also performed to determine the uncertainty contribution/significance of the input variables for the water scarcity in the study region. The developed model in the study is tested on a rural water management system. HIGHLIGHTS Numerous approaches were performed regarding Sustainability Index (SI), but in the current study SI using multiple factors was assessed.; Artificial Neural Networks (ANN) model was trained and developed for future scenarios.; Current research work has a case study application on a community in a village.; Determination of SI was helpful in following and setting up a rainwater harvesting method at a selected village.; | ||
| 650 | 4 | |a artificial intelligence | |
| 650 | 4 | |a rural areas | |
| 650 | 4 | |a scenario development | |
| 650 | 4 | |a sustainability index | |
| 650 | 4 | |a water resource management | |
| 653 | 0 | |a Water supply for domestic and industrial purposes | |
| 653 | 0 | |a River, lake, and water-supply engineering (General) | |
| 700 | 0 | |a Gaurav Kumar |e verfasserin |4 aut | |
| 700 | 0 | |a Rajiv Gupta |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i In |t Water Supply |d IWA Publishing, 2021 |g 22(2022), 2, Seite 1421-1433 |w (DE-627)34988062X |w (DE-600)2082134-7 |x 16070798 |7 nnns |
| 773 | 1 | 8 | |g volume:22 |g year:2022 |g number:2 |g pages:1421-1433 |
| 856 | 4 | 0 | |u https://doi.org/10.2166/ws.2021.346 |z kostenfrei |
| 856 | 4 | 0 | |u https://doaj.org/article/a5ad139bbf004718a129704bd86d6015 |z kostenfrei |
| 856 | 4 | 0 | |u http://ws.iwaponline.com/content/22/2/1421 |z kostenfrei |
| 856 | 4 | 2 | |u https://doaj.org/toc/1606-9749 |y Journal toc |z kostenfrei |
| 856 | 4 | 2 | |u https://doaj.org/toc/1607-0798 |y Journal toc |z kostenfrei |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_DOAJ | ||
| 912 | |a GBV_ILN_2027 | ||
| 951 | |a AR | ||
| 952 | |d 22 |j 2022 |e 2 |h 1421-1433 | ||
| author_variant |
r r k rrk g k gk r g rg |
|---|---|
| matchkey_str |
article:16070798:2022----::sesetfutiaiiynefruawtr |
| hierarchy_sort_str |
2022 |
| callnumber-subject-code |
TD |
| publishDate |
2022 |
| allfields |
10.2166/ws.2021.346 doi (DE-627)DOAJ038306727 (DE-599)DOAJa5ad139bbf004718a129704bd86d6015 DE-627 ger DE-627 rakwb eng TD201-500 TC401-506 R. Raghavendra Kumar verfasserin aut Assessment of sustainability index for rural water management using ANN 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The current study proposes a sustainability index (SI) measure based on artificial neural networks (ANN) and globally accepted parameters. Some of the available methods for SI measurement are multi-criteria analysis, external costs, energy analysis, and ecological footprint methods. However, validity remains a concern due to a system's needs, criteria, and requirements. Generally, sustainability is assessed in economic, environmental, and social issues, which varies across regions and countries. Most of the studies accept sub-indices but to a limited extent. Therefore, the proposed study develops an SI evaluation method based on the idea of multi-sustainability incorporating operations, institutions, risks, and climate factors besides economic, environmental, and social issues. All these issues might not be applicable to a single project but may help to develop a complete index when applied. The present study considered different scenarios in building a method to calculate SI using ANN. The results obtained by the ANN model for various input parameters helped to identify the best water conservation strategy. Sensitivity analysis was also performed to determine the uncertainty contribution/significance of the input variables for the water scarcity in the study region. The developed model in the study is tested on a rural water management system. HIGHLIGHTS Numerous approaches were performed regarding Sustainability Index (SI), but in the current study SI using multiple factors was assessed.; Artificial Neural Networks (ANN) model was trained and developed for future scenarios.; Current research work has a case study application on a community in a village.; Determination of SI was helpful in following and setting up a rainwater harvesting method at a selected village.; artificial intelligence rural areas scenario development sustainability index water resource management Water supply for domestic and industrial purposes River, lake, and water-supply engineering (General) Gaurav Kumar verfasserin aut Rajiv Gupta verfasserin aut In Water Supply IWA Publishing, 2021 22(2022), 2, Seite 1421-1433 (DE-627)34988062X (DE-600)2082134-7 16070798 nnns volume:22 year:2022 number:2 pages:1421-1433 https://doi.org/10.2166/ws.2021.346 kostenfrei https://doaj.org/article/a5ad139bbf004718a129704bd86d6015 kostenfrei http://ws.iwaponline.com/content/22/2/1421 kostenfrei https://doaj.org/toc/1606-9749 Journal toc kostenfrei https://doaj.org/toc/1607-0798 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_2027 AR 22 2022 2 1421-1433 |
| spelling |
