{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T05:53:13Z","timestamp":1774331593447,"version":"3.50.1"},"reference-count":80,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,4,9]],"date-time":"2021-04-09T00:00:00Z","timestamp":1617926400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Using advanced deep learning (DL) algorithms for forecasting significant wave height of coastal sea waves over a relatively short period can generate important information on its impact and behaviour. This is vital for prior planning and decision making for events such as search and rescue and wave surges along the coastal environment. Short-term 24 h forecasting could provide adequate time for relevant groups to take precautionary action. This study uses features of ocean waves such as zero up crossing wave period (Tz), peak energy wave period (Tp), sea surface temperature (SST) and significant lags for significant wave height (Hs) forecasting. The dataset was collected from 2014 to 2019 at 30 min intervals along the coastal regions of major cities in Queensland, Australia. The novelty of this study is the development and application of a highly accurate hybrid Boruta random forest (BRF)\u2013ensemble empirical mode decomposition (EEMD)\u2013bidirectional long short-term memory (BiLSTM) algorithm to predict significant wave height (Hs). The EEMD\u2013BiLSTM model outperforms all other models with a higher Pearson\u2019s correlation (R) value of 0.9961 (BiLSTM\u20140.991, EEMD-support vector regression (SVR)\u20140.9852, SVR\u20140.9801) and comparatively lower relative mean square error (RMSE) of 0.0214 (BiLSTM\u20140.0248, EEMD-SVR\u20140.043, SVR\u20140.0507) for Cairns and similarly a higher Pearson\u2019s correlation (R) value of 0.9965 (BiLSTM\u20140.9903, EEMD\u2013SVR\u20140.9953, SVR\u20140.9935) and comparatively lower RMSE of 0.0413 (BiLSTM\u20140.075, EEMD-SVR\u20140.0481, SVR\u20140.057) for Gold Coast.<\/jats:p>","DOI":"10.3390\/rs13081456","type":"journal-article","created":{"date-parts":[[2021,4,12]],"date-time":"2021-04-12T05:52:00Z","timestamp":1618206720000},"page":"1456","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["An EEMD-BiLSTM Algorithm Integrated with Boruta Random Forest Optimiser for Significant Wave Height Forecasting along Coastal Areas of Queensland, Australia"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8364-2644","authenticated-orcid":false,"given":"Nawin","family":"Raj","sequence":"first","affiliation":[{"name":"School of Sciences, University of Southern Queensland, Springfield, QLD 4300, Australia"}]},{"given":"Jason","family":"Brown","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, University of Southern Queensland, Springfield, QLD 4300, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,9]]},"reference":[{"key":"ref_1","first-page":"3","article-title":"Coastal vulnerability and risk parameters","volume":"11","author":"Doukakis","year":"2005","journal-title":"Eur. Water"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"137","DOI":"10.3354\/cr012137","article-title":"Vulnerability of island countries in the South Pacific to sea level rise and climate change","volume":"12","author":"Mimura","year":"1999","journal-title":"Clim. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.3844\/ajassp.2009.1169.1174","article-title":"Sea level threat in Tuvalu","volume":"6","author":"Aung","year":"2009","journal-title":"Am. J. Appl. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Hopley, D., Smithers, S.G., and Parnell, K. (2007). The Geomorphology of the Great Barrier Reef: Development, Diversity and Change, Cambridge University Press.","DOI":"10.1017\/CBO9780511535543"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/S0029-8018(99)00057-8","article-title":"A wave model for the Great Barrier Reef","volume":"28","author":"Hardy","year":"2001","journal-title":"Ocean Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2681","DOI":"10.4319\/lo.2008.53.6.2681","article-title":"Episodic circulation and exchange in a wave-driven coral reef and lagoon system","volume":"53","author":"Hench","year":"2008","journal-title":"Limnol. Oceanogr."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"9779","DOI":"10.1029\/JC094iC07p09779","article-title":"Wave transformation over coral reefs","volume":"94","author":"Young","year":"1989","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_8","unstructured":"Hardy, T., and Young, I. (1991). Modelling spectral wave transformation on a coral reef flat. Coastal Engineering: Climate for Change, Proceedings of the 10th Australasian Conference on Coastal and Ocean Engineering 1991, Auckland, New Zealand, 2\u20136 December 1991, Water Quality Centre, DSIR Marine and Freshwater."