An application of local linear radial basis function neural network for flood prediction. Issue 1 (2nd January 2019)

• Record Type: Journal Article
• Authors: Panigrahi, Binaya Kumar; Nath, Tushar Kumar; Senapati, Manas Ranjan
• Is Part Of: Journal of management analytics. Volume 6:Issue 1(2019)
• Page Start: 67
• Page End: 87
• Publication Date: 2019-01-02
• Subjects: multilayer perceptron -- radial basis functional neural network -- local linear radial basis functional neural network -- whale optimization; Management -- Mathematical models -- Periodicals; Management -- Periodicals; Management -- Mathematical models; Management; Periodicals; 658.4033
• DOI: 10.1080/23270012.2019.1566033
• Languages: English
• ISSNs: 2327-0012

Abstract

Abstract : Heavy seasonal rain makes waterway flood and is one of the preeminent reason behind flooding. Flooding causes various perils with outcomes including danger to human life, harm to building, streets, misfortune to horticultural fields and bringing about human uprooting. Thus, prediction of flood is of prime importance so as to reduce exposure of people and destruction of property. This paper focuses on applying different neural networks approach, i.e. Multilayer Perceptron, Radial Basis functional neural network, Local Linear Radial Basis Functional Neural Network and Artificial Neural Network with Whale Optimization to predict flood in terms of rainfall, gauge, area, velocity, pressure, average temperature, average wind speed that are setup through field and lab investigation from the contextual analysis of river "Daya" and "Bhargavi". It has always been a troublesome undertaking to predict flood as many factors have influence on it although with this neural network models the prediction accuracy can be optimized using back propagation method which is a widely applied over traditional learning method for neural system because of its preeminent learning ability. The flood prediction system is built with the four models and a comparison is made which provides us the answer to which model is effective for the prediction.