10.2166/ws.2021.346 doi (DE-627)DOAJ038306727 (DE-599)DOAJa5ad139bbf004718a129704bd86d6015 DE-627 ger DE-627 rakwb eng TD201-500 TC401-506 R. Raghavendra Kumar verfasserin aut Assessment of sustainability index for rural water management using ANN 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The current study proposes a sustainability index (SI) measure based on artificial neural networks (ANN) and globally accepted parameters. Some of the available methods for SI measurement are multi-criteria analysis, external costs, energy analysis, and ecological footprint methods. However, validity remains a concern due to a system's needs, criteria, and requirements. Generally, sustainability is assessed in economic, environmental, and social issues, which varies across regions and countries. Most of the studies accept sub-indices but to a limited extent. Therefore, the proposed study develops an SI evaluation method based on the idea of multi-sustainability incorporating operations, institutions, risks, and climate factors besides economic, environmental, and social issues. All these issues might not be applicable to a single project but may help to develop a complete index when applied. The present study considered different scenarios in building a method to calculate SI using ANN. The results obtained by the ANN model for various input parameters helped to identify the best water conservation strategy. Sensitivity analysis was also performed to determine the uncertainty contribution/significance of the input variables for the water scarcity in the study region. The developed model in the study is tested on a rural water management system. HIGHLIGHTS Numerous approaches were performed regarding Sustainability Index (SI), but in the current study SI using multiple factors was assessed.; Artificial Neural Networks (ANN) model was trained and developed for future scenarios.; Current research work has a case study application on a community in a village.; Determination of SI was helpful in following and setting up a rainwater harvesting method at a selected village.; artificial intelligence rural areas scenario development sustainability index water resource management Water supply for domestic and industrial purposes River, lake, and water-supply engineering (General) Gaurav Kumar verfasserin aut Rajiv Gupta verfasserin aut In Water Supply IWA Publishing, 2021 22(2022), 2, Seite 1421-1433 (DE-627)34988062X (DE-600)2082134-7 16070798 nnns volume:22 year:2022 number:2 pages:1421-1433 https://doi.org/10.2166/ws.2021.346 kostenfrei https://doaj.org/article/a5ad139bbf004718a129704bd86d6015 kostenfrei http://ws.iwaponline.com/content/22/2/1421 kostenfrei https://doaj.org/toc/1606-9749 Journal toc kostenfrei https://doaj.org/toc/1607-0798 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_2027 AR 22 2022 2 1421-1433 |
| allfields_unstemmed |
10.2166/ws.2021.346 doi (DE-627)DOAJ038306727 (DE-599)DOAJa5ad139bbf004718a129704bd86d6015 DE-627 ger DE-627 rakwb eng TD201-500 TC401-506 R. Raghavendra Kumar verfasserin aut Assessment of sustainability index for rural water management using ANN 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The current study proposes a sustainability index (SI) measure based on artificial neural networks (ANN) and globally accepted parameters. Some of the available methods for SI measurement are multi-criteria analysis, external costs, energy analysis, and ecological footprint methods. However, validity remains a concern due to a system's needs, criteria, and requirements. Generally, sustainability is assessed in economic, environmental, and social issues, which varies across regions and countries. Most of the studies accept sub-indices but to a limited extent. Therefore, the proposed study develops an SI evaluation method based on the idea of multi-sustainability incorporating operations, institutions, risks, and climate factors besides economic, environmental, and social issues. All these issues might not be applicable to a single project but may help to develop a complete index when applied. The present study considered different scenarios in building a method to calculate SI using ANN. The results obtained by the ANN model for various input parameters helped to identify the best water conservation strategy. Sensitivity analysis was also performed to determine the uncertainty contribution/significance of the input variables for the water scarcity in the study region. The developed model in the study is tested on a rural water management system. HIGHLIGHTS Numerous approaches were performed regarding Sustainability Index (SI), but in the current study SI using multiple factors was assessed.; Artificial Neural Networks (ANN) model was trained and developed for future scenarios.; Current research work has a case study application on a community in a village.; Determination of SI was helpful in following and setting up a rainwater harvesting method at a selected village.; artificial intelligence rural areas scenario development sustainability index water resource management Water supply for domestic and industrial purposes River, lake, and water-supply engineering (General) Gaurav Kumar verfasserin aut Rajiv Gupta verfasserin aut In Water Supply IWA Publishing, 2021 22(2022), 2, Seite 1421-1433 (DE-627)34988062X (DE-600)2082134-7 16070798 nnns volume:22 year:2022 number:2 pages:1421-1433 https://doi.org/10.2166/ws.2021.346 kostenfrei https://doaj.org/article/a5ad139bbf004718a129704bd86d6015 kostenfrei http://ws.iwaponline.com/content/22/2/1421 kostenfrei https://doaj.org/toc/1606-9749 Journal toc kostenfrei https://doaj.org/toc/1607-0798 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_2027 AR 22 2022 2 1421-1433 |
