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

Faruq, Amrul and Marto, Aminaton and Abdullah, Shahrum Shah (2022) Flood River Water Level Forecasting using Ensemble Machine Learning for Early Warning Systems. In: IOP Conference Series: Earth and Environmental Science. IOP Science Publishing. ISBN 1755-1315

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Abstract

Flood forecasting is crucial for early warning system and disaster risk reduction. Yet the flood river water levels are difficult and challenging task that it cannot be easily captured with classical time-series approaches. 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. The results show that ANFIS outperforms as individual model, while ICML-FF-based model produced better accuracy and lowest error than any one of the individuals. In general, it is found that the proposed ICML-FF is capable of robust forecasting model for flood early warning systems.

Item Type: Book Section / Proceedings
Keywords: Flood Forecasting Ensemble Machine Learning Early Warning System
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering > Department of Electrical Engineering (20201)
Depositing User: faruq Amrul Faruq
Date Deposited: 23 Apr 2024 05:58
Last Modified: 23 Apr 2024 05:58
URI: https://eprints.umm.ac.id/id/eprint/5712

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