节点文献
基于支持向量机的干旱预测研究
Study of Drought Prediction Based on Support Vector Machine
【作者】 张波；
【导师】 马利；
【作者基本信息】 南京信息工程大学 ， 气象信息技术与安全， 2012， 硕士
【摘要】 干旱是我国主要的自然灾害之一,也是大气科学研究的热点问题之一。本文选用支持向量机方法,对建立基于气候因子的土壤湿度预报模型以及基于时间序列的干旱等级预测模型进行了研究,研究结果将有益于实现大面积的干旱预测,为政府和有关部门进行防旱减灾提供科学依据。本文的主要结论如下：(1)本文设计了基于支持向量机(SVM)的土壤相对湿度混合预报模型,进行了仿真实验,并且针对传统支持向量机在参数选择方法上的不足,使用网格划分法、遗传算法和粒子群算法对模型的参数进行了优化。仿真实验结果表明,本文提出的模型提供了一种新的直接基于作物水分信号所无法实现的土壤湿度预报方法,为解决非线性问题提供了新思路。(2)本文研究了基于SVM的时间序列预测模型的构建,经参数寻优后建立模型,并对某站月平均温度和降水量仿真预测。然后根据预测结果计算干旱指数,预测干旱等级。预报结果比较准确,表明这种干旱预测方法是行之有效的,具有实用性。
【Abstract】 The drought is one of the main natural disasters. It is also one of the hot issues of the atmosphere of scientific research. This paper used the method of Support Vector Machine, and studied for based on climate factor of soil humidity mixed forecasting model and based on time series of Drought Prediction model. The results of the study will benefit to the widespread drought monitoring for government and relevant departments to provide the scientific basis of Drought Relief.The main conclusions are as follows:(1) This paper designed a model of soil relative humidity mixed forecasting which based on Support Vector Machine (SVM). and conducted the simulation experiments. And then according to the traditional support vector machine’s weaknesses on the method of choosing parameters, grid partition method. genetic algorithm and particle swarm algorithm are used to optimize the parameters of the model. The simulation results show that, the introduction of the SVM method provides a new tool for predicting soil moisture, in addition, the method which directly based on crop water signal is unable to achieve, and for the nonlinear problem solving provides a new idea.(2) This paper studied the time series prediction model which based on the SVM, established model after optimizing parameters, and conducted the simulation prediction with the data of one station’s average temperature and precipitation. And then based on the forecasting results calculated out the drought index, and then predicted drought level. The forecast results is accurate, shows that the drought forecast method is effective and practical.