{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T16:19:06Z","timestamp":1791994746714,"version":"4.4.0"},"reference-count":72,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T00:00:00Z","timestamp":1768780800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T00:00:00Z","timestamp":1768780800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100002628","name":"Incheon National University","doi-asserted-by":"publisher","award":["2025"],"award-info":[{"award-number":["2025"]}],"id":[{"id":"10.13039\/501100002628","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Environ Monit Assess"],"DOI":"10.1007\/s10661-026-14981-3","type":"journal-article","created":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T17:42:07Z","timestamp":1768844527000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Modeling regional mean sea level based on climate measurements using a stacked ensemble approach"],"prefix":"10.1007","volume":"198","author":[{"given":"Mohamed T.","family":"Elnabwy","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mosbeh R.","family":"Kaloop","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emad","family":"Elbeltagi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jong Wan","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,19]]},"reference":[{"key":"14981_CR1","doi-asserted-by":"publisher","unstructured":"Awad, M., & Khanna, R. (2015). Support vector regression. In Efficient Learning Machines (pp. 67\u201380). Berkeley, CA: Apress. https:\/\/doi.org\/10.1007\/978-1-4302-5990-9_4","DOI":"10.1007\/978-1-4302-5990-9_4"},{"key":"14981_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2024.176410","volume":"954","author":"AS Ayinde","year":"2024","unstructured":"Ayinde, A. S., Huaming, Y., & Kejian, W. (2024). Review of machine learning methods for sea level change modeling and prediction. Science of the Total Environment, 954, Article 176410. https:\/\/doi.org\/10.1016\/j.scitotenv.2024.176410","journal-title":"Science of the Total Environment"},{"key":"14981_CR3","doi-asserted-by":"publisher","DOI":"10.3390\/act11080217","author":"AG Baziyad","year":"2022","unstructured":"Baziyad, A. G., Nouh, A. S., Ahmad, I., & Alkuhayli, A. (2022). Application of least-squares support-vector machine based on hysteresis operators and particle swarm optimization for modeling and control of hysteresis in piezoelectric actuators. Actuators. https:\/\/doi.org\/10.3390\/act11080217","journal-title":"Actuators"},{"issue":"11","key":"14981_CR4","doi-asserted-by":"publisher","first-page":"3965","DOI":"10.1007\/s11269-019-02346-0","volume":"33","author":"H Bonakdari","year":"2019","unstructured":"Bonakdari, H., Ebtehaj, I., Samui, P., & Gharabaghi, B. (2019). Lake water-level fluctuations forecasting using minimax probability machine regression, relevance vector machine, Gaussian process regression, and extreme learning machine. Water Resources Management, 33(11), 3965\u20133984. https:\/\/doi.org\/10.1007\/s11269-019-02346-0","journal-title":"Water Resources Management"},{"key":"14981_CR5","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-50262-5","author":"XN Bui","year":"2019","unstructured":"Bui, X. N., Jaroonpattanapong, P., Nguyen, H., Tran, Q. H., & Long, N. Q. (2019). A novel hybrid model for predicting blast-induced ground vibration based on k-nearest neighbors and particle swarm optimization. Scientific Reports. https:\/\/doi.org\/10.1038\/s41598-019-50262-5","journal-title":"Scientific Reports"},{"key":"14981_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2023.127188","volume":"273","author":"W Cai","year":"2023","unstructured":"Cai, W., Wen, X., Li, C., Shao, J., & Xu, J. (2023). Predicting the energy consumption in buildings using the optimized support vector regression model. Energy, 273, Article 127188. https:\/\/doi.org\/10.1016\/j.energy.2023.127188","journal-title":"Energy"},{"key":"14981_CR7","doi-asserted-by":"publisher","unstructured":"Cazenave, A., & Llovel, W. (2010). Contemporary sea level rise. Annual Review of Marine Science, 2(1), 145-173. https:\/\/doi.org\/10.1146\/ANNUREV-MARINE-120308-081105","DOI":"10.1146\/ANNUREV-MARINE-120308-081105"},{"issue":"11","key":"14981_CR8","doi-asserted-by":"publisher","DOI":"10.1007\/s10661-024-13191-z","volume":"196","author":"A Das","year":"2024","unstructured":"Das, A., & Swain, P. K. (2024). Navigating the sea