Optimizing Water Distribution through Explainable AI and Rule-Based Control
Optimizing water distribution both from an energy-saving perspective and from a quality of service perspective is a challenging task since it involves a complex system with many nodes, many hidden variables and many operational constraints. For this reason, water distribution systems need to handle...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-06-01
|
| Series: | Computers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-431X/12/6/123 |
| _version_ | 1797595404257198080 |
|---|---|
| author | Enrico Ferrari Damiano Verda Nicolò Pinna Marco Muselli |
| author_facet | Enrico Ferrari Damiano Verda Nicolò Pinna Marco Muselli |
| author_sort | Enrico Ferrari |
| collection | DOAJ |
| description | Optimizing water distribution both from an energy-saving perspective and from a quality of service perspective is a challenging task since it involves a complex system with many nodes, many hidden variables and many operational constraints. For this reason, water distribution systems need to handle a delicate trade-off between the effectiveness and computational time of the solution. In this paper, we propose a new computationally efficient method, named rule-based control, to optimize water distribution networks without the need for a rigorous formulation of the optimization problem. As a matter of fact, since it is based on a machine learning approach, the proposed method employs only a set of historical data, where the configuration can be labeled according to a quality criterion. Since it is a data-driven approach, it could be applied to any complex network where historical labeled data are available. In particular, rule-based control exploits a rule-based classification method that allows us to retrieve the rules leading to good or bad performances of the system, even without any information about its physical laws. The evaluation of the results on some simulated scenarios shows that the proposed approach is able to reduce energy consumption while ensuring a good quality of the service. The proposed approach is currently used in the water distribution system of the Milan (Italy) water main. |
| first_indexed | 2024-03-11T02:36:59Z |
| format | Article |
| id | doaj.art-93970e7466d34a529840205b86f57b92 |
| institution | Directory Open Access Journal |
| issn | 2073-431X |
| language | English |
| last_indexed | 2024-03-11T02:36:59Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj.art-93970e7466d34a529840205b86f57b922023-11-18T09:54:21ZengMDPI AGComputers2073-431X2023-06-0112612310.3390/computers12060123Optimizing Water Distribution through Explainable AI and Rule-Based ControlEnrico Ferrari0Damiano Verda1Nicolò Pinna2Marco Muselli3Rulex Innovation Labs, Rulex Inc., 16122 Genoa, ItalyRulex Innovation Labs, Rulex Inc., 16122 Genoa, ItalyRulex Innovation Labs, Rulex Inc., 16122 Genoa, ItalyInstitute of Electronics, Computer and Telecommunication Engineering, Italian National Research Council, 16149 Genova, ItalyOptimizing water distribution both from an energy-saving perspective and from a quality of service perspective is a challenging task since it involves a complex system with many nodes, many hidden variables and many operational constraints. For this reason, water distribution systems need to handle a delicate trade-off between the effectiveness and computational time of the solution. In this paper, we propose a new computationally efficient method, named rule-based control, to optimize water distribution networks without the need for a rigorous formulation of the optimization problem. As a matter of fact, since it is based on a machine learning approach, the proposed method employs only a set of historical data, where the configuration can be labeled according to a quality criterion. Since it is a data-driven approach, it could be applied to any complex network where historical labeled data are available. In particular, rule-based control exploits a rule-based classification method that allows us to retrieve the rules leading to good or bad performances of the system, even without any information about its physical laws. The evaluation of the results on some simulated scenarios shows that the proposed approach is able to reduce energy consumption while ensuring a good quality of the service. The proposed approach is currently used in the water distribution system of the Milan (Italy) water main.https://www.mdpi.com/2073-431X/12/6/123rule-based controlwater distribution networkclassificationoptimization |
| spellingShingle | Enrico Ferrari Damiano Verda Nicolò Pinna Marco Muselli Optimizing Water Distribution through Explainable AI and Rule-Based Control Computers rule-based control water distribution network classification optimization |
| title | Optimizing Water Distribution through Explainable AI and Rule-Based Control |
| title_full | Optimizing Water Distribution through Explainable AI and Rule-Based Control |
| title_fullStr | Optimizing Water Distribution through Explainable AI and Rule-Based Control |
| title_full_unstemmed | Optimizing Water Distribution through Explainable AI and Rule-Based Control |
| title_short | Optimizing Water Distribution through Explainable AI and Rule-Based Control |
| title_sort | optimizing water distribution through explainable ai and rule based control |
| topic | rule-based control water distribution network classification optimization |
| url | https://www.mdpi.com/2073-431X/12/6/123 |
| work_keys_str_mv | AT enricoferrari optimizingwaterdistributionthroughexplainableaiandrulebasedcontrol AT damianoverda optimizingwaterdistributionthroughexplainableaiandrulebasedcontrol AT nicolopinna optimizingwaterdistributionthroughexplainableaiandrulebasedcontrol AT marcomuselli optimizingwaterdistributionthroughexplainableaiandrulebasedcontrol |