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.apor.2015.01.004","article-title":"Ocean wave energy harvesting with a piezoelectric coupled buoy structure","volume":"50","author":"Wu","year":"2015","journal-title":"Appl. Ocean Res."},{"key":"ref_10","unstructured":"McCormick, M.E. (2013). Ocean Wave Energy Conversion, Courier Corporation."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Pecher, A., and Kofoed, J.P. (2017). Handbook of Ocean Wave Energy, Springer.","DOI":"10.1007\/978-3-319-39889-1"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Thomsen, K. (2014). Offshore Wind: A Comprehensive Guide to Successful Offshore Wind Farm Installation, Academic Press.","DOI":"10.1016\/B978-0-12-410422-8.00017-0"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wang, H., Wang, J., Yang, J., Ren, L., Zhu, J., Yuan, X., and Xie, C. (2018). Empirical algorithm for significant wave height retrieval from wave mode data provided by the Chinese satellite Gaofen-3. Remote Sens., 10.","DOI":"10.3390\/rs10030363"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1016\/j.oceaneng.2015.05.038","article-title":"Short-term forecasting of the wave energy flux: Analogues, random forests, and physics-based models","volume":"104","author":"Esnaola","year":"2015","journal-title":"Ocean Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1016\/j.renene.2016.05.094","article-title":"Significant wave height and energy flux prediction for marine energy applications: A grouping genetic algorithm\u2013Extreme Learning Machine approach","volume":"97","year":"2016","journal-title":"Renew. Energy"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/S0029-8018(97)10025-7","article-title":"Real time wave forecasting using neural networks","volume":"26","author":"Deo","year":"1998","journal-title":"Ocean Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1016\/j.oceaneng.2016.10.033","article-title":"Regional ocean wave height prediction using sequential learning neural networks","volume":"129","author":"Savitha","year":"2017","journal-title":"Ocean Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"5695","DOI":"10.1109\/TVT.2018.2805189","article-title":"Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries","volume":"67","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_19","unstructured":"Cui, Z., Ke, R., Pu, Z., and Wang, Y. (2018). Deep bidirectional and unidirectional LSTM recurrent neural network for network-wide traffic speed prediction. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1158","DOI":"10.1016\/j.renene.2018.08.101","article-title":"New approach for solar tracking systems based on computer vision, low cost hardware and deep learning","volume":"133","author":"Carballo","year":"2019","journal-title":"Renew. Energy"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1016\/j.energy.2019.01.075","article-title":"Optimal load dispatch of community microgrid with deep learning based solar power and load forecasting","volume":"171","author":"Wen","year":"2019","journal-title":"Energy"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1245","DOI":"10.1016\/j.apenergy.2017.01.043","article-title":"A data-driven multi-model methodology with deep feature selection for short-term wind forecasting","volume":"190","author":"Feng","year":"2017","journal-title":"Appl. Energy"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1177\/0958305X18787258","article-title":"Prediction of wind speed using a new Grey-extreme learning machine hybrid algorithm: A case study","volume":"30","author":"Qolipour","year":"2019","journal-title":"Energy Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3814","DOI":"10.1109\/TIE.2018.2856205","article-title":"The optimized deep belief networks with improved logistic Sigmoid units and their application in fault diagnosis for planetary gearboxes of wind turbines","volume":"66","author":"Qin","year":"2019","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3196","DOI":"10.1109\/TIE.2018.2844805","article-title":"Multiscale convolutional neural networks for fault diagnosis of wind turbine gearbox","volume":"66","author":"Jiang","year":"2019","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1416","DOI":"10.1109\/TSTE.2015.2434387","article-title":"Predictive deep Boltzmann machine for multiperiod wind speed forecasting","volume":"6","author":"Zhang","year":"2015","journal-title":"IEEE Trans. Sustain. Energy"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.apenergy.2016.08.108","article-title":"Deep belief network based deterministic and probabilistic wind speed forecasting