level rise: Exploring the interplay of climate change, sea level rise, and coastal communities in India. Environmental Monitoring and Assessment, 196(11), Article 1010. https:\/\/doi.org\/10.1007\/s10661-024-13191-z","journal-title":"Environmental Monitoring and Assessment"},{"issue":"9","key":"14981_CR9","doi-asserted-by":"publisher","DOI":"10.1007\/s10661-024-12961-z","volume":"196","author":"A de Souza Lima","year":"2024","unstructured":"de Souza de Lima, A., Cassalho, F., Miesse, T. W., Henke, M., Canick, M. R., & Ferreira, C. M. (2024). Assessing the potential long-term effects of sea-level rise on salt marsh\u2019s coastal protective capacity under different climate pathway scenarios abstract. Environmental Monitoring and Assessment, 196(9), 817. https:\/\/doi.org\/10.1007\/s10661-024-12961-z","journal-title":"Environmental Monitoring and Assessment"},{"issue":"1","key":"14981_CR10","doi-asserted-by":"publisher","DOI":"10.1186\/s44147-023-00348-9","volume":"71","author":"M Duan","year":"2024","unstructured":"Duan, M. (2024). Innovative compressive strength prediction for recycled aggregate\/concrete using K-nearest neighbors and meta-heuristic optimization approaches. Journal of Engineering and Applied Science, 71(1), Article 15. https:\/\/doi.org\/10.1186\/s44147-023-00348-9","journal-title":"Journal of Engineering and Applied Science"},{"issue":"8","key":"14981_CR11","doi-asserted-by":"publisher","first-page":"1215","DOI":"10.1007\/S11027-012-9418-Y\/TABLES\/4","volume":"18","author":"OE Frihy","year":"2013","unstructured":"Frihy, O. E., & El-Sayed, M. K. (2013). Vulnerability risk assessment and adaptation to climate change induced sea level rise along the Mediterranean coast of Egypt. Mitigation and Adaptation Strategies for Global Change, 18(8), 1215\u20131237. https:\/\/doi.org\/10.1007\/S11027-012-9418-Y\/TABLES\/4","journal-title":"Mitigation and Adaptation Strategies for Global Change"},{"key":"14981_CR12","doi-asserted-by":"publisher","unstructured":"Gao, F., Liu, L., Hu, H., Shi, Z., Ren, D., Wang, G., Liang, X., & Sun, X. (2023). Tidal evolution and predictable tide\u2010only inundation along the east coast of the United States. Journal of Geophysical Research: Oceans, 128(5). https:\/\/doi.org\/10.1029\/2022JC019410","DOI":"10.1029\/2022JC019410"},{"key":"14981_CR13","doi-asserted-by":"publisher","first-page":"103358","DOI":"10.1016\/j.inffus.2025.103358","volume":"124","author":"Moncef Garouani","year":"2025","unstructured":"Garouani, M., Barhrhouj, A., & Teste, O. (2025). XStacking : An effective and inherently explainable framework for stacked ensemble learning. Information Fusion, 103358. https:\/\/doi.org\/10.1016\/j.inffus.2025.103358","journal-title":"Information Fusion"},{"key":"14981_CR14","doi-asserted-by":"publisher","first-page":"100597","DOI":"10.1016\/j.wace.2023.100597","volume":"41","author":"Nathalie Giloy","year":"2023","unstructured":"Giloy, N., Latapy, A., & Andr\u00e9, G. (2023). Estimation of skew surge uncertainties when predicting tides in the past. Weather and Climate Extremes, 41,100597. https:\/\/doi.org\/10.1016\/j.wace.2023.100597","journal-title":"Weather and Climate Extremes"},{"key":"14981_CR15","doi-asserted-by":"publisher","unstructured":"Goma, R. H., Radwan, I. M., El Saharty, A. A., &\u00a0 Abdel Ghani, S. A. (2025). Spatiotemporal distribution of urea in seawater along the eastern Mediterranean Sea coast in Egypt. Environmental Monitoring and Assessment, 197(6), 689. https:\/\/doi.org\/10.1007\/s10661-025-14105-3","DOI":"10.1007\/s10661-025-14105-3"},{"issue":"12","key":"14981_CR16","doi-asserted-by":"publisher","first-page":"2537","DOI":"10.1007\/s00477-021-02018-9","volume":"35","author":"F Granata","year":"2021","unstructured":"Granata, F., & Di Nunno, F. (2021). Artificial Intelligence models for prediction of the tide level in Venice. Stochastic Environmental Research and Risk Assessment, 35(12), 2537\u20132548. https:\/\/doi.org\/10.1007\/s00477-021-02018-9","journal-title":"Stochastic Environmental Research and Risk Assessment"},{"key":"14981_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2022.128431","volume":"613","author":"F Granata","year":"2022","unstructured":"Granata, F., Di Nunno, F., & de Marinis, G. (2022). Stacked machine learning algorithms and bidirectional long short-term memory networks for multi-step ahead streamflow