Efficient Chlorophyll Prediction and Sampling in the Sea: A Real-Time Approach With UCB-Based Path Planning
This study focuses on predicting chlorophyll concentration in the sea, which is a key factor influencing fish populations, oxygen production, and carbon balance in marine ecosystems. Traditional methods for measuring chlorophyll involve time-consuming and costly sample collection and laboratory anal...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10819389/ |
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| author | Perihan Karakose Cafer Bal |
| author_facet | Perihan Karakose Cafer Bal |
| author_sort | Perihan Karakose |
| collection | DOAJ |
| description | This study focuses on predicting chlorophyll concentration in the sea, which is a key factor influencing fish populations, oxygen production, and carbon balance in marine ecosystems. Traditional methods for measuring chlorophyll involve time-consuming and costly sample collection and laboratory analysis, making real-time monitoring a challenging task. To address these challenges, the research utilizes real-time measurable parameters, such as temperature and salinity, to predict chlorophyll levels. A feature selection method is employed to identify relevant factors, such as wind speed and conductivity, ensuring accurate predictions with minimal uncertainty. In the second phase of the study, an exhaustive search algorithm is combined with reward functions like Upper Confidence Bound (UCB), entropy, and variance reduction. This combination allows for balancing exploration (sampling across the area) and exploitation (focusing on high-chlorophyll regions). The results show that UCB initially sampled from high-chlorophyll areas but gradually shifted towards broader exploration, achieving a balance between exploration and exploitation. Furthermore, the performance of UCB was found to closely match that of entropy and variance reduction in reducing uncertainty. |
| format | Article |
| id | doaj-art-4997f4d6d4044737b0ae0b4784d239b1 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-4997f4d6d4044737b0ae0b4784d239b12025-01-15T00:03:28ZengIEEEIEEE Access2169-35362025-01-01138127813910.1109/ACCESS.2024.352491710819389Efficient Chlorophyll Prediction and Sampling in the Sea: A Real-Time Approach With UCB-Based Path PlanningPerihan Karakose0https://orcid.org/0000-0002-8894-6997Cafer Bal1Department of Electronic and Automation, Bartin University, Bartin, TürkiyeDepartment of Mechatronic Engineering, Firat University, Elâziǧ, TürkiyeThis study focuses on predicting chlorophyll concentration in the sea, which is a key factor influencing fish populations, oxygen production, and carbon balance in marine ecosystems. Traditional methods for measuring chlorophyll involve time-consuming and costly sample collection and laboratory analysis, making real-time monitoring a challenging task. To address these challenges, the research utilizes real-time measurable parameters, such as temperature and salinity, to predict chlorophyll levels. A feature selection method is employed to identify relevant factors, such as wind speed and conductivity, ensuring accurate predictions with minimal uncertainty. In the second phase of the study, an exhaustive search algorithm is combined with reward functions like Upper Confidence Bound (UCB), entropy, and variance reduction. This combination allows for balancing exploration (sampling across the area) and exploitation (focusing on high-chlorophyll regions). The results show that UCB initially sampled from high-chlorophyll areas but gradually shifted towards broader exploration, achieving a balance between exploration and exploitation. Furthermore, the performance of UCB was found to closely match that of entropy and variance reduction in reducing uncertainty.https://ieeexplore.ieee.org/document/10819389/Adaptive samplingGaussian regressioninformative path planning |
| spellingShingle | Perihan Karakose Cafer Bal Efficient Chlorophyll Prediction and Sampling in the Sea: A Real-Time Approach With UCB-Based Path Planning IEEE Access Adaptive sampling Gaussian regression informative path planning |
| title | Efficient Chlorophyll Prediction and Sampling in the Sea: A Real-Time Approach With UCB-Based Path Planning |
| title_full | Efficient Chlorophyll Prediction and Sampling in the Sea: A Real-Time Approach With UCB-Based Path Planning |
| title_fullStr | Efficient Chlorophyll Prediction and Sampling in the Sea: A Real-Time Approach With UCB-Based Path Planning |
| title_full_unstemmed | Efficient Chlorophyll Prediction and Sampling in the Sea: A Real-Time Approach With UCB-Based Path Planning |
| title_short | Efficient Chlorophyll Prediction and Sampling in the Sea: A Real-Time Approach With UCB-Based Path Planning |
| title_sort | efficient chlorophyll prediction and sampling in the sea a real time approach with ucb based path planning |
| topic | Adaptive sampling Gaussian regression informative path planning |
| url | https://ieeexplore.ieee.org/document/10819389/ |
| work_keys_str_mv | AT perihankarakose efficientchlorophyllpredictionandsamplingintheseaarealtimeapproachwithucbbasedpathplanning AT caferbal efficientchlorophyllpredictionandsamplingintheseaarealtimeapproachwithucbbasedpathplanning |