| allfieldsGer |
10.2166/ws.2021.346 doi (DE-627)DOAJ038306727 (DE-599)DOAJa5ad139bbf004718a129704bd86d6015 DE-627 ger DE-627 rakwb eng TD201-500 TC401-506 R. Raghavendra Kumar verfasserin aut Assessment of sustainability index for rural water management using ANN 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The current study proposes a sustainability index (SI) measure based on artificial neural networks (ANN) and globally accepted parameters. Some of the available methods for SI measurement are multi-criteria analysis, external costs, energy analysis, and ecological footprint methods. However, validity remains a concern due to a system's needs, criteria, and requirements. Generally, sustainability is assessed in economic, environmental, and social issues, which varies across regions and countries. Most of the studies accept sub-indices but to a limited extent. Therefore, the proposed study develops an SI evaluation method based on the idea of multi-sustainability incorporating operations, institutions, risks, and climate factors besides economic, environmental, and social issues. All these issues might not be applicable to a single project but may help to develop a complete index when applied. The present study considered different scenarios in building a method to calculate SI using ANN. The results obtained by the ANN model for various input parameters helped to identify the best water conservation strategy. Sensitivity analysis was also performed to determine the uncertainty contribution/significance of the input variables for the water scarcity in the study region. The developed model in the study is tested on a rural water management system. HIGHLIGHTS Numerous approaches were performed regarding Sustainability Index (SI), but in the current study SI using multiple factors was assessed.; Artificial Neural Networks (ANN) model was trained and developed for future scenarios.; Current research work has a case study application on a community in a village.; Determination of SI was helpful in following and setting up a rainwater harvesting method at a selected village.; artificial intelligence rural areas scenario development sustainability index water resource management Water supply for domestic and industrial purposes River, lake, and water-supply engineering (General) Gaurav Kumar verfasserin aut Rajiv Gupta verfasserin aut In Water Supply IWA Publishing, 2021 22(2022), 2, Seite 1421-1433 (DE-627)34988062X (DE-600)2082134-7 16070798 nnns volume:22 year:2022 number:2 pages:1421-1433 https://doi.org/10.2166/ws.2021.346 kostenfrei https://doaj.org/article/a5ad139bbf004718a129704bd86d6015 kostenfrei http://ws.iwaponline.com/content/22/2/1421 kostenfrei https://doaj.org/toc/1606-9749 Journal toc kostenfrei https://doaj.org/toc/1607-0798 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_2027 AR 22 2022 2 1421-1433 |
| allfieldsSound |
10.2166/ws.2021.346 doi (DE-627)DOAJ038306727 (DE-599)DOAJa5ad139bbf004718a129704bd86d6015 DE-627 ger DE-627 rakwb eng TD201-500 TC401-506 R. Raghavendra Kumar verfasserin aut Assessment of sustainability index for rural water management using ANN 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The current study proposes a sustainability index (SI) measure based on artificial neural networks (ANN) and globally accepted parameters. Some of the available methods for SI measurement are multi-criteria analysis, external costs, energy analysis, and ecological footprint methods. However, validity remains a concern due to a system's needs, criteria, and requirements. Generally, sustainability is assessed in economic, environmental, and social issues, which varies across regions and countries. Most of the studies accept sub-indices but to a limited extent. Therefore, the proposed study develops an SI evaluation method based on the idea of multi-sustainability incorporating operations, institutions, risks, and climate factors besides economic, environmental, and social issues. All these issues might not be applicable to a single project but may help to develop a complete index when applied. The present study considered different scenarios in building a method to calculate SI using ANN. The results obtained by the ANN model for various input parameters helped to identify the best water conservation strategy. Sensitivity analysis was also performed to determine