forecasting: A comparative study. Journal of Hydrology, 613, Article 128431. https:\/\/doi.org\/10.1016\/j.jhydrol.2022.128431","journal-title":"Journal of Hydrology"},{"key":"14981_CR18","doi-asserted-by":"publisher","DOI":"10.1007\/s10712-019-09525-z","author":"JM Gregory","year":"2019","unstructured":"Gregory, J. M., Griffies, S. M., Hughes, C. W., Lowe, J. A., Church, J. A., Fukimori, I., et al. (2019). Concepts and terminology for sea level: Mean, variability and change, both local and global. Surveys in Geophysics. https:\/\/doi.org\/10.1007\/s10712-019-09525-z","journal-title":"Surveys in Geophysics"},{"issue":"3","key":"14981_CR19","doi-asserted-by":"publisher","DOI":"10.3390\/w14030492","volume":"14","author":"J Gu","year":"2022","unstructured":"Gu, J., Liu, S., Zhou, Z., Chalov, S. R., & Zhuang, Q. (2022). A stacking ensemble learning model for monthly rainfall prediction in the Taihu Basin, China. Water, 14(3), Article 492. https:\/\/doi.org\/10.3390\/w14030492","journal-title":"Water"},{"key":"14981_CR20","doi-asserted-by":"publisher","DOI":"10.3390\/en12010112","author":"T Guo","year":"2019","unstructured":"Guo, T., He, W., Jiang, Z., Chu, X., Malekian, R., & Li, Z. (2019). An improved LSSVM model for intelligent prediction of the daily water level. Energies. https:\/\/doi.org\/10.3390\/en12010112","journal-title":"Energies"},{"key":"14981_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.pocean.2021.102521","volume":"192","author":"P Hogarth","year":"2021","unstructured":"Hogarth, P., Pugh, D. T., Hughes, C. W., & Williams, S. D. P. (2021). Changes in mean sea level around Great Britain over the past 200\u202fyears. Progress in Oceanography, 192, Article 102521. https:\/\/doi.org\/10.1016\/j.pocean.2021.102521","journal-title":"Progress in Oceanography"},{"key":"14981_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2024.101535","volume":"49","author":"DG Honi","year":"2024","unstructured":"Honi, D. G., & Szathmary, L. (2024). A one-dimensional convolutional neural network-based deep learning approach for predicting cardiovascular diseases. Informatics in Medicine Unlocked, 49, Article 101535. https:\/\/doi.org\/10.1016\/j.imu.2024.101535","journal-title":"Informatics in Medicine Unlocked"},{"key":"14981_CR23","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1016\/j.gloplacha.2017.12.018","volume":"161","author":"M Imani","year":"2018","unstructured":"Imani, M., Kao, H.-C., Lan, W.-H., & Kuo, C.-Y. (2018). Daily sea level prediction at Chiayi coast, Taiwan using extreme learning machine and relevance vector machine. Global and Planetary Change, 161, 211\u2013221. https:\/\/doi.org\/10.1016\/j.gloplacha.2017.12.018","journal-title":"Global and Planetary Change"},{"issue":"2","key":"14981_CR24","doi-asserted-by":"publisher","first-page":"14","DOI":"10.18466\/cbayarfbe.1384547","volume":"20","author":"Y Karsavran","year":"2024","unstructured":"Karsavran, Y. (2024). Comparison of random forest, SVR and KNN based models in sea level prediction for Erdemli coast of Mersin. Celal Bayar \u00dcniversitesi Fen Bilimleri Dergisi, 20(2), 14\u201318. https:\/\/doi.org\/10.18466\/cbayarfbe.1384547","journal-title":"Celal Bayar \u00dcniversitesi Fen Bilimleri Dergisi"},{"issue":"4","key":"14981_CR25","doi-asserted-by":"publisher","first-page":"4519","DOI":"10.1007\/s11063-022-11055-6","volume":"55","author":"M Kaveh","year":"2023","unstructured":"Kaveh, M., & Mesgari, M. S. (2023). Application of meta-heuristic algorithms for training neural networks and deep learning architectures: A comprehensive review. Neural Processing Letters, 55(4), 4519\u20134622. https:\/\/doi.org\/10.1007\/s11063-022-11055-6","journal-title":"Neural Processing Letters"},{"issue":"1","key":"14981_CR26","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-91339-8","volume":"15","author":"O Kisi","year":"2025","unstructured":"Kisi, O., Heddam, S., Parmar, K. S., Petroselli, A., K\u00fclls, C., & Zounemat-Kermani, M. (2025). Integration of Gaussian process regression and K means clustering for enhanced short term rainfall runoff modeling. Scientific Reports, 15(1), Article 7444. https:\/\/doi.org\/10.1038\/s41598-025-91339-8","journal-title":"Scientific Reports"},{"key":"14981_CR27","doi-asserted-by":"publisher","DOI":"10.3390\/su11174643","author":"V Lai","year":"2019","unstructured":"Lai, V., Ahmed, A. N., Malek, M. A., Afan, H. A., Ibrahim, R. K., El-Shafie, A., & El-Shafie, A. (2019). Modeling