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DYNAMIC CHANGE FORECAST OF THE SALINE-ALKALI FARMLAND BASED ON THE NON-EQUAL TIME INTERVAL GREY MODEL

文献类型： 外文期刊

作者： Zhu, Huaji 1 ; Wu, Huarui 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

2.Key Lab Informat Technol Agr, Beijing 100097, Peoples R China

关键词： Farmland;Saline-alkali Degree;Forecast;Non-Equal Time Interval;Grey Model

期刊名称：INTELLIGENT AUTOMATION AND SOFT COMPUTING （ 影响因子：1.647； 五年影响因子：1.469 ）

ISSN： 1079-8587

年卷期： 2013 年 19 卷 3 期

页码：

收录情况： SCI

摘要： Salinization has become one of the most important questions about farmland in China. How to monitor and forecast the salinization trend of the farmland and to carry out valid prevention and cure measures is important for farmland protection in China. The grey model based on the grey system theory is different from the traditional regression forecast analysis. It doesn't need a large number of original data and doesn't require complex computation. It can avoid the fatal weakness of insufficient data and avoid the subjective factors on the forecast precision. By the mechanism of the grey model, the internal rules of the change of the saline-alkali farmland can be scientifically forecasted. It is usually executed with the mode of GM (1, 1). However, the GM (1,1) requires the equal time interval original data sequence. The saline-alkali degree always can't be monitored with the equal time interval. So, a new model by integrating the GM (1,1) with the least square method is proposed. With the new model, the original data sequence, the intervening results and the final forecast results are modified with the least square method. The results showed that the forecast precision was improved with the new model.

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