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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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 km2) 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. |
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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 km2) 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.</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2021.3050934</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Algorithms ; Aquatic environment ; Autonomous Vehicles ; Bayes methods ; Bayesian analysis ; Bayesian Optimization ; Data Acquisition ; Environmental Monitoring ; Gaussian process ; Gaussian Processes ; Genetic algorithms ; Informative Path Planning ; Initial conditions ; Lakes ; Lawnmowers ; Monitoring ; Optimization ; Process parameters ; Robustness (mathematics) ; Surface vehicles ; Task analysis ; Traveling salesman problem ; Water pollution ; Water quality ; Water resources</subject><ispartof>IEEE access, 2021-01, Vol.9, p.1-1</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c408t-7adccf4d51ac1583994175ef99c613aa4552a98a6bc5cf1d491f8fc81d6ff8173</citedby><cites>FETCH-LOGICAL-c408t-7adccf4d51ac1583994175ef99c613aa4552a98a6bc5cf1d491f8fc81d6ff8173</cites><orcidid>0000-0003-2612-0388 ; 0000-0003-2606-5980 ; 0000-0002-6486-8402 ; 0000-0001-8847-3555</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9319641$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,860,2096,27610,27901,27902,54908</link.rule.ids></links><search><creatorcontrib>Peralta, Federico</creatorcontrib><creatorcontrib>Reina, Daniel Gutierrez</creatorcontrib><creatorcontrib>Toral, Sergio</creatorcontrib><creatorcontrib>Arzamendia, Mario</creatorcontrib><creatorcontrib>Gregor, Derlis</creatorcontrib><title>A Bayesian Optimization Approach for Water Resources Monitoring through an Autonomous Surface Vehicle: The Ypacarai Lake Case Study</title><title>IEEE access</title><addtitle>Access</addtitle><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 km2) 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.</description><subject>Algorithms</subject><subject>Aquatic environment</subject><subject>Autonomous Vehicles</subject><subject>Bayes methods</subject><subject>Bayesian analysis</subject><subject>Bayesian Optimization</subject><subject>Data Acquisition</subject><subject>Environmental Monitoring</subject><subject>Gaussian process</subject><subject>Gaussian Processes</subject><subject>Genetic algorithms</subject><subject>Informative Path Planning</subject><subject>Initial conditions</subject><subject>Lakes</subject><subject>Lawnmowers</subject><subject>Monitoring</subject><subject>Optimization</subject><subject>Process parameters</subject><subject>Robustness (mathematics)</subject><subject>Surface vehicles</subject><subject>Task analysis</subject><subject>Traveling salesman problem</subject><subject>Water pollution</subject><subject>Water quality</subject><subject>Water resources</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNUU1v1DAQjRBIVKW_oBdLnHex44_Y3EJUSqVFldgC4mTNOvbGSzcOtnNYrvxxvKSqmMuMnua9eZpXVdcErwnB6l3bdTfb7brGNVlTzLGi7EV1UROhVpRT8fK_-XV1ldIBl5IF4s1F9adFH-Bkk4cR3U_ZH_1vyD6MqJ2mGMAMyIWIvkO2EX2xKczR2IQ-h9HnEP24R3mIYd4PqPDbOYcxHMOc0HaODoxF3-zgzaN9jx4Gi35MYCCCRxv4aVEHyaJtnvvTm-qVg8dkr576ZfX1481D92m1ub-969rNyjAs86qB3hjHek7AEC6pUow03DqljCAUgHFeg5IgdoYbR3qmiJPOSNIL5yRp6GV1t-j2AQ56iv4I8aQDeP0PCHGvIeazX00UN5ztellzwcSuBqx2VBmOeaMUrnHRertolS_9mm3K-lB-Mxb7umaNlJQyocoWXbZMDClF656vEqzP4eklPH0OTz-FV1jXC8tba58ZihIlGKF_AZXwlc8</recordid><startdate>20210101</startdate><enddate>20210101</enddate><creator>Peralta, Federico</creator><creator>Reina, Daniel Gutierrez</creator><creator>Toral, Sergio</creator><creator>Arzamendia, Mario</creator><creator>Gregor, Derlis</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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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 km2) 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.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2021.3050934</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0003-2612-0388</orcidid><orcidid>https://orcid.org/0000-0003-2606-5980</orcidid><orcidid>https://orcid.org/0000-0002-6486-8402</orcidid><orcidid>https://orcid.org/0000-0001-8847-3555</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Aquatic environment Autonomous Vehicles Bayes methods Bayesian analysis Bayesian Optimization Data Acquisition Environmental Monitoring Gaussian process Gaussian Processes Genetic algorithms Informative Path Planning Initial conditions Lakes Lawnmowers Monitoring Optimization Process parameters Robustness (mathematics) Surface vehicles Task analysis Traveling salesman problem Water pollution Water quality Water resources |
title | A Bayesian Optimization Approach for Water Resources Monitoring through an Autonomous Surface Vehicle: The Ypacarai Lake Case Study |
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