approach","volume":"182","author":"Wang","year":"2016","journal-title":"Appl. Energy"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Dalto, M., Matu\u0161ko, J., and Va\u0161ak, M. (2015, January 17\u201319). Deep neural networks for ultra-short-term wind forecasting. Proceedings of the Industrial Technology (ICIT), Seville, Spain.","DOI":"10.1109\/ICIT.2015.7125335"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.renene.2015.06.034","article-title":"Transfer learning for short-term wind speed prediction with deep neural networks","volume":"85","author":"Hu","year":"2016","journal-title":"Renew. Energy"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"113541","DOI":"10.1016\/j.apenergy.2019.113541","article-title":"Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms","volume":"253","author":"Ghimire","year":"2019","journal-title":"Appl. Energy"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"840","DOI":"10.1016\/j.energy.2018.09.118","article-title":"Deep belief network based k-means cluster approach for short-term wind power forecasting","volume":"165","author":"Wang","year":"2018","journal-title":"Energy"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"10348","DOI":"10.1109\/TVT.2019.2925562","article-title":"Behavioral modeling and linearization of wideband RF power amplifiers using BiLSTM networks for 5G wireless systems","volume":"68","author":"Sun","year":"2019","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zeng, Y., Yang, H., Feng, Y., Wang, Z., and Zhao, D. (2016). A convolution BiLSTM neural network model for Chinese event extraction. Natural Language Understanding and Intelligent Applications, Springer.","DOI":"10.1007\/978-3-319-50496-4_23"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1381","DOI":"10.1093\/bioinformatics\/btx761","article-title":"An attention-based BiLSTM-CRF approach to document-level chemical named entity recognition","volume":"34","author":"Luo","year":"2018","journal-title":"Bioinformatics"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Greenberg, N., Bansal, T., Verga, P., and McCallum, A. (November, January 31). Marginal likelihood training of bilstm-crf for biomedical named entity recognition from disjoint label sets. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium.","DOI":"10.18653\/v1\/D18-1306"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.envres.2015.02.002","article-title":"Improving forecasting accuracy of medium and long-term runoff using artificial neural network based on EEMD decomposition","volume":"139","author":"Wang","year":"2015","journal-title":"Environ. Res."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1377","DOI":"10.2166\/hydro.2013.134","article-title":"Improved annual rainfall-runoff forecasting using PSO\u2013SVM model based on EEMD","volume":"15","author":"Wang","year":"2013","journal-title":"J. Hydroinform."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhao, H., Sun, M., Deng, W., and Yang, X. (2017). A new feature extraction method based on EEMD and multi-scale fuzzy entropy for motor bearing. Entropy, 19.","DOI":"10.3390\/e19010014"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.physa.2018.09.120","article-title":"Improved EEMD-based crude oil price forecasting using LSTM networks","volume":"516","author":"Wu","year":"2019","journal-title":"Phys. A Stat. Mech. Appl."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Huang, Y., Liu, S., and Yang, L. (2018). Wind speed forecasting method using EEMD and the combination forecasting method based on GPR and LSTM. Sustainability, 10.","DOI":"10.3390\/su10103693"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"198935","DOI":"10.1109\/ACCESS.2020.3034113","article-title":"ELS-Net: A New Approach to Forecast Decomposed Intrinsic Mode Functions of Electricity Load","volume":"8","author":"Javaid","year":"2020","journal-title":"IEEE Access"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v036.i11","article-title":"Feature selection with the Boruta package","volume":"36","author":"Kursa","year":"2010","journal-title":"J. Stat. Softw."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ahmed, A.M., Deo, R.C., Ghahramani, A., Raj, N., Feng, Q., Yin, Z., and Yang, L. (2021). LSTM integrated with Boruta-random forest optimiser for soil moisture estimation under RCP4. 5 and RCP8. 5 global warming scenarios. Stoch. Environ. Res. Risk Assess., 1\u201331.","DOI":"10.1007\/s00477-021-01969-3"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.catena.2019.02.012","article-title":"Weekly soil moisture forecasting with multivariate sequential, ensemble empirical mode decomposition and Boruta-random forest hybridizer algorithm approach","volume":"177","author":"Prasad","year":"2019","journal-title":"Catena"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1029","DOI":"10.1007\/s11269-021-02770-1","article-title":"Binary Grey Wolf Optimization-Regularized Extreme Learning Machine Wrapper Coupled with the Boruta Algorithm for Monthly Streamflow Forecasting","volume":"35","author":"Qu","year":"2021","journal-title":"Water Resour. Manag."