Hindcasting Extreme Significant Wave Heights Under Fetch-Limited Conditions with Tree-Based Models
Accurately hindcasting waves in semi-enclosed, fetch-limited basins remains challenging for reanalysis models, which tend to underestimate storm peaks near the coast. We developed interpretable ML models for Rijeka Bay (northern Adriatic) using only wind observations from two land-based wind station...
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MDPI AG
2025-07-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/7/1355 |
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| author | Damjan Bujak Hanna Miličević Goran Lončar Dalibor Carević |
| author_facet | Damjan Bujak Hanna Miličević Goran Lončar Dalibor Carević |
| author_sort | Damjan Bujak |
| collection | DOAJ |
| description | Accurately hindcasting waves in semi-enclosed, fetch-limited basins remains challenging for reanalysis models, which tend to underestimate storm peaks near the coast. We developed interpretable ML models for Rijeka Bay (northern Adriatic) using only wind observations from two land-based wind stations to predict buoy <i>H<sub>m</sub></i><sub>0</sub> measurements spanning 2009–2011 (testing) and 2019–2021 (training and validation). The tested tree-based models included Random Forest, XGBoost, and Explainable Boosting Machine. This study introduces a novel approach in the literature by employing weighted schemes and feature engineering to enhance the predictive performance of interpretable, low-complexity machine learning models in hindcasting waves. Representing wind direction as sine–cosine components generally reduced RMSE and BIAS relative to traditional speed–direction inputs, while an exponential sample weight scheme that emphasized storm waves halved extreme <i>H<sub>m</sub></i><sub>0</sub> underprediction without inflating overall RMSE. The best-performing model, a Random Forest model, achieved an RMSE of 0.096 m and a correlation of 0.855 on the unseen test set—30% lower overall RMSE and 50% lower extreme wave RMSE than the MEDSEA and COEXMED hindcasts. Additionally, the underprediction was reduced by 90% compared to these reanalysis models. The method offers a computationally lightweight, transferable supplement to numerical wave guidance for coastal engineering and harbor operations. |
| format | Article |
| id | doaj-art-54190afbda34447982dfd3fd19136949 |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-54190afbda34447982dfd3fd191369492025-08-20T03:58:31ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-07-01137135510.3390/jmse13071355Hindcasting Extreme Significant Wave Heights Under Fetch-Limited Conditions with Tree-Based ModelsDamjan Bujak0Hanna Miličević1Goran Lončar2Dalibor Carević3Faculty of Civil Engineering, University of Zagreb, 10000 Zagreb, CroatiaFaculty of Civil Engineering, University of Zagreb, 10000 Zagreb, CroatiaFaculty of Civil Engineering, University of Zagreb, 10000 Zagreb, CroatiaFaculty of Civil Engineering, University of Zagreb, 10000 Zagreb, CroatiaAccurately hindcasting waves in semi-enclosed, fetch-limited basins remains challenging for reanalysis models, which tend to underestimate storm peaks near the coast. We developed interpretable ML models for Rijeka Bay (northern Adriatic) using only wind observations from two land-based wind stations to predict buoy <i>H<sub>m</sub></i><sub>0</sub> measurements spanning 2009–2011 (testing) and 2019–2021 (training and validation). The tested tree-based models included Random Forest, XGBoost, and Explainable Boosting Machine. This study introduces a novel approach in the literature by employing weighted schemes and feature engineering to enhance the predictive performance of interpretable, low-complexity machine learning models in hindcasting waves. Representing wind direction as sine–cosine components generally reduced RMSE and BIAS relative to traditional speed–direction inputs, while an exponential sample weight scheme that emphasized storm waves halved extreme <i>H<sub>m</sub></i><sub>0</sub> underprediction without inflating overall RMSE. The best-performing model, a Random Forest model, achieved an RMSE of 0.096 m and a correlation of 0.855 on the unseen test set—30% lower overall RMSE and 50% lower extreme wave RMSE than the MEDSEA and COEXMED hindcasts. Additionally, the underprediction was reduced by 90% compared to these reanalysis models. The method offers a computationally lightweight, transferable supplement to numerical wave guidance for coastal engineering and harbor operations.https://www.mdpi.com/2077-1312/13/7/1355machine learningrandom forestXGBoostexplainable boosting modelwave predictionwave hindcast |
| spellingShingle | Damjan Bujak Hanna Miličević Goran Lončar Dalibor Carević Hindcasting Extreme Significant Wave Heights Under Fetch-Limited Conditions with Tree-Based Models Journal of Marine Science and Engineering machine learning random forest XGBoost explainable boosting model wave prediction wave hindcast |
| title | Hindcasting Extreme Significant Wave Heights Under Fetch-Limited Conditions with Tree-Based Models |
| title_full | Hindcasting Extreme Significant Wave Heights Under Fetch-Limited Conditions with Tree-Based Models |
| title_fullStr | Hindcasting Extreme Significant Wave Heights Under Fetch-Limited Conditions with Tree-Based Models |
| title_full_unstemmed | Hindcasting Extreme Significant Wave Heights Under Fetch-Limited Conditions with Tree-Based Models |
| title_short | Hindcasting Extreme Significant Wave Heights Under Fetch-Limited Conditions with Tree-Based Models |
| title_sort | hindcasting extreme significant wave heights under fetch limited conditions with tree based models |
| topic | machine learning random forest XGBoost explainable boosting model wave prediction wave hindcast |
| url | https://www.mdpi.com/2077-1312/13/7/1355 |
| work_keys_str_mv | AT damjanbujak hindcastingextremesignificantwaveheightsunderfetchlimitedconditionswithtreebasedmodels AT hannamilicevic hindcastingextremesignificantwaveheightsunderfetchlimitedconditionswithtreebasedmodels AT goranloncar hindcastingextremesignificantwaveheightsunderfetchlimitedconditionswithtreebasedmodels AT daliborcarevic hindcastingextremesignificantwaveheightsunderfetchlimitedconditionswithtreebasedmodels |