the nonlinearity of sea level oscillations in the Malaysian coastal areas using machine learning algorithms. Sustainability. https:\/\/doi.org\/10.3390\/su11174643","journal-title":"Sustainability"},{"issue":"9","key":"14981_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.asej.2024.102916","volume":"15","author":"SD Latif","year":"2024","unstructured":"Latif, S. D., Almubaidin, M. A., Shen, C. G., Sapitang, M., Birima, A. H., Ahmed, A. N., et al. (2024). Improving sea level prediction in coastal areas using machine learning techniques. Ain Shams Engineering Journal, 15(9), Article 102916. https:\/\/doi.org\/10.1016\/j.asej.2024.102916","journal-title":"Ain Shams Engineering Journal"},{"issue":"2","key":"14981_CR29","doi-asserted-by":"publisher","first-page":"368","DOI":"10.2166\/HYDRO.2020.055","volume":"23","author":"BX Liang","year":"2021","unstructured":"Liang, B. X., Hu, J. P., Liu, C., & Hong, B. (2021). Data pre-processing and artificial neural networks for tidal level prediction at the Pearl River Estuary. Journal of Hydroinformatics, 23(2), 368\u2013382. https:\/\/doi.org\/10.2166\/HYDRO.2020.055","journal-title":"Journal of Hydroinformatics"},{"issue":"12","key":"14981_CR30","doi-asserted-by":"publisher","first-page":"8503","DOI":"10.1007\/s00521-019-04375-7","volume":"31","author":"C Lin","year":"2019","unstructured":"Lin, C., Li, T., Chen, S., Liu, X., Lin, C., & Liang, S. (2019). Gaussian process regression-based forecasting model of dam deformation. Neural Computing and Applications, 31(12), 8503\u20138518. https:\/\/doi.org\/10.1007\/s00521-019-04375-7","journal-title":"Neural Computing and Applications"},{"issue":"4","key":"14981_CR31","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1007\/s00382-025-07655-2","volume":"63","author":"A Liu","year":"2025","unstructured":"Liu, A., Zuo, J., Gao, H., Tian, B., Yuan, J., & Wan, J. (2025). Seasonal prediction and potential predictability of the northern tropical Atlantic SST anomalies in current coupled climate models. Climate Dynamics, 63(4), 178. https:\/\/doi.org\/10.1007\/s00382-025-07655-2","journal-title":"Climate Dynamics"},{"key":"14981_CR32","unstructured":"Louppe, G. (2014). Understanding random forests: From theory to practice. Retrieved from https:\/\/arxiv.org\/abs\/1407.7502. Accessed 14 Jan 2026."},{"issue":"7","key":"14981_CR33","doi-asserted-by":"publisher","DOI":"10.3390\/w15071265","volume":"15","author":"M Lu","year":"2023","unstructured":"Lu, M., Hou, Q., Qin, S., Zhou, L., Hua, D., Wang, X., & Cheng, L. (2023). A stacking ensemble model of various machine learning models for daily runoff forecasting. Water, 15(7), Article 1265. https:\/\/doi.org\/10.3390\/w15071265","journal-title":"Water"},{"issue":"27","key":"14981_CR34","doi-asserted-by":"publisher","first-page":"16492","DOI":"10.1080\/10106049.2022.2109761","volume":"37","author":"LV Lucchese","year":"2024","unstructured":"Lucchese, L. V., de Oliveira, G. G., & Pedrollo, O. C. (2024). A hybrid random forests and artificial neural networks bagging ensemble for landslide susceptibility modelling. Geocarto International, 37(27), 16492\u201316511. https:\/\/doi.org\/10.1080\/10106049.2022.2109761","journal-title":"Geocarto International"},{"key":"14981_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.eve.2024.100051","volume":"3","author":"S Majumder","year":"2025","unstructured":"Majumder, S., Abeer, A. N., Rahman, M., & Bhuiyan, M. A. E. (2025). Analysis and prediction of sea level rise along the U.S. East and Gulf coasts and its socio-economic impacts on the nearby inland areas. Evolving Earth, 3, Article 100051. https:\/\/doi.org\/10.1016\/j.eve.2024.100051","journal-title":"Evolving Earth"},{"issue":"2","key":"14981_CR36","doi-asserted-by":"publisher","DOI":"10.1007\/s10661-023-12287-2","volume":"196","author":"M Marzouk","year":"2024","unstructured":"Marzouk, M., & Azab, S. (2024). Modeling climate change adaptation for sustainable coastal zones using GIS and AHP. Environmental Monitoring and Assessment, 196(2), Article 147. https:\/\/doi.org\/10.1007\/s10661-023-12287-2","journal-title":"Environmental Monitoring and Assessment"},{"key":"14981_CR37","doi-asserted-by":"publisher","unstructured":"Meena, B. L., & Agrawal, J. D. (2015). Tidal level forecasting using ANN. In Procedia