Transactions of Nonferrous Metals Society of China The Chinese Journal of Nonferrous Metals

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Transactions of Nonferrous Metals Society of China

Vol. 24    No. 8    August 2014

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Rapid assessment of flood loss based on neural network ensemble
Xiao-sheng LIU1, Xiao HU1, Ting-li WANG2

1. School of Architecture and Survey Engineering, Jiangxi University of Science and Technology,
Ganzhou 341000, China;
2. School of Applied Science, Jiangxi University of Science and Technology, Ganzhou 341000, China

Abstract:Considering the defects of low accuracy and slow speed existing in traditional flood loss assessment, firstly, the technical route of flood loss assessment was presented based on the neural network ensemble. Secondly, through the study of certain country of Poyang Lake district, the flood loss assessment indicators of the test area were analyzed and extracted by utilizing analytic hierarchy process (AHP), and the weights of the impact factors were assigned. Subsequently, the approaches to generate individuals and conclusions of neural network ensemble model were also investigated. In the platform of C# language and neural network library under AForge.NET open source, a flood loss assessment program which could rapidly build neural network ensemble models was developed. Finally, the proposed method was tested and verified. The comparison results between the assessment results of the proposed method and the actual statistical flood loss proved the feasibility of this method, thus a new approach for flood loss assessment was provided.


Key words: neural network ensemble; flood loss; rapid assessment; AForge.NET

ISSN 1004-0609
CN 43-1238/TG

ISSN 1003-6326
CN 43-1239/TG

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