@article{scholars17895,
            year = {2022},
       publisher = {Taylor and Francis Ltd.},
         journal = {Geocarto International},
           pages = {6892--6914},
            note = {cited By 5},
          volume = {37},
          number = {23},
             doi = {10.1080/10106049.2021.1958015},
           title = {A deep-learning model for national scale modelling and mapping of sea level rise in Malaysia: the past, present, and future},
        abstract = {In this study, we conducted a holistic evaluation of current and future trend in coastal sea level at the 21 stations along Malaysia{\^a}??s coastline. For sea level prediction, univariate and 3 scenarios of multivariate Long Short Term Memory (LSTM) neural networks were trained with absolute sea level data and ocean-atmospheric variables. The result from the four scenario predictive models revealed that multivariate LSTM neural network trained with combined ocean-atmospheric variables performed best for modelling sea level variation, giving a mean RMSE and R accuracy of 0.060 and 0.861, respectively. The national sea level rise estimated from the average of sea level trend at all stations is 3.72 mm/yr for relative sea level and 3.68 mm/yr for absolute sea level. The 2050 and 2100 projections indicate that sea level will continue to rise but at a very slow rate with no acceleration. {\^A}{\copyright} 2021 Informa UK Limited, trading as Taylor \& Francis Group.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111847855&doi=10.1080\%2f10106049.2021.1958015&partnerID=40&md5=04b087ef0ff1b499e0d34e5f1db40c4a},
        keywords = {air-sea interaction; future prospect; holistic approach; mapping method; sea level change; trend analysis, Malaysia},
          author = {Adebisi, N. and Balogun, A.-L.},
            issn = {10106049}
}