Borwarnginn P.Haga J.H.Kusakunniran W.Mahidol University2023-06-182023-06-182022-12-01ICT Express Vol.8 No.4 (2022) , 588-594https://repository.li.mahidol.ac.th/handle/20.500.14594/84236The paper presents the river height prediction model using real-world historical sensor data such as rainfall, cumulative rainfall, and river water heights. The study evaluates using a Support Vector Regression, a Long Short-Term Memory, and a combination of a Long Short-Term Memory as the feature extraction and a support vector regression. Through experiments, various future predictions are tested, including a few hours or a day. As expected, RNN achieved the lowest error, but it could not capture rapid changes in river height levels. In comparison, the LSTM-SVR can better represent rapid transient changes in the data by using nonlinear kernels.Computer ScienceArticleSCOPUS10.1016/j.icte.2022.03.0122-s2.0-8512788080224059595