{"title":"An application of local linear radial basis function neural network for flood prediction","authors":"B. Panigrahi, T. K. Nath, M. Senapati","doi":"10.1080/23270012.2019.1566033","DOIUrl":null,"url":null,"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...","PeriodicalId":46290,"journal":{"name":"Journal of Management Analytics","volume":" ","pages":""},"PeriodicalIF":3.6000,"publicationDate":"2019-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/23270012.2019.1566033","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Management Analytics","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1080/23270012.2019.1566033","RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BUSINESS","Score":null,"Total":0}
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...
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.