the uncertainty contribution/significance of the input variables for the water scarcity in the study region. The developed model in the study is tested on a rural water management system. HIGHLIGHTS Numerous approaches were performed regarding Sustainability Index (SI), but in the current study SI using multiple factors was assessed.; Artificial Neural Networks (ANN) model was trained and developed for future scenarios.; Current research work has a case study application on a community in a village.; Determination of SI was helpful in following and setting up a rainwater harvesting method at a selected village.; artificial intelligence rural areas scenario development sustainability index water resource management Water supply for domestic and industrial purposes River, lake, and water-supply engineering (General) Gaurav Kumar verfasserin aut Rajiv Gupta verfasserin aut In Water Supply IWA Publishing, 2021 22(2022), 2, Seite 1421-1433 (DE-627)34988062X (DE-600)2082134-7 16070798 nnns volume:22 year:2022 number:2 pages:1421-1433 https://doi.org/10.2166/ws.2021.346 kostenfrei https://doaj.org/article/a5ad139bbf004718a129704bd86d6015 kostenfrei http://ws.iwaponline.com/content/22/2/1421 kostenfrei https://doaj.org/toc/1606-9749 Journal toc kostenfrei https://doaj.org/toc/1607-0798 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_2027 AR 22 2022 2 1421-1433 |
| language |
English |
| source |
In Water Supply 22(2022), 2, Seite 1421-1433 volume:22 year:2022 number:2 pages:1421-1433 |
| sourceStr |
In Water Supply 22(2022), 2, Seite 1421-1433 volume:22 year:2022 number:2 pages:1421-1433 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
artificial intelligence rural areas scenario development sustainability index water resource management Water supply for domestic and industrial purposes River, lake, and water-supply engineering (General) |
| isfreeaccess_bool |
true |
| container_title |
Water Supply |
| authorswithroles_txt_mv |
R. Raghavendra Kumar @@aut@@ Gaurav Kumar @@aut@@ Rajiv Gupta @@aut@@ |
| publishDateDaySort_date |
2022-01-01T00:00:00Z |
| hierarchy_top_id |
34988062X |
| id |
DOAJ038306727 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ038306727</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230308021011.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.2166/ws.2021.346</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ038306727</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJa5ad139bbf004718a129704bd86d6015</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TD201-500</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TC401-506</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">R. Raghavendra Kumar</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Assessment of sustainability index for rural water management using ANN</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The current study proposes a sustainability index (SI) measure based on artificial neural networks (ANN) and globally accepted parameters. Some of the available methods for SI measurement are multi-criteria analysis, external costs, energy analysis, and ecological footprint methods. However, validity remains a concern due to a system's needs, criteria, and requirements. Generally, sustainability is assessed in economic, environmental, and social issues, which varies across regions and countries. Most of the studies accept sub-indices but to a limited extent. Therefore, the proposed study develops an SI evaluation method based on the idea of multi-sustainability incorporating operations, institutions, risks, and climate factors besides economic, environmental, and social issues. All these issues might not be applicable to a single project but may help to develop a complete index when applied. The present study considered different scenarios in building a method to calculate SI using ANN. The results obtained by the ANN model for various input parameters helped to identify the best water conservation strategy. Sensitivity analysis was also performed to determine the uncertainty contribution/significance of the input variables for the water scarcity in the study region. The developed model in the study is tested on a rural water management system. HIGHLIGHTS Numerous approaches were performed regarding Sustainability Index (SI), but in the current study SI using multiple factors was assessed.; Artificial Neural Networks (ANN) model was trained and developed for future scenarios.; Current research work has a case study application on a community in a village.; Determination of SI was helpful in following and setting up a rainwater harvesting method at a selected village.;</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">artificial intelligence</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">rural areas</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">scenario development</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">sustainability index</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">water