Engineering (Vol. 116, pp. 607\u2013614). Elsevier Ltd. https:\/\/doi.org\/10.1016\/j.proeng.2015.08.332","DOI":"10.1016\/j.proeng.2015.08.332"},{"key":"14981_CR38","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.jog.2012.03.005","volume":"58","author":"Benoit Meyssignac","year":"2012","unstructured":"Meyssignac, B., & Cazenave, A. (2012). Sea level: A review of present-day and recent-past changes and variability. Journal of Geodynamics, 58, 96\u2013109. https:\/\/doi.org\/10.1016\/j.jog.2012.03.005","journal-title":"Journal of Geodynamics"},{"issue":"7","key":"14981_CR39","doi-asserted-by":"publisher","first-page":"281","DOI":"10.2183\/pjab.89.281","volume":"89","author":"N Mimura","year":"2013","unstructured":"Mimura, N. (2013). Sea-level rise caused by climate change and its implications for society. Proceedings of the Japan Academy Series B: Physical and Biological Sciences, 89(7), 281\u2013301. https:\/\/doi.org\/10.2183\/pjab.89.281","journal-title":"Proceedings of the Japan Academy Series B: Physical and Biological Sciences"},{"key":"14981_CR40","doi-asserted-by":"publisher","unstructured":"Montesinos L\u00f3pez, O. A., Montesinos L\u00f3pez, A., & Crossa, J. (2022). Fundamentals of artificial neural networks and deep learning. In Multivariate Statistical Machine Learning Methods for Genomic Prediction (pp. 379\u2013425). Springer International Publishing. https:\/\/doi.org\/10.1007\/978-3-030-89010-0_10","DOI":"10.1007\/978-3-030-89010-0_10"},{"issue":"6","key":"14981_CR41","doi-asserted-by":"publisher","first-page":"2701","DOI":"10.1007\/s00477-025-02990-6","volume":"39","author":"I Morlini","year":"2025","unstructured":"Morlini, I., & Orlandini, S. (2025). Anthropogenic heat effects on local deviations from the global mean annual air temperature: A case study of Modena, Italy. Stochastic Environmental Research and Risk Assessment, 39(6), 2701\u20132722. https:\/\/doi.org\/10.1007\/s00477-025-02990-6","journal-title":"Stochastic Environmental Research and Risk Assessment"},{"key":"14981_CR42","doi-asserted-by":"publisher","unstructured":"Muslim, T. O., Ahmed, A. N., Malek, M. A., Abdulmohsin Afan, H., Khaleel Ibrahim, R., El-Shafie, A., Sapitang, M., Sherif, M., Sefelnasr, A., & El-Shafie, A. (2020). Investigating the influence of meteorological parameters on the accuracy of sea-level prediction models in Sabah, Malaysia. Sustainability, 12(3), 1193. https:\/\/doi.org\/10.3390\/su12031193","DOI":"10.3390\/su12031193"},{"issue":"17","key":"14981_CR43","doi-asserted-by":"publisher","first-page":"50661","DOI":"10.1007\/s11356-023-25886-w","volume":"30","author":"MB Nafouanti","year":"2023","unstructured":"Nafouanti, M. B., Li, J., Nyakilla, E. E., Mwakipunda, G. C., & Mulashani, A. (2023). A novel hybrid random forest linear model approach for forecasting groundwater fluoride contamination. Environmental Science and Pollution Research, 30(17), 50661\u201350674. https:\/\/doi.org\/10.1007\/s11356-023-25886-w","journal-title":"Environmental Science and Pollution Research"},{"key":"14981_CR44","doi-asserted-by":"publisher","first-page":"1353","DOI":"10.3389\/FMARS.2021.710342\/BIBTEX","volume":"8","author":"RJ Nicholls","year":"2021","unstructured":"Nicholls, R. J., Beaven, R. P., Stringfellow, A., Monfort, D., Le Cozannet, G., Wahl, T., et al. (2021). Coastal landfills and rising sea levels: A challenge for the 21st century. Frontiers in Marine Science, 8, 1353. https:\/\/doi.org\/10.3389\/FMARS.2021.710342\/BIBTEX","journal-title":"Frontiers in Marine Science"},{"issue":"1","key":"14981_CR45","doi-asserted-by":"publisher","first-page":"112","DOI":"10.3390\/geohazards5010006","volume":"5","author":"K Pand\u017ei\u0107","year":"2024","unstructured":"Pand\u017ei\u0107, K., Likso, T., Biondi\u0107, R., & Biondi\u0107, B. (2024). A review of the contribution of satellite altimetry and tide gauge data to evaluate sea level trends in the Adriatic Sea within a Mediterranean and Global Context. GeoHazards, 5(1), 112\u2013141. https:\/\/doi.org\/10.3390\/geohazards5010006","journal-title":"GeoHazards"},{"issue":"8","key":"14981_CR46","doi-asserted-by":"publisher","first-page":"929","DOI":"10.1016\/S0098-3004(02)00013-4","volume":"28","author":"R Pawlowicz","year":"2002","unstructured":"Pawlowicz, R., Beardsley, B., & Lentz, S. (2002). Classical tidal harmonic analysis including error estimates in MATLAB using