基于自适应进化相关向量机的耕地面积预测模型

为解决耕地面积预测模型建立过程中的非线性、稀疏化及结果可靠性评价等问题,该文将相关向量机与差分进化优化算法进行融合及改进,提出基于自适应进化相关向量机的耕地面积预测模型。该文以黄石市为例,建立基于自适应进化相关向量机的短期、中期耕地预测模型,并与多元线性回归、BP神经网络、支持向量机算法在精度、计算效率及可靠性方面进行对比分析。试验验数据表明,自适应进化相关向量机的预测精度大约是其余3种方法的2倍以上;模型的计算效率是多元线性回归方法的2倍,比BP神经网络、支持向量机高出2个数量级;测试数据的实际耕地面积均在自适应进化相关向量机估计的95%置信度的置信区间内,并且由后验差比、小误差概率判定模型等级属于"好"。基于以上数据,证实该模型是一种精度高、计算快、可靠性强的耕地预测新方法。

Prediction model for cultivated land area based on self-adaptive differential evolution and relevance vector machine

Abstract: In order to solve some key problems existing in the previous prediction model for cultivated land area, for instance, the nonlinearity, sparseness and the result reliability, a new prediction model for cultivated land area is proposed through fusing and improving the self-adaptive evolution and relevance vector machine in the paper. By analyzing the characteristics of convergence rate for differential evolution algorithm, the functional relationship among shrinkage ratio factor, crossover probability, maximum fitness and minimum fitness is established. Meanwhile, individual shrinkage ratio factor and crossover probability of the next generation are determined based on the current individual fitness data. And the self-adaptive differential evolution algorithm is also developed in this way which can effectively improve the global convergence ability and the robustness of the algorithm. The current studies have confirmed that the kernel parameters have a greater impact on the prediction accuracy of relevance vector machine. Therefore, in order to improve the accuracy of the model, fitness function is established based on leave one cross validation and the relevance vector machine on the basis of self-adaptive differential evolution is also proposed by optimizing the kernel parameters. As the new model has the advantages of sparsity and nonlinearity, and can output the information of the uncertainty of the results, the new method is used to predict the cultivated land area. In order to prove the excellent properties of the new method, the accuracy of the model is evaluated by choosing 5 kinds of precision indices including mean absolute error (MAE), mean relative error (MRE), root mean square error (RMSE), posterior error and error frequency. The computational efficiency and the reliability of the model are estimated quantitatively by running time and confidence interval. Taking Huangshi City as an example, a short-term and a middle-term prediction model for the cultivated land area are set up on the basis of the self-adaptive evolution and relevance vector machine. And these two established prediction models are also compared with the multivariate linear regression model, back propagation (BP) neural network and least squares support vector machine in terms of accuracy, computational efficiency and reliability. The experimental statistics reveal that the newly established prediction model based on the self-adaptive evolution and relevance vector machine is about 2 times higher than the rest 3 models in accuracy, 2 times as much as multivariate linear regression model and 2 orders of magnitude higher than the BP neural network and least squares support vector machine in computational efficiency; the actual land area of the test data set is all in confidence intervals at the 95% confidence level, which is obtained by prediction model for cultivated land area based on self-adaptive differential evolution and relevance vector machine. All the above data confirms that the model based on the self-adaptive evolution and relevance vector machine is a new approach to the prediction of the cultivated farm land with high accuracy, fast calculation and strong reliability.