resource management</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Water supply for domestic and industrial purposes</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">River, lake, and water-supply engineering (General)</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Gaurav Kumar</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Rajiv Gupta</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Water Supply</subfield><subfield code="d">IWA Publishing, 2021</subfield><subfield code="g">22(2022), 2, Seite 1421-1433</subfield><subfield code="w">(DE-627)34988062X</subfield><subfield code="w">(DE-600)2082134-7</subfield><subfield code="x">16070798</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:22</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:2</subfield><subfield code="g">pages:1421-1433</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.2166/ws.2021.346</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/a5ad139bbf004718a129704bd86d6015</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://ws.iwaponline.com/content/22/2/1421</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1606-9749</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1607-0798</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">22</subfield><subfield code="j">2022</subfield><subfield code="e">2</subfield><subfield code="h">1421-1433</subfield></datafield></record></collection>
|
| callnumber-first |
T - Technology |
| author |
R. Raghavendra Kumar |
| spellingShingle |
R. Raghavendra Kumar misc TD201-500 misc TC401-506 misc artificial intelligence misc rural areas misc scenario development misc sustainability index misc water resource management misc Water supply for domestic and industrial purposes misc River, lake, and water-supply engineering (General) Assessment of sustainability index for rural water management using ANN |
| authorStr |
R. Raghavendra Kumar |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)34988062X |
| format |
electronic Article |
| delete_txt_mv |
keep |
| author_role |
aut aut aut |
| collection |
DOAJ |
| remote_str |
true |
| callnumber-label |
TD201-500 |
| illustrated |
Not Illustrated |
| issn |
16070798 |
| topic_title |
TD201-500 TC401-506 Assessment of sustainability index for rural water management using ANN artificial intelligence rural areas scenario development sustainability index water resource management |
| topic |
misc TD201-500 misc TC401-506 misc artificial intelligence misc rural areas misc scenario development misc sustainability index misc water resource management misc Water supply for domestic and industrial purposes misc River, lake, and water-supply engineering (General) |
| topic_unstemmed |
misc TD201-500 misc TC401-506 misc artificial intelligence misc rural areas misc scenario development misc sustainability index misc water resource management misc Water supply for domestic and industrial purposes misc River, lake, and water-supply engineering (General) |
| topic_browse |
misc TD201-500 misc TC401-506 misc artificial intelligence misc rural areas misc scenario development misc sustainability index misc water resource management misc Water supply for domestic and industrial purposes misc River, lake, and water-supply engineering (General) |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
cr |
| hierarchy_parent_title |
Water Supply |
| hierarchy_parent_id |
34988062X |
| hierarchy_top_title |
Water Supply |
| isfreeaccess_txt |
true |
| familylinks_str_mv |
(DE-627)34988062X (DE-600)2082134-7 |
| title |
Assessment of sustainability index for rural water management using ANN |
| ctrlnum |
(DE-627)DOAJ038306727 (DE-599)DOAJa5ad139bbf004718a129704bd86d6015 |
| title_full |
Assessment of sustainability index for rural water management using ANN |
| author_sort |
R. Raghavendra Kumar |
| journal |
Water Supply |
| journalStr |
Water Supply |
| callnumber-first-code |
T |
| lang_code |
eng |
| isOA_bool |
true |
| recordtype |
marc |
| publishDateSort |
2022 |
| contenttype_str_mv |
txt |
| container_start_page |
1421 |
| author_browse |
R. Raghavendra Kumar Gaurav Kumar Rajiv Gupta |
| container_volume |
22 |
| class |
TD201-500 TC401-506 |
| format_se |
Elektronische Aufsätze |
| author-letter |
R. Raghavendra Kumar |
| doi_str_mv |
10.2166/ws.2021.346 |
| author2-role |
verfasserin |
| title_sort |
assessment of sustainability index for rural water management using ann |
| callnumber |
TD201-500 |
| title_auth |
Assessment of sustainability index for rural water management using ANN |
| abstract |