T_TIDE. Computers & Geosciences, 28(8), 929\u2013937. https:\/\/doi.org\/10.1016\/S0098-3004(02)00013-4","journal-title":"Computers & Geosciences"},{"key":"14981_CR47","doi-asserted-by":"publisher","unstructured":"Piro, P., Nock, R., Bel Haj Ali, W., Nielsen, F., & Barlaud, M. (2013). Boosting k-nearest neighbors classification. (pp. 341\u2013375). https:\/\/doi.org\/10.1007\/978-1-4471-5520-1_12","DOI":"10.1007\/978-1-4471-5520-1_12"},{"issue":"11","key":"14981_CR48","doi-asserted-by":"publisher","DOI":"10.3390\/rs15112881","volume":"15","author":"N Raj","year":"2023","unstructured":"Raj, N., & Brown, J. (2023). Prediction of mean sea level with GNSS-VLM correction using a hybrid deep learning model in Australia. Remote Sensing, 15(11), Article 2881. https:\/\/doi.org\/10.3390\/rs15112881","journal-title":"Remote Sensing"},{"issue":"15","key":"14981_CR49","doi-asserted-by":"publisher","DOI":"10.3390\/math12152376","volume":"12","author":"N Raj","year":"2024","unstructured":"Raj, N., Murali, J., Singh-Peterson, L., & Downs, N. (2024). Prediction of sea level using double data decomposition and hybrid deep learning model for Northern Territory, Australia. Mathematics, 12(15), Article 2376. https:\/\/doi.org\/10.3390\/math12152376","journal-title":"Mathematics"},{"issue":"3","key":"14981_CR50","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1007\/s00382-025-07653-4","volume":"63","author":"J Ramos-Alc\u00e1ntara","year":"2025","unstructured":"Ramos-Alc\u00e1ntara, J., Agulles, M., Gomis, D., & Jord\u00e0, G. (2025). Quantifying the contributors to extreme sea level events in a Mediterranean microtidal region at high spatio-temporal resolution. Climate Dynamics, 63(3), 167. https:\/\/doi.org\/10.1007\/s00382-025-07653-4","journal-title":"Climate Dynamics"},{"key":"14981_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.cscm.2023.e01898","volume":"18","author":"R Ray","year":"2023","unstructured":"Ray, R., Choudhary, S. S., Roy, L. B., Kaloop, M. R., Samui, P., Kurup, P. U., et al. (2023). Reliability analysis of reinforced soil slope stability using GA-ANFIS, RFC, and GMDH soft computing techniques. Case Studies in Construction Materials, 18, Article e01898. https:\/\/doi.org\/10.1016\/j.cscm.2023.e01898","journal-title":"Case Studies in Construction Materials"},{"issue":"23","key":"14981_CR52","doi-asserted-by":"publisher","first-page":"17095","DOI":"10.1007\/s00521-023-08568-z","volume":"35","author":"M Reyad","year":"2023","unstructured":"Reyad, M., Sarhan, A. M., & Arafa, M. (2023). A modified Adam algorithm for deep neural network optimization. Neural Computing and Applications, 35(23), 17095\u201317112. https:\/\/doi.org\/10.1007\/s00521-023-08568-z","journal-title":"Neural Computing and Applications"},{"issue":"1","key":"14981_CR53","doi-asserted-by":"publisher","first-page":"271","DOI":"10.5194\/gmd-16-271-2023","volume":"16","author":"M Rus","year":"2023","unstructured":"Rus, M., Fettich, A., Kristan, M., & Li\u010der, M. (2023). HIDRA2: Deep-learning ensemble sea level and storm tide forecasting in the presence of seiches \u2013 the case of the northern Adriatic. Geoscientific Model Development, 16(1), 271\u2013288. https:\/\/doi.org\/10.5194\/gmd-16-271-2023","journal-title":"Geoscientific Model Development"},{"issue":"3","key":"14981_CR54","doi-asserted-by":"publisher","first-page":"605","DOI":"10.5194\/gmd-18-605-2025","volume":"18","author":"M Rus","year":"2025","unstructured":"Rus, M., Mihanovi\u0107, H., Li\u010der, M., & Kristan, M. (2025). HIDRA3: A deep-learning model for multipoint ensemble sea level forecasting in the presence of tide gauge sensor failures. Geoscientific Model Development, 18(3), 605\u2013620. https:\/\/doi.org\/10.5194\/gmd-18-605-2025","journal-title":"Geoscientific Model Development"},{"key":"14981_CR55","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jmp.2018.03.001","volume":"85","author":"E Schulz","year":"2018","unstructured":"Schulz, E., Speekenbrink, M., & Krause, A. (2018). A tutorial on Gaussian process regression: Modelling, exploring, and exploiting functions. Journal of Mathematical Psychology, 85, 1\u201316. https:\/\/doi.org\/10.1016\/j.jmp.2018.03.001","journal-title":"Journal of Mathematical Psychology"},{"issue":"1","key":"14981_CR56","doi-asserted-by":"publisher","DOI":"10.3390\/atmos11010066","volume":"11","author":"S