An application of local linear radial basis function neural network for flood prediction

IF 3.6 2区 管理学 Q2 BUSINESS Journal of Management Analytics Pub Date : 2019-01-18 DOI:10.1080/23270012.2019.1566033
B. Panigrahi, T. K. Nath, M. Senapati
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引用次数: 9

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 op...
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局部线性径向基函数神经网络在洪水预报中的应用
季节性大雨使航道发生洪水,是造成洪水的主要原因之一。洪水造成了各种危险,其后果包括对人类生命的危险、对建筑物、街道的伤害、对园艺场的不幸以及导致人类背井离乡。因此,预测洪水对于减少人员暴露和财产破坏至关重要。本文重点应用不同的神经网络方法,即多层感知器、径向基函数神经网络、局部线性径向基函数神经元网络和鲸鱼优化人工神经网络,从降雨量、表压、面积、速度、压力、平均温度等方面预测洪水,通过对“达亚”河和“巴尔加维”河的背景分析,通过现场和实验室调查确定的平均风速。洪水预报一直是一项麻烦的工作,因为许多因素都会影响它,尽管使用这种神经网络模型可以提高预报精度。。。
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来源期刊
Journal of Management Analytics
Journal of Management Analytics SOCIAL SCIENCES, MATHEMATICAL METHODS-
CiteScore
13.30
自引率
3.40%
发文量
14
期刊介绍: The Journal of Management Analytics (JMA) is dedicated to advancing the theory and application of data analytics in traditional business fields. It focuses on the intersection of data analytics with key disciplines such as accounting, finance, management, marketing, production/operations management, and supply chain management. JMA is particularly interested in research that explores the interface between data analytics and these business areas. The journal welcomes studies employing a range of research methods, including empirical research, big data analytics, data science, operations research, management science, decision science, and simulation modeling.
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