A Bayesian Optimization Approach for Water Resources Monitoring Through an Autonomous Surface Vehicle: The Ypacarai Lake Case Study
Bayesian Optimization is a sequential method for obtaining the maximum of an unknown function that has gained much popularity in recent years. Bayesian Optimization is commonly used to monitor the surface of large-scale aquatic environments using an Autonomous Surface Vehicle. We propose to model wa...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9319641/ |
| _version_ | 1818351861197963264 |
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| author | Federico Peralta Samaniego Daniel Gutierrez Reina Sergio L. Toral Marin Mario Arzamendia Derlis O. Gregor |
| author_facet | Federico Peralta Samaniego Daniel Gutierrez Reina Sergio L. Toral Marin Mario Arzamendia Derlis O. Gregor |
| author_sort | Federico Peralta Samaniego |
| collection | DOAJ |
| description | Bayesian Optimization is a sequential method for obtaining the maximum of an unknown function that has gained much popularity in recent years. Bayesian Optimization is commonly used to monitor the surface of large-scale aquatic environments using an Autonomous Surface Vehicle. We propose to model water quality parameters using Gaussian Processes, and propose three different adaptations of classical Acquisition Functions in order to explore an unknown space, considering surface vehicle restrictions. The proposed Sequential Bayesian Optimization system uses the aforementioned information in order to monitor the Lake and also to obtain a water quality model, which has an associated uncertainty map. For evaluation, the Mean Squared Error of the resulting approximated models are compared. Afterwards, they are compared with other monitoring algorithms, like the Traveling Salesman Problem, using Genetic Algorithms and Lawnmower. Concluding remarks indicate that the proposed method not only performs better while minimizing the Mean Squared Error (via active monitoring), but also manages to quickly identify an approximate of the black-box function, which is very useful for monitoring lakes like Ypacarai Lake ($60\: km^{2}$ ) in Paraguay. Additionally, the proposed method reduces the MSE by 25% when compared with Traveling Salesman Problem-based monitoring algorithms and also provides a more robust solution, i.e., 30% more independent of initial conditions, when compared with known robust coverage methods like the lawnmower method. |
| first_indexed | 2024-12-13T18:44:28Z |
| format | Article |
| id | doaj.art-195c54bd825646b2a09b39c505799020 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T18:44:28Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-195c54bd825646b2a09b39c5057990202022-12-21T23:35:06ZengIEEEIEEE Access2169-35362021-01-0199163917910.1109/ACCESS.2021.30509349319641A Bayesian Optimization Approach for Water Resources Monitoring Through an Autonomous Surface Vehicle: The Ypacarai Lake Case StudyFederico Peralta Samaniego0https://orcid.org/0000-0001-8847-3555Daniel Gutierrez Reina1Sergio L. Toral Marin2https://orcid.org/0000-0003-2612-0388Mario Arzamendia3https://orcid.org/0000-0002-6486-8402Derlis O. Gregor4https://orcid.org/0000-0003-2606-5980Department of Electronic Engineering, Technical School of Engineering of Seville, Seville, SpainDepartment of Electronic Engineering, Technical School of Engineering of Seville, Seville, SpainDepartment of Electronic Engineering, Technical School of Engineering of Seville, Seville, SpainFaculty of Engineering, National University of Asuncion, San Lorenzo, ParaguayFaculty of Engineering, National University of Asuncion, San Lorenzo, ParaguayBayesian Optimization is a sequential method for obtaining the maximum of an unknown function that has gained much popularity in recent years. Bayesian Optimization is commonly used to monitor the surface of large-scale aquatic environments using an Autonomous Surface Vehicle. We propose to model water quality parameters using Gaussian Processes, and propose three different adaptations of classical Acquisition Functions in order to explore an unknown space, considering surface vehicle restrictions. The proposed Sequential Bayesian Optimization system uses the aforementioned information in order to monitor the Lake and also to obtain a water quality model, which has an associated uncertainty map. For evaluation, the Mean Squared Error of the resulting approximated models are compared. Afterwards, they are compared with other monitoring algorithms, like the Traveling Salesman Problem, using Genetic Algorithms and Lawnmower. Concluding remarks indicate that the proposed method not only performs better while minimizing the Mean Squared Error (via active monitoring), but also manages to quickly identify an approximate of the black-box function, which is very useful for monitoring lakes like Ypacarai Lake ($60\: km^{2}$ ) in Paraguay. Additionally, the proposed method reduces the MSE by 25% when compared with Traveling Salesman Problem-based monitoring algorithms and also provides a more robust solution, i.e., 30% more independent of initial conditions, when compared with known robust coverage methods like the lawnmower method.https://ieeexplore.ieee.org/document/9319641/Bayesian optimizationBayes methodsGaussian processesdata acquisitionenvironmental monitoringinformative path planning |
| spellingShingle | Federico Peralta Samaniego Daniel Gutierrez Reina Sergio L. Toral Marin Mario Arzamendia Derlis O. Gregor A Bayesian Optimization Approach for Water Resources Monitoring Through an Autonomous Surface Vehicle: The Ypacarai Lake Case Study IEEE Access Bayesian optimization Bayes methods Gaussian processes data acquisition environmental monitoring informative path planning |
| title | A Bayesian Optimization Approach for Water Resources Monitoring Through an Autonomous Surface Vehicle: The Ypacarai Lake Case Study |
| title_full | A Bayesian Optimization Approach for Water Resources Monitoring Through an Autonomous Surface Vehicle: The Ypacarai Lake Case Study |
| title_fullStr | A Bayesian Optimization Approach for Water Resources Monitoring Through an Autonomous Surface Vehicle: The Ypacarai Lake Case Study |
| title_full_unstemmed | A Bayesian Optimization Approach for Water Resources Monitoring Through an Autonomous Surface Vehicle: The Ypacarai Lake Case Study |
| title_short | A Bayesian Optimization Approach for Water Resources Monitoring Through an Autonomous Surface Vehicle: The Ypacarai Lake Case Study |
| title_sort | bayesian optimization approach for water resources monitoring through an autonomous surface vehicle the ypacarai lake case study |
| topic | Bayesian optimization Bayes methods Gaussian processes data acquisition environmental monitoring informative path planning |
| url | https://ieeexplore.ieee.org/document/9319641/ |
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