本文關(guān)鍵詞：基于自組織神經(jīng)網(wǎng)絡(luò)的耕地自然質(zhì)量評價方法及其應(yīng)用，，由筆耕文化傳播整理發(fā)布。

基于自組織神經(jīng)網(wǎng)絡(luò)的耕地自然質(zhì)量評價方法及其應(yīng)用

Method and its application of natural quality evaluation of arable land based on self-organizing feature map neural network

[1] [2] [3] [4] [5]

Qie Ruiqing, Guan Xia, Yan Xujiu, Dou Shixiang, Zhao Ling（1. College of Economics and Management, Jilin Agricultural University, Changchun 130118, China; 2. Institute of Land

[1]吉林農(nóng)業(yè)大學(xué)經(jīng)濟管理學(xué)院,長春130118; [2]吉林省國土勘測規(guī)劃研究院,長春130214

文章摘要：耕地質(zhì)量各構(gòu)成要素的特點和相互間的影響,決定了耕地質(zhì)量的外在表現(xiàn),客觀地確定耕地自然質(zhì)量,對耕地分等定級具有重要的意義。該文通過對已有耕地質(zhì)量評價方法的優(yōu)勢與不足的分析,提出在空間數(shù)據(jù)庫基礎(chǔ)上應(yīng)用自組織神經(jīng)網(wǎng)絡(luò)的耕地自然質(zhì)量評價方法,并應(yīng)用該方法對吉林省九臺市耕地自然質(zhì)量進行了評價,通過步長為1 000次訓(xùn)練,自動生成13個類別,在13個類別基礎(chǔ)上按照九臺市的指定作物的光溫生產(chǎn)潛力指數(shù)、作物的產(chǎn)量比系數(shù),進行了耕地自然質(zhì)量評價。根據(jù)評價分值的大小分為3等,其中質(zhì)量等級Ⅰ級占全市耕地面積42.13%,Ⅱ級占全市耕地面積30.40%,Ⅲ級占全市耕地面積27.47%。評價結(jié)果與《九臺市耕地質(zhì)量更新成果》比較,圖斑重合率為80.78%,面積重合率為79.42%。2種評價方法可能出現(xiàn)差異的原因：該文評價方法增加了坡度因子,且《九臺市耕地質(zhì)量更新成果》采用的是全省統(tǒng)一的指標(biāo)權(quán)重;2種方法對于一些定性描述指標(biāo)均通過信息賦權(quán)值法進行量化,而2種方法中量化方法不同,賦值不同。該方法將自組織神經(jīng)網(wǎng)絡(luò)和地理信息系統(tǒng)相結(jié)合,有效地集成影響耕地質(zhì)量相關(guān)的土壤及土壤環(huán)境信息,利用自組織神經(jīng)網(wǎng)絡(luò)在沒有教師信號時自動連接權(quán)值向著更利于競爭方向調(diào)整,通過度量評價單元的相似程度,使類間差異最大而類內(nèi)差異最小,逐步將評價單元劃分類別。根據(jù)每個類別中圖斑自然質(zhì)量指數(shù)的大小進行耕地質(zhì)量等別評價,提高了評價結(jié)果的可信度,為耕地質(zhì)量評價提供了新思路。

Abstr：The characteristics and interactions of arable land quality components determine the external manifestation of arable land quality. It was the vital significance to objectively determine natural quality of arable land for arable land classification and grading. In this paper, advantage and disadvantage of the existing methods used in the arable land natural quality evaluation was analyzed, SOFM（self organizing feature map） neural network method was proposed to evaluate arable land natural quality basing on spatial database in Jiutai city of Jilin province. The nine evaluation indicators including surface soil texture, profile configuration, content of soil organic matter, soil p H value, barrier layer depth, soil salinity, effective soil depth, drainage condition and slope, were chosen and the corresponding database layer was established in the method. At the same time, attribute values were input and data were normalized. By training step for 1000, the system automatically generated 13 categories. Based on temperature potential productivity index and crop yield ratio of Jiutai city, evaluation index of arable land natural quality were calculated based on GIS. According the size of arable land natural quality indicators, the arable land natural quality of Jiutai city was divided into 3 grades. The ratios of Ⅰ, Ⅱ and Ⅲ grade of arable land nature quality which was classified with proposed method in this paper to the total arable land area in Jiutai city were 42.13%, 30.40%, 27.47%, respectively. The natural quality evaluation results of arable land based on SOFM neural network were compared with that with farmland natural quality grading of update results in Jiutai city. The comparison results indicated that

文章關(guān)鍵詞：

Keyword：:geographic information system classification neural networks self organizing feature map arable land Jiutai city

課題項目：國家自然科學(xué)基金（41071160）; 吉林省科技發(fā)展計劃項目（20150418079FG）; 吉林省教育廳項目（20100068）; 吉林省社科基金項目（2012BS32）; 長春市哲學(xué)社會科學(xué)規(guī)劃項目（CSK2014ZYJ-0018）

作者信息：會員可見

  本文關(guān)鍵詞：基于自組織神經(jīng)網(wǎng)絡(luò)的耕地自然質(zhì)量評價方法及其應(yīng)用，由筆耕文化傳播整理發(fā)布。