A risk assessment method based on RBF artificial neural network - cloud model for urban water hazard

被引：38

作者：

Liu, Dengfeng ^{[1
]}

Wang, Dong ^{[1
]}

Wu, Jichun ^{[1
]}

Wang, Yuankun ^{[1
]}

Wang, Lachun ^{[2
]}

Zou, Xinqing ^{[2
]}

Chen, Yuanfang ^{[3
]}

Chen, Xi ^{[4
]}

机构：

[1] Nanjing Univ, Sch Earth Sci & Engn, Dept Hydrosci,Key Lab Surficial Geochem, MOE,State Key Lab Pollut Control & Resource Reuse, Nanjing 210008, Jiangsu, Peoples R China

[2] Nanjing Univ, Sch Geog & Oceanog Sci, Nanjing 210008, Jiangsu, Peoples R China

[3] Hohai Univ, Sch Hydrol & Water Resources, Nanjing, Jiangsu, Peoples R China

[4] Hohai Univ, Sch Hydrol & Water Resources, State Key Lab Hydrol Water Resources & Hydraul En, Nanjing, Jiangsu, Peoples R China

来源：

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS | 2014年 / 27卷 / 05期

关键词：

Risk assessment; cloud model; RBF artificial neural network; urban water hazard; time series analysis;

D O I：

10.3233/IFS-141210

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A risk assessment for urban water hazard based on RBF artificial neural network - Cloud model (RBF-ANN-Cloud) is proposed, according to the nonlinear characteristics, randomness and fuzziness in water hazard. Four assessment factors influencing urban water hazard are selected; the ranges of risk levels are calculated according to the Pearson-III frequency curve and the comprehensive cloud model of all risk levels belonging to assessment factors are generated. Historical data of assessment factors are simulated and forecasted by RBF artificial neural network; distribution curves of certainty degrees of risk levels are drawn, which indicate the final water hazard risk. Comparative researches with ARIMA and fuzzy decision-making set showed RBF-ANN-Cloud's suitability and effectiveness in water hazard risk assessment. RBF- Cloud model provides a new way of forecast and assessment of urban water hazard.

引用

页码：2409 / 2416

页数：8

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