Flood River Water Level Forecasting using Ensemble Machine Learning for Early Warning Systems

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dc.contributor.authorAmrul Faruq
dc.contributor.authorShamsul Faisal Mohd Hussein
dc.contributor.authorAminaton Marto
dc.contributor.authorShahrum Shah Abdullah
dc.coverage.publicationMalaysia
dc.date.accessioned2024-05-20T01:11:07Z
dc.date.available2024-05-20T01:11:07Z
dc.date.issued2022
dc.description.abstractThis study proposed a novel intelligence system utilised various machine learning techniques as individual models, including radial basis function neural network (RBF-NN), adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM), and long short-term memory network (LSTM) to establish intelligent committee machine learning flood forecasting (ICML-FF) framework. The combination of these individual models achieved through simple averaging method, and further optimised using weighted averaging by K-nearest neighbour (K-NN) and genetic algorithm (GA). The effectiveness of the proposed model was evaluated using real case study for Malaysia's Kelantan River.
dc.identifier.citationFaruq, A., Hussein, S. F. M., Marto, A., & Abdullah, S. S. (2022). Flood River Water Level Forecasting using Ensemble Machine Learning for Early Warning Systems. In IOP Conference Series: Earth and Environmental Science (Vol. 1091, No. 1, p. 012041). IOP Publishing.
dc.identifier.urihttps://repoemc.ukm.my/handle/123456789/1192
dc.language.isoen
dc.publisherIOP Publishing
dc.publisher.alternativeIOP Conference Series: Earth and Environmental Science
dc.titleFlood River Water Level Forecasting using Ensemble Machine Learning for Early Warning Systems
dc.typeJournal

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