Shabani","year":"2020","unstructured":"Shabani, S., Samadianfard, S., Sattari, M. T., Mosavi, A., Shamshirband, S., Kmet, T., & V\u00e1rkonyi-K\u00f3czy, A. R. (2020). Modeling pan evaporation using Gaussian process regression K-nearest neighbors random forest and support vector machines; comparative analysis. Atmosphere, 11(1), Article 66. https:\/\/doi.org\/10.3390\/atmos11010066","journal-title":"Atmosphere"},{"issue":"5","key":"14981_CR57","doi-asserted-by":"publisher","DOI":"10.1007\/s13201-024-02135-0","volume":"14","author":"B Sharma","year":"2024","unstructured":"Sharma, B., & Goel, N. K. (2024). Streamflow prediction using support vector regression machine learning model for Tehri Dam. Applied Water Science, 14(5), Article 99. https:\/\/doi.org\/10.1007\/s13201-024-02135-0","journal-title":"Applied Water Science"},{"issue":"6","key":"14981_CR58","doi-asserted-by":"publisher","first-page":"1779","DOI":"10.1007\/s11600-020-00484-3","volume":"68","author":"S Sithara","year":"2020","unstructured":"Sithara, S., Pramada, S. K., & Thampi, S. G. (2020). Sea level prediction using climatic variables: A comparative study of SVM and hybrid wavelet SVM approaches. Acta Geophysica, 68(6), 1779\u20131790. https:\/\/doi.org\/10.1007\/s11600-020-00484-3","journal-title":"Acta Geophysica"},{"issue":"5","key":"14981_CR59","doi-asserted-by":"publisher","first-page":"2065","DOI":"10.1007\/s00477-024-02669-4","volume":"38","author":"S Sithara","year":"2024","unstructured":"Sithara, S., Pramada, S. K., & Thampi, S. G. (2024). Quantifying uncertainty in future sea level projections downscaled from CMIP5 global climate models. Stochastic Environmental Research and Risk Assessment, 38(5), 2065\u20132079. https:\/\/doi.org\/10.1007\/s00477-024-02669-4","journal-title":"Stochastic Environmental Research and Risk Assessment"},{"key":"14981_CR60","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/j.jhydrol.2014.01.023","volume":"511","author":"AY Sun","year":"2014","unstructured":"Sun, A. Y., Wang, D., & Xu, X. (2014). Monthly streamflow forecasting using Gaussian process regression. Journal of Hydrology, 511, 72\u201381. https:\/\/doi.org\/10.1016\/j.jhydrol.2014.01.023","journal-title":"Journal of Hydrology"},{"issue":"11","key":"14981_CR61","doi-asserted-by":"publisher","DOI":"10.3390\/w14111828","volume":"14","author":"N Sun","year":"2022","unstructured":"Sun, N., Zhang, S., Peng, T., Zhang, N., Zhou, J., & Zhang, H. (2022). Multi-variables-driven model based on random forest and Gaussian process regression for monthly streamflow forecasting. Water, 14(11), Article 1828. https:\/\/doi.org\/10.3390\/w14111828","journal-title":"Water"},{"key":"14981_CR62","doi-asserted-by":"publisher","DOI":"10.1007\/s11069-025-07203-5","author":"S Tumse","year":"2025","unstructured":"Tumse, S., & Alcansoy, U. (2025). Statistical and deep learning approaches in estimating present and future global mean sea level rise. Natural Hazards. https:\/\/doi.org\/10.1007\/s11069-025-07203-5","journal-title":"Natural Hazards"},{"issue":"17","key":"14981_CR63","doi-asserted-by":"publisher","DOI":"10.3390\/rs12172691","volume":"12","author":"S Wang","year":"2020","unstructured":"Wang, S., Zhou, J., Lei, T., Wu, H., Zhang, X., Ma, J., & Zhong, H. (2020). Estimating land surface temperature from satellite passive microwave observations with the traditional neural network, deep belief network, and convolutional neural network. Remote Sensing, 12(17), Article 2691. https:\/\/doi.org\/10.3390\/rs12172691","journal-title":"Remote Sensing"},{"issue":"6","key":"14981_CR64","doi-asserted-by":"publisher","first-page":"1351","DOI":"10.1007\/s10712-019-09531-1","volume":"40","author":"PL Woodworth","year":"2019","unstructured":"Woodworth, P. L., Melet, A., Marcos, M., Ray, R. D., W\u00f6ppelmann, G., Sasaki, Y. N., et al. (2019). Forcing factors affecting sea level changes at the coast. Surveys in Geophysics, 40(6), 1351\u20131397. https:\/\/doi.org\/10.1007\/s10712-019-09531-1","journal-title":"Surveys in Geophysics"},{"key":"14981_CR65","doi-asserted-by":"publisher","DOI":"10.1016\/j.ocemod.2025.102539","volume":"196","author":"Y Wu","year":"2025","unstructured":"Wu, Y., & Zhao, E. (2025). Sensitivity analysis of wind drift parameter for lagrangian particle model of dummy drift trajectory in the Northern South China Sea. Ocean Modelling, 196, Article 