The current study proposes a sustainability index (SI) measure based on artificial neural networks (ANN) and globally accepted parameters. Some of the available methods for SI measurement are multi-criteria analysis, external costs, energy analysis, and ecological footprint methods. However, validity remains a concern due to a system's needs, criteria, and requirements. Generally, sustainability is assessed in economic, environmental, and social issues, which varies across regions and countries. Most of the studies accept sub-indices but to a limited extent. Therefore, the proposed study develops an SI evaluation method based on the idea of multi-sustainability incorporating operations, institutions, risks, and climate factors besides economic, environmental, and social issues. All these issues might not be applicable to a single project but may help to develop a complete index when applied. The present study considered different scenarios in building a method to calculate SI using ANN. The results obtained by the ANN model for various input parameters helped to identify the best water conservation strategy. Sensitivity analysis was also performed to determine the uncertainty contribution/significance of the input variables for the water scarcity in the study region. The developed model in the study is tested on a rural water management system. HIGHLIGHTS Numerous approaches were performed regarding Sustainability Index (SI), but in the current study SI using multiple factors was assessed.; Artificial Neural Networks (ANN) model was trained and developed for future scenarios.; Current research work has a case study application on a community in a village.; Determination of SI was helpful in following and setting up a rainwater harvesting method at a selected village.; |
| abstractGer |
The current study proposes a sustainability index (SI) measure based on artificial neural networks (ANN) and globally accepted parameters. Some of the available methods for SI measurement are multi-criteria analysis, external costs, energy analysis, and ecological footprint methods. However, validity remains a concern due to a system's needs, criteria, and requirements. Generally, sustainability is assessed in economic, environmental, and social issues, which varies across regions and countries. Most of the studies accept sub-indices but to a limited extent. Therefore, the proposed study develops an SI evaluation method based on the idea of multi-sustainability incorporating operations, institutions, risks, and climate factors besides economic, environmental, and social issues. All these issues might not be applicable to a single project but may help to develop a complete index when applied. The present study considered different scenarios in building a method to calculate SI using ANN. The results obtained by the ANN model for various input parameters helped to identify the best water conservation strategy. Sensitivity analysis was also performed to determine the uncertainty contribution/significance of the input variables for the water scarcity in the study region. The developed model in the study is tested on a rural water management system. HIGHLIGHTS Numerous approaches were performed regarding Sustainability Index (SI), but in the current study SI using multiple factors was assessed.; Artificial Neural Networks (ANN) model was trained and developed for future scenarios.; Current research work has a case study application on a community in a village.; Determination of SI was helpful in following and setting up a rainwater harvesting method at a selected village.; |
| abstract_unstemmed |
The current study proposes a sustainability index (SI) measure based on artificial neural networks (ANN) and globally accepted parameters. Some of the available methods for SI measurement are multi-criteria analysis, external costs, energy analysis, and ecological footprint methods. However, validity remains a concern due to a system's needs, criteria, and requirements. Generally, sustainability is assessed in economic, environmental, and social issues, which varies across regions and countries. Most of the studies accept sub-indices but to a limited extent. Therefore, the proposed study develops an SI evaluation method based on the idea of multi-sustainability incorporating operations, institutions, risks, and climate factors besides economic, environmental, and social issues. All these issues might not be applicable to a single project but may help to develop a complete index when applied. The present study considered different scenarios in building a method to calculate SI using ANN. The results obtained by the ANN model for various input parameters helped to identify the best water conservation strategy. Sensitivity analysis was also performed to determine the uncertainty contribution/significance of the input variables for the water scarcity in the study region. The developed model in the study is tested on a rural water management system. HIGHLIGHTS Numerous approaches were performed regarding Sustainability Index (SI), but in the current study SI using multiple factors was assessed.; Artificial Neural Networks (ANN) model was trained and developed for future scenarios.; Current research work has a case study application on a community in a village.; Determination of SI was helpful in following and setting up a rainwater harvesting method at a selected village.; |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_2027 |
| container_issue |
