基于ANN-产量的耕地地力定量评价模型及其应用

管理水平近似条件下的作物实际产量是耕地地力等级的直观反映,在目前传统耕地地力评价AHP-模糊评判方法的基础上,尝试建立耕地地力的ANN-产量定量评价模型,并以山东省鱼台县为研究对象进行了实例研究.采用相对隶属度对各评价指标进行描述,以实际产量为目标输出标准,经神经网络训练得到评价模型.与传统方法相比,模型不仅能反映耕地地力评价的非线性特征,而且评价过程中不需要确定权重,消除了传统方法确定权值时人为因素的影响,增加了评价结果的客观性.通过与传统方法的对比发现,该模型评价结果与现行耕地地力评价方法的结果较为一致,为耕地地力的定量评价探索了一条新路.

Evaluation model of cultivated land fertility using artificial neural network and productivity and its application

The crops productivity at the approximate management level reflects the cultivated land fertility grade. On the basis of AHP-fuzzy evaluation method, the ANN-productivity quantitative evaluation model was attempted to establish, and Yutai County, Shandong Provice is taken as case. In this evaluation model, used the relative membership grade to describe the evaluation index, took the crops productivity as the goal output standard, obtained the evaluation model after the neural network training. Compared with the traditional method, the model not only can reflect the non-linear characteristic of the cultivated land fertility, but also the process does not need to determine the weight, and this eliminated human factor influence. More importantly, the objectivity of evaluation results is further increased by using the crops productivity as the goal output. Through contrast with the traditional method, results of this model are consistent with the present evaluation method, and this method has explored a new way for the cultivated land fertility quantitative evaluation.