Flood forecasting using committee machine with intelligent systems: a framework for advanced machine learning approach

• Main Authors: Faruq, Amrul, Abdullah, Shahrum Shah, Marto, Aminaton, Che Razali, Che Munira, Mohd. Hussein, Shamsul Faisal
• Format: Conference or Workshop Item
• Language: English
• Published: 2020
• Subjects: TA Engineering (General). Civil engineering (General); TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.utm.my/id/eprint/93809/1/ShahrumShah2020_FloodForecastingusingCommitteeMachine.pdf; http://eprints.utm.my/id/eprint/93809/; http://dx.doi.org/10.1088/1755-1315/479/1/012039

Among many natural hazards, flood disasters are the most incisive, causing tremendous casualties, in-depth injury to human life, property losses and agriculture, therefore affected the socioeconomic system of the area. Contributing to disaster risk reduction and the property damage associated with floods, the research on the advancement of flood modelling and forecasting is increasingly essential. Flood forecasting technique is one of the most significant current discussion in hydrological-engineering area, in which a highly complex system and difficult to model. The past decade has been seen the rapid development of machine learning techniques contributed extremely within the advancement of prediction systems providing better performance and efficient solutions. This paper proposes a framework design of flood forecasting model utilizing committee machine learning methods. Previously published works employing committee machine techniques in the analysis of the robustness of the model, effectiveness, and accuracy are particularly investigated on the used in various subjects. It is found that artificial neural networks, hybridizations, and model optimization are reported as the most effective ways for the improved development of machine learning methods. The proposed framework employs four representative intelligent systems as individual members, including radial basis neural networks, adaptive-neuro fuzzy, support vector machine and deep learning networks to construct a committee machine. As a conclusion, this committee machine with intelligent systems appears to be capable of enhancing the designing of flood forecasting model for disaster risk reduction.