2 |
| title_short |
Assessment of sustainability index for rural water management using ANN |
| url |
https://doi.org/10.2166/ws.2021.346 https://doaj.org/article/a5ad139bbf004718a129704bd86d6015 http://ws.iwaponline.com/content/22/2/1421 https://doaj.org/toc/1606-9749 https://doaj.org/toc/1607-0798 |
| remote_bool |
true |
| author2 |
Gaurav Kumar Rajiv Gupta |
| author2Str |
Gaurav Kumar Rajiv Gupta |
| ppnlink |
34988062X |
| callnumber-subject |
TD - Environmental Technology |
| mediatype_str_mv |
c |
| isOA_txt |
true |
| hochschulschrift_bool |
false |
| doi_str |
10.2166/ws.2021.346 |
| callnumber-a |
TD201-500 |
| up_date |
2024-07-03T17:13:04.226Z |
| _version_ |
1803578805297610752 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ038306727</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230308021011.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.2166/ws.2021.346</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ038306727</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJa5ad139bbf004718a129704bd86d6015</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TD201-500</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TC401-506</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">R. Raghavendra Kumar</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Assessment of sustainability index for rural water management using ANN</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The current study proposes a sustainability index (SI) measure based on artificial neural networks (ANN) and globally accepted parameters. Some of the available methods for SI measurement are multi-criteria analysis, external costs, energy analysis, and ecological footprint methods. However, validity remains a concern due to a system's needs, criteria, and requirements. Generally, sustainability is assessed in economic, environmental, and social issues, which varies across regions and countries. Most of the studies accept sub-indices but to a limited extent. Therefore, the proposed study develops an SI evaluation method based on the idea of multi-sustainability incorporating operations, institutions, risks, and climate factors besides economic, environmental, and social issues. All these issues might not be applicable to a single project but may help to develop a complete index when applied. The present study considered different scenarios in building a method to calculate SI using ANN. The results obtained by the ANN model for various input parameters helped to identify the best water conservation strategy. Sensitivity analysis was also performed to determine the uncertainty contribution/significance of the input variables for the water scarcity in the study region. The developed model in the study is tested on a rural water management system. HIGHLIGHTS Numerous approaches were performed regarding Sustainability Index (SI), but in the current study SI using multiple factors was assessed.; Artificial Neural Networks (ANN) model was trained and developed for future scenarios.; Current research work has a case study application on a community in a village.; Determination of SI was helpful in following and setting up a rainwater harvesting method at a selected village.;</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">artificial intelligence</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">rural areas</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">scenario development</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">sustainability index</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">water resource management</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Water supply for domestic and industrial purposes</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">River, lake, and water-supply engineering (General)</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Gaurav Kumar</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Rajiv Gupta</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Water Supply</subfield><subfield code="d">IWA Publishing, 2021</subfield><subfield code="g">22(2022), 2, Seite 1421-1433</subfield><subfield code="w">(DE-627)34988062X</subfield><subfield code="w">(DE-600)2082134-7</subfield><subfield code="x">16070798</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:22</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:2</subfield><subfield code="g">pages:1421-1433</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.2166/ws.2021.346</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/a5ad139bbf004718a129704bd86d6015</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://ws.iwaponline.com/content/22/2/1421</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1606-9749</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1607-0798</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">22</subfield><subfield code="j">2022</subfield><subfield code="e">2</subfield><subfield code="h">1421-1433</subfield></datafield></record></collection>
|
| score |
7.4032135 |