Flood River Water Level Forecasting using Ensemble Machine Learning for Early Warning Systems
Date
2022
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Publisher
IOP Publishing
Abstract
This 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.
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Citation
Faruq, 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.