时空数据挖掘算法在玉米产量预测中的应用

Liying Cao , Xiaohui San , Yueling Zhao , Guifen Chen
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引用次数: 9

摘要

提出了一种基于时空数据挖掘的玉米产量预测新方法。该方法一方面利用统计原理对目标对象本身的序列进行预测。另一方面,该方法通过神经网络计算邻接区域土壤肥力空间分布对玉米产量的影响。最后,通过线性回归得到综合预测结果。采用该方法对试验区连续6年玉米产量进行预测,优于不考虑空间影响。误差控制在5%以内。本文为解决耕地对玉米产量影响的时空数据更新问题提供了一种新的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The application of the spatio-temporal data mining algorithm in maize yield prediction

This paper has advanced a new method of maize yield prediction which is based on the spatio-temporal data mining. On the one hand, this method predicts the sequence of the target object itself by adopting statistics principles. On the other hand, the method calculates the influence of the spatial distribution of adjacent-area soil fertility on maize yield by neural network. Finally, the method obtains the integrated forecast outcome by taking linear regression. Taking the method to predict the maize yield of experimental area for six consecutive years is better than taking no account of the spatial influence. The errors are controlled by 5%. This paper provides a new method to solve the spatio-temporal data update of farmland affected on the maize yield.

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来源期刊
Mathematical and Computer Modelling
Mathematical and Computer Modelling 数学-计算机:跨学科应用
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