102539. https:\/\/doi.org\/10.1016\/j.ocemod.2025.102539","journal-title":"Ocean Modelling"},{"key":"14981_CR66","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119469","volume":"217","author":"Y Xie","year":"2023","unstructured":"Xie, Y., Sun, W., Ren, M., Chen, S., Huang, Z., & Pan, X. (2023). Stacking ensemble learning models for daily runoff prediction using 1D and 2D CNNs. Expert Systems with Applications, 217, Article 119469. https:\/\/doi.org\/10.1016\/j.eswa.2022.119469","journal-title":"Expert Systems with Applications"},{"key":"14981_CR67","unstructured":"Xu, Y. (2025). Smoothed particle hydrodynamics for fluid simulation in computer graphics. University of Groningen. Retrieved from http:\/\/hdl.handle.net\/11370\/7293fd68-8e55-4d4d-a65a-2ba0a5a8ea70"},{"key":"14981_CR68","doi-asserted-by":"publisher","DOI":"10.1016\/j.ocemod.2024.102463","volume":"193","author":"A Yavuzdo\u011fan","year":"2025","unstructured":"Yavuzdo\u011fan, A., & Tanir Kay\u0131k\u00e7\u0131, E. (2025). Advancing sea level anomaly modeling in the black sea with LSTM auto-encoders: A novel approach. Ocean Modelling, 193, Article 102463. https:\/\/doi.org\/10.1016\/j.ocemod.2024.102463","journal-title":"Ocean Modelling"},{"issue":"8","key":"14981_CR69","doi-asserted-by":"publisher","DOI":"10.3390\/jmse10081150","volume":"10","author":"Y Zhan","year":"2022","unstructured":"Zhan, Y., Zhang, H., Li, J., & Li, G. (2022). Prediction method for Ocean wave height based on stacking ensemble learning model. Journal of Marine Science and Engineering, 10(8), Article 1150. https:\/\/doi.org\/10.3390\/jmse10081150","journal-title":"Journal of Marine Science and Engineering"},{"key":"14981_CR70","doi-asserted-by":"publisher","DOI":"10.1016\/j.jocs.2023.102097","volume":"72","author":"J Zhou","year":"2023","unstructured":"Zhou, J., Wang, Z., Li, C., Wei, W., Wang, S., Armaghani, D. J., & Peng, K. (2023). Hybridized random forest with population-based optimization for predicting shear properties of rock fractures. Journal of Computational Science, 72, Article 102097. https:\/\/doi.org\/10.1016\/j.jocs.2023.102097","journal-title":"Journal of Computational Science"},{"key":"14981_CR71","doi-asserted-by":"publisher","DOI":"10.1016\/j.conbuildmat.2023.133911","volume":"409","author":"J Zhou","year":"2023","unstructured":"Zhou, J., Yang, P., Li, C., & Du, K. (2023). Hybrid random forest-based models for predicting shear strength of structural surfaces based on surface morphology parameters and metaheuristic algorithms. Construction and Building Materials, 409, Article 133911. https:\/\/doi.org\/10.1016\/j.conbuildmat.2023.133911","journal-title":"Construction and Building Materials"},{"issue":"4","key":"14981_CR72","doi-asserted-by":"publisher","first-page":"2057","DOI":"10.5194\/gmd-14-2057-2021","volume":"14","author":"L \u017dust","year":"2021","unstructured":"\u017dust, L., Fettich, A., Kristan, M., & Li\u010der, M. (2021). HIDRA 1.0: Deep-learning-based ensemble sea level forecasting in the northern Adriatic. Geoscientific Model Development, 14(4), 2057\u20132074. https:\/\/doi.org\/10.5194\/gmd-14-2057-2021","journal-title":"Geoscientific Model Development"}],"container-title":["Environmental Monitoring and Assessment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10661-026-14981-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10661-026-14981-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10661-026-14981-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T05:23:53Z","timestamp":1771478633000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10661-026-14981-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,19]]},"references-count":72,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["14981"],"URL":"https:\/\/doi.org\/10.1007\/s10661-026-14981-3","relation":{},"ISSN":["1573-2959"],"issn-type":[{"value":"1573-2959","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,19]]},"assertion":[{"value":"6 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"This study was completed in collaboration with Prof. Emad Elbeltagi, who sadly passed away after the manuscript was\u00a0revised. The authors respectfully dedicate this work to his memory, in recognition of his valuable scientific contributions, mentorship, and lasting impact on the research community.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Dedication"}}],"article-number":"147"}}