Forecasting Agriculture Water Consumption Based on PSO and SVM

摘要：

Forecasting agriculture water consumption is significant to optimize confiration of water resources. In the paper, we have combined particle swarm optimization (PSO) and support vector machines (SVM) for agriculture water consumption forecasting. Compared to GA, the advantages of PSO are that PSO is easy to implement and there are few parameters to adjust. Thus, PSO is very suitable to determine training parameters of support vector machine. The experimental results demonstrate that the proposed PSOSVM model has good forecasting results in agriculture water consumption Forecasting.

关键词： support vector machine parameter optimization agriculture water consumption

作者: Sheng Lu Zhong-jinan Cai Xiao-bin Zhang

作者单位: School of Computer Science and Information Engineering Chongqing Technology and Business University Guangxi Special Equipment Supervision and Inspection Institute Nanning ,China

会议类型: 国际会议

会议名称: 2009 2nd IEEE International Conference on Computer Science and Information Technology(第二届计算机科学与信息技术国际会议 ICCSIT2009)

会议地点: 北京

会议语种:英文

页码: 2904-2907

在线出版日期: 2009-08-08（万方平台首次上网日期，不代表论文的发表时间）