[1]侯翔,廖小平. 基于PSO算法的洪水预报模型研究[J].计算机技术与发展,2015,25(04):200-203.
　HOU Xiang,LIAO Xiao-ping. Research on Flood Forecasting Model Based on PSO[J].,2015,25(04):200-203.
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 基于PSO算法的洪水预报模型研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
25
期数:
2015年04期
页码:
200-203
栏目:
应用开发研究
出版日期:
2015-04-10

文章信息/Info

Title:
 Research on Flood Forecasting Model Based on PSO
文章编号:
1673-629X（2015)04-0200-04
作者:
 侯翔; 廖小平
 四川文理学院 计算机学院
Author(s):
 HOU Xiang ; LIAO Xiao-ping
关键词:
 ; BP神经网络; 粒子群算法; 洪水流量; 洪水预报
Keywords:
 ; BP neural network; particle swarm optimization; flood flow; flood forecasting
分类号:
TP301.6
文献标志码:
A
摘要:
 受四川盆地地形与北部秦岭山脉的影响，达州市河流众多，洪灾频频发生，每次洪灾都给达州市政府和人民带来巨大的经济损失和惨重的人员伤亡。文中以四川省达州市州河流域为研究对象，采用达县水文站月平均流量作为洪水属性，提出了一种基于PSO算法优化的神经网络，并建立了洪水预报模型。通过实验仿真对比，其预报精度高于传统的BP神经网络，具有预报结果合理、相对误差小、收敛速度快、预报精度高等优点，从而能更有效地帮助防汛部门预报洪水，降低洪水带来的风险，还能够为达州市的防汛工作提供一定的参考意见。
Abstract:
 Influenced by the Sichuan basin and the north Qinling mountains, there are many rivers that flooding frequently occurs in Dazhou. Every time,floods bring huge economic loss and heavy casualties to the government and people. Taking Zhou River in Dazhou City,Sichuan Province,as the study objects,use the monthly average flow as flood properties collected by Daxian hydrological stations, a neural network based on PSO algorithm is proposed,and a flood forecasting model is established. By the simulation experiments,the forecast accuracy is higher than traditional BP neural network,the prediction result is reasonable with small relative error,fast convergence rate and high prediction accuracy. It will help flood control departments to predict flood flow effectively and reduce the risks of flooding, and also can provide some reference opinions to flood control work in Dazhou.

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更新日期/Last Update: 2015-06-05