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

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.

Description

Keywords

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.

Collections