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...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/14/8/1231 |
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| author | Edgardo Medina Carlos Roberto Fonseca Iván Gallego-Alarcón Oswaldo Morales-Nápoles Miguel Ángel Gómez-Albores Mario Esparza-Soto Carlos Alberto Mastachi-Loza Daury García-Pulido |
| author_facet | Edgardo Medina Carlos Roberto Fonseca Iván Gallego-Alarcón Oswaldo Morales-Nápoles Miguel Ángel Gómez-Albores Mario Esparza-Soto Carlos Alberto Mastachi-Loza Daury García-Pulido |
| author_sort | Edgardo Medina |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T04:07:00Z |
| format | Article |
| id | doaj.art-d49c025254804281806606c09224bb2f |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-09T04:07:00Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-d49c025254804281806606c09224bb2f2023-12-03T14:05:09ZengMDPI AGWater2073-44412022-04-01148123110.3390/w14081231Decision Making Model for Municipal Wastewater Conventional Secondary Treatment with Bayesian NetworksEdgardo Medina0Carlos Roberto Fonseca1Iván Gallego-Alarcón2Oswaldo Morales-Nápoles3Miguel Ángel Gómez-Albores4Mario Esparza-Soto5Carlos Alberto Mastachi-Loza6Daury García-Pulido7Inter-American Institute of Water Sciences and Technology, Autonomous University of Mexico State, km 14.5 Toluca-Atlacomulco Road, Toluca 50295, MexicoInter-American Institute of Water Sciences and Technology, Autonomous University of Mexico State, km 14.5 Toluca-Atlacomulco Road, Toluca 50295, MexicoInter-American Institute of Water Sciences and Technology, Autonomous University of Mexico State, km 14.5 Toluca-Atlacomulco Road, Toluca 50295, MexicoFaculty of Civil Engineering and Geosciences, Delft University of Technology, P.O. Box 5, 2600 AA Delft, The NetherlandsInter-American Institute of Water Sciences and Technology, Autonomous University of Mexico State, km 14.5 Toluca-Atlacomulco Road, Toluca 50295, MexicoInter-American Institute of Water Sciences and Technology, Autonomous University of Mexico State, km 14.5 Toluca-Atlacomulco Road, Toluca 50295, MexicoInter-American Institute of Water Sciences and Technology, Autonomous University of Mexico State, km 14.5 Toluca-Atlacomulco Road, Toluca 50295, MexicoInter-American Institute of Water Sciences and Technology, Autonomous University of Mexico State, km 14.5 Toluca-Atlacomulco Road, Toluca 50295, MexicoTechnical, 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.https://www.mdpi.com/2073-4441/14/8/1231decision making modelwastewater secondary treatmentBayesian networksstructured expert judgment |
| spellingShingle | Edgardo Medina Carlos Roberto Fonseca Iván Gallego-Alarcón Oswaldo Morales-Nápoles Miguel Ángel Gómez-Albores Mario Esparza-Soto Carlos Alberto Mastachi-Loza Daury García-Pulido Decision Making Model for Municipal Wastewater Conventional Secondary Treatment with Bayesian Networks Water decision making model wastewater secondary treatment Bayesian networks structured expert judgment |
| title | Decision Making Model for Municipal Wastewater Conventional Secondary Treatment with Bayesian Networks |
| title_full | Decision Making Model for Municipal Wastewater Conventional Secondary Treatment with Bayesian Networks |
| title_fullStr | Decision Making Model for Municipal Wastewater Conventional Secondary Treatment with Bayesian Networks |
| title_full_unstemmed | Decision Making Model for Municipal Wastewater Conventional Secondary Treatment with Bayesian Networks |
| title_short | Decision Making Model for Municipal Wastewater Conventional Secondary Treatment with Bayesian Networks |
| title_sort | decision making model for municipal wastewater conventional secondary treatment with bayesian networks |
| topic | decision making model wastewater secondary treatment Bayesian networks structured expert judgment |
| url | https://www.mdpi.com/2073-4441/14/8/1231 |
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