Decision Making Model for Municipal Wastewater Conventional Secondary Treatment with Bayesian Networks
Technical, economic, regulatory, environmental, and social and political interests make the process of selecting an appropriate wastewater treatment technology complex. Although this problem has already been addressed from the dimensioning approach, our proposal in this research, a model of decision...
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| Veröffentlicht in: | Water (Basel) 2022-04, Vol.14 (8), p.1231 |
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| creator | Medina, Edgardo Fonseca, Carlos Roberto Gallego-Alarcón, Iván Morales-Nápoles, Oswaldo Gómez-Albores, Miguel Ángel Esparza-Soto, Mario Mastachi-Loza, Carlos Alberto García-Pulido, Daury |
| description | Technical, economic, regulatory, environmental, and social and political interests make the process of selecting an appropriate wastewater treatment technology complex. Although this problem has already been addressed from the dimensioning approach, our proposal in this research, a model of decision making for conventional secondary treatment of municipal wastewater through continuous-discrete, non-parametric Bayesian networks was developed. The most suitable network was structured in unit processes, independent of each other. Validation, with data in a mostly Mexican context, provided a positive predictive power of 83.5%, an excellent kappa (0.77 > 0.75), and the criterion line was surpassed with the location of the model in a receiver operating characteristic (ROC) graph, so the model can be implemented in this region. The final configuration of the Bayesian network allows the methodology to be easily extended to other types of treatments, wastewater, and to other regions. |
| doi_str_mv | 10.3390/w14081231 |
| format | Article |
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Although this problem has already been addressed from the dimensioning approach, our proposal in this research, a model of decision making for conventional secondary treatment of municipal wastewater through continuous-discrete, non-parametric Bayesian networks was developed. The most suitable network was structured in unit processes, independent of each other. Validation, with data in a mostly Mexican context, provided a positive predictive power of 83.5%, an excellent kappa (0.77 > 0.75), and the criterion line was surpassed with the location of the model in a receiver operating characteristic (ROC) graph, so the model can be implemented in this region. The final configuration of the Bayesian network allows the methodology to be easily extended to other types of treatments, wastewater, and to other regions.</description><identifier>ISSN: 2073-4441</identifier><identifier>EISSN: 2073-4441</identifier><identifier>DOI: 10.3390/w14081231</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Analysis ; Bayesian analysis ; Business metrics ; Decision making ; Environmental impact ; Environmental law ; Intelligent systems ; Municipal wastewater ; Probability ; Purification ; Sewage ; Variables ; Wastewater treatment ; Water quality ; Water treatment</subject><ispartof>Water (Basel), 2022-04, Vol.14 (8), p.1231</ispartof><rights>COPYRIGHT 2022 MDPI AG</rights><rights>2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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The final configuration of the Bayesian network allows the methodology to be easily extended to other types of treatments, wastewater, and to other regions.</description><subject>Analysis</subject><subject>Bayesian analysis</subject><subject>Business metrics</subject><subject>Decision making</subject><subject>Environmental impact</subject><subject>Environmental law</subject><subject>Intelligent systems</subject><subject>Municipal wastewater</subject><subject>Probability</subject><subject>Purification</subject><subject>Sewage</subject><subject>Variables</subject><subject>Wastewater treatment</subject><subject>Water quality</subject><subject>Water treatment</subject><issn>2073-4441</issn><issn>2073-4441</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNpNUE1PAjEQbYwmEuTgP2jiycNiv5bdHhE_E9CDGI-b0p1iYdliW9zw7y3BGGcOM3kzbzLvIXRJyZBzSW46KkhJGacnqMdIwTMhBD3915-jQQgrkkLIssxJD5k70DZY1-KZWtt2iWeuhgYb5_Fs11ptt6rBHypE6FQEjyeu_YY2JkLC30C7tlZ-j-ceVNykAe5s_MS3ag_Bqha_QOycX4cLdGZUE2DwW_vo_eF-PnnKpq-Pz5PxNNOc05iZWhAJhaYLXkgtmaZlIYGUnJIRYzIvSM24YJJRU9Jc0VIBrSVXZCEICEV4H10d7269-9pBiNXK7Xz6NVRslHMikjuHreFxa6kaqGxrXPRKp6xhY5MkMDbh40JSLoo854lwfSRo70LwYKqtt5skvKKkOlhf_VnPfwCGYnSv</recordid><startdate>20220401</startdate><enddate>20220401</enddate><creator>Medina, Edgardo</creator><creator>Fonseca, Carlos Roberto</creator><creator>Gallego-Alarcón, Iván</creator><creator>Morales-Nápoles, Oswaldo</creator><creator>Gómez-Albores, Miguel Ángel</creator><creator>Esparza-Soto, Mario</creator><creator>Mastachi-Loza, Carlos Alberto</creator><creator>García-Pulido, Daury</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><orcidid>https://orcid.org/0000-0001-9516-1170</orcidid><orcidid>https://orcid.org/0000-0002-6313-9187</orcidid><orcidid>https://orcid.org/0000-0002-6764-4674</orcidid><orcidid>https://orcid.org/0000-0002-3377-6564</orcidid></search><sort><creationdate>20220401</creationdate><title>Decision Making Model for Municipal Wastewater Conventional Secondary Treatment with Bayesian Networks</title><author>Medina, Edgardo ; 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| subjects | Analysis Bayesian analysis Business metrics Decision making Environmental impact Environmental law Intelligent systems Municipal wastewater Probability Purification Sewage Variables Wastewater treatment Water quality Water treatment |
| title | Decision Making Model for Municipal Wastewater Conventional Secondary Treatment with Bayesian Networks |
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