Forecasting agriculture water consumption based on PSO and SVM

Abstract

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.