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1002\/wics.6","article-title":"Detection of outliers","volume":"1","author":"Hadi","year":"2009","journal-title":"Wiley Interdiscip. Rev. Comput. Stat."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1080\/00401706.1977.10489493","article-title":"Detection of Influential Observation in Linear Regression","volume":"19","author":"Cook","year":"1977","journal-title":"Technometrics"},{"key":"ref_48","first-page":"2319","article-title":"Identification of outliers by cook\u2019s distance in agriculture datasets","volume":"2","author":"Jagadeeswari","year":"2013","journal-title":"Int. J. Eng. Comput. Sci."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Metcalfe, A.V., and Cowpertwait, P.S. (2009). Introductory Time Series with R, Springer.","DOI":"10.1007\/978-0-387-88698-5"},{"key":"ref_50","first-page":"111","article-title":"Data preprocessing for supervised leaning","volume":"1","author":"Kotsiantis","year":"2006","journal-title":"Int. J. Comput. Sci."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Deo, R.C., Ghimire, S., Downs, N.J., and Raj, N. (2018). Optimization of windspeed prediction using an artificial neural network compared with a genetic programming model. Handbook of Research on Predictive Modeling and Optimization Methods in Science and Engineering, IGI Global.","DOI":"10.4018\/978-1-5225-4766-2.ch015"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.rse.2018.05.003","article-title":"Self-adaptive differential evolutionary extreme learning machines for long-term solar radiation prediction with remotely-sensed MODIS satellite and Reanalysis atmospheric products in solar-rich cities","volume":"212","author":"Ghimire","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1098\/rspa.1998.0193","article-title":"The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis","volume":"454","author":"Huang","year":"1998","journal-title":"Proc. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1142\/S1793536909000187","article-title":"The multi-dimensional ensemble empirical mode decomposition method","volume":"1","author":"Wu","year":"2009","journal-title":"Adv. Adapt. Data Anal."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1327","DOI":"10.1016\/j.ymssp.2008.11.005","article-title":"Application of the EEMD method to rotor fault diagnosis of rotating machinery","volume":"23","author":"Lei","year":"2009","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2222","DOI":"10.1109\/TNNLS.2016.2582924","article-title":"LSTM: A search space odyssey","volume":"28","author":"Greff","year":"2016","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Gers, F.A., Schmidhuber, J., and Cummins, F. (1999, January 7\u201310). Learning to forget: Continual prediction with LSTM. Proceedings of the 9th International Conference on Artificial Neural Networks (ICANN \u201999), Edinburgh, UK.","DOI":"10.1049\/cp:19991218"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Breuel, T.M., Ul-Hasan, A., Al-Azawi, M.A., and Shafait, F. (2013, January 25\u201328). High-performance OCR for printed English and Fraktur using LSTM networks. Proceedings of the 2013 12th International Conference on Document Analysis and Recognition, Washington, DC, USA.","DOI":"10.1109\/ICDAR.2013.140"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"3226","DOI":"10.1109\/TII.2018.2811377","article-title":"Replicating a trading strategy by means of LSTM for financial industry applications","volume":"14","author":"Troiano","year":"2018","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Chen, Y., Zhong, K., Zhang, J., Sun, Q., and Zhao, X. (2016, January 24\u201325). Lstm networks for mobile human activity recognition. Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications, Bangkok, Thailand.","DOI":"10.2991\/icaita-16.2016.13"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Mauch, L., and Yang, B. (2015, January 14\u201316). A new approach for supervised power disaggregation by using a deep recurrent LSTM network. Proceedings of the 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Orlando, FL, USA.","DOI":"10.1109\/GlobalSIP.2015.7418157"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/S0925-2312(97)00161-6","article-title":"Discrete time recurrent neural network architectures: A unifying review","volume":"15","author":"Tsoi","year":"1997","journal-title":"Neurocomputing"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/72.279181","article-title":"Learning long-term dependencies with gradient descent is difficult","volume":"5","author":"Bengio","year":"1994","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Sak, H., Senior, A., and Beaufays, F. (2014). Long short-term memory based recurrent neural network architectures for large vocabulary speech recognition. arXiv.","DOI":"10.21437\/Interspeech.2014-80"},{"key":"ref_65","unstructured":"Zhang, S., Zheng, D., Hu, X., and Yang, M. (November, January 30). Bidirectional long short-term memory networks for relation classification. Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation, Shanghai, China."},{"key":"ref_66","unstructured":"Sun, S., and Xie, Z. (2017, January 8\u201312). Bilstm-based models for metaphor detection. Proceedings of the National CCF Conference on Natural Language Processing and Chinese Computing, Dalian, China."},{"key":"ref_67","first-page":"203","article-title":"Support vector regression","volume":"11","author":"Basak","year":"2007","journal-title":"Neural Inf. Process. Lett. Rev."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1023\/B:STCO.0000035301.49549.88","article-title":"A tutorial on support vector regression","volume":"14","author":"Smola","year":"2004","journal-title":"Stat. Comput."},{"key":"ref_69","unstructured":"Vapnik, V., Golowich, S.E., and Smola, A.J. (1997). Support vector method for function approximation, regression estimation and signal processing. Advances in Neural Information Processing Systems, MIT Press."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.chaos.2015.10.019","article-title":"Study on network traffic forecast model of SVR optimized by GAFSA","volume":"89","author":"Liu","year":"2016","journal-title":"Chaos Solitons Fractals"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Willmott, C.J. (1984). On the evaluation of model performance in physical geography. Spatial Statistics and Models, Springer.","DOI":"10.1007\/978-94-017-3048-8_23"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/0022-1694(70)90255-6","article-title":"River flow forecasting through conceptual models part I\u2014A discussion of principles","volume":"10","author":"Nash","year":"1970","journal-title":"J. Hydrol."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1029\/1998WR900018","article-title":"Evaluating the use of \u201cgoodness-of-fit\u201d measures in hydrologic and hydroclimatic model validation","volume":"35","author":"Legates","year":"1999","journal-title":"Water Resour. Res."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1080\/02723646.1981.10642213","article-title":"On the validation of models","volume":"2","author":"Willmott","year":"1981","journal-title":"Phys. Geogr."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1061\/(ASCE)1084-0699(2006)11:6(597)","article-title":"Evaluation of the Nash\u2013Sutcliffe efficiency index","volume":"11","author":"McCuen","year":"2006","journal-title":"J. Hydrol. Eng."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1061\/(ASCE)1084-0699(2008)13:10(981)","article-title":"Fitting of hydrologic models: A close look at the Nash\u2013Sutcliffe index","volume":"13","author":"Jain","year":"2008","journal-title":"J. Hydrol. Eng."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"59","DOI":"10.13031\/2013.15870","article-title":"Statistical procedures for evaluating daily and monthly hydrologic model predictions","volume":"47","author":"Coffey","year":"2004","journal-title":"Trans. ASAE"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.jhydrol.2007.08.004","article-title":"Coupling of hydrologic and hydraulic models for the Illinois River Basin","volume":"344","author":"Lian","year":"2007","journal-title":"J. Hydrol."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"523","DOI":"10.2112\/09-1186.1","article-title":"Coastal vulnerability assessment for Orissa State, east coast of India","volume":"26","author":"Kumar","year":"2010","journal-title":"J. Coast. Res."},{"key":"ref_80","unstructured":"Queensland Government (2020, January 01). Queensland Government Open Data Portal, Available online: https:\/\/www.data.qld.gov.au\/dataset\/coastal-data-system-historical-wave-data."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/8\/1456\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:27:38Z","timestamp":1760365658000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/8\/1456"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,9]]},"references-count":80,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["rs13081456"],"URL":"https:\/\/doi.org\/10.3390\/rs13081456","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,9]]}}}