基于深度置信网络的农用地基准地价评估模型

该文针对现有基准地价评估模型主观性较强及精度不够等问题，提出了一种基于深度学习思想的农用地基准地价评估方法，构建了样本特征集合与地价标签集合的深层网络结构映射关系，并以广东省普宁市农用地基准地价评估为实例，验证了模型的可行性和有效性。结果如下：1）与神经网络、支持向量机这类浅层学习模型相比，深度置信网络模型其对地价的拟合精度要高出3.61%，3.12%；2）在训练样本只有300时，深度置信网络模型对地价的拟合误差为16.43%，比神经网络、支持向量机模型的拟合精度最少要高出6.76个百分点；3）深度置信网络模型对单个样本的运算时间及内存占用比神经网络、支持向量机模型都要高，但在评估精度都达到95%左右的情况下，深度置信网络模型所需训练样本较少，支持向量机训练时间为193 s，而深度置信网络模型耗时187 s，两者耗时基本持平；4）基于DBN模型对耕地评估单元的地价测算结果，将将普宁市耕地评估单元划分为5级，然后利用面积加权法求取对应的级别基准地价范围为21.34～26.23元/m2。上述试验结果表明该方法在对样本点地价的评估精度上要优于传统的浅层方法，并且该模型计算所得普宁市耕地基准地价与耕地质量在空间分布规律上保持一致。

Establishment of agricultural land appraisal model based on deep belief network

Abstract: The basic prices of agricultural land appraisal (ALA) has the extremely vital significance on establishing a complete set of agricultural land price system and smoothly carry out land assets, such as reasonable allocation of land consolidation rural land management. Existing models such as artificial neural networks, support vector machines, multivariable regression cannot build the complex function relationship fully between the affecting factors and land prices, and the above-mentioned models with shallow structure have no ability to handle a high-dimensional sample set for land appraisal. So deep learning method was firstly introduced into ALA, and a novel method for ALA based on deep belief networks (DBN) was proposed. A group of 19-dimensional original features reflecting status of land location and quality were employed as inputs, and the land prices were used as outputs of DBN model. The parameters of DBN model were firstly initialized by unsupervised learning method with no-label samples, and then fine-tuned by supervised learning method with labeled samples. The land price of each assessment unit can be calculated by using the well-trained DBN deep neural network with the input of feature vector, and the level of assessment unit can be determined by taking advantage of total value frequency distribution histogram, then the benchmark land price for each level can be calculated via the method of area weighted technique. Take the city of Puning in Guangdong province as a case study, feasibility and validity of the model was validated. The results of the present study indicate that: 1) With respect to the artificial neural networks and support vector machines models, the DBN model get better assessment accuracy with a slight increase of 3.61% and 3.12% because of it is able to take the advantage of feature extraction of deep structure, and can enhance its generation ability by a large amount of no-label land price samples; 2) The simulating error of DBN model for land price appraisal is 16.43% when the number of training samples is only 300, which is less than the artificial neural networks and support vector machines models with a least reduction of 6.76%, DBN model gets high assessment accuracy with a small amount of training samples resorting to its unsupervised learning framework, and the assessment accuracy increases with the number of no-label samples; 3) The running time and memory usage of DBN for single training sample is higher than the artificial neural networks and support vector machines models, and the running time of support vector machines model is 193 and 187 s for DBN model when the assessment accuracy reaches about 95%, both of which are equal because of the DBN model need less training samples than the SVM model; 4) The cultivated land assessment units of Puning city are divided into 5 levels based on the their land price results which are calculated based on the DBN model, and the benchmark land price of Puning city ranges from 21.34 to 26.23 yuan/m2 which are calculated based on the area weighted technique. The above experiment results indicate that the assessment accuracy of DBN model is significantly better than the models with shallow structure, and the spatial distribution pattern of benchmark land price and quality level of cultivated land for Puning city are consistent with each other. It is concluded that the method is feasible and effective in measurement and calculation on agricultural base land price.