农业工程学报
農業工程學報
농업공정학보
2014年
23期
298-305
,共8页
郄瑞卿%关侠%鄢旭久%窦世翔%赵玲
郄瑞卿%關俠%鄢旭久%竇世翔%趙玲
극서경%관협%언욱구%두세상%조령
地理信息系统%分等%神经网络%自组织特征映射%耕地%九台市
地理信息繫統%分等%神經網絡%自組織特徵映射%耕地%九檯市
지리신식계통%분등%신경망락%자조직특정영사%경지%구태시
geographic information system%classification%neural networks%self organizing feature map%arable land%Jiutai city
耕地质量各构成要素的特点和相互间的影响，决定了耕地质量的外在表现，客观地确定耕地自然质量，对耕地分等定级具有重要的意义。该文通过对已有耕地质量评价方法的优势与不足的分析，提出在空间数据库基础上应用自组织神经网络的耕地自然质量评价方法，并应用该方法对吉林省九台市耕地自然质量进行了评价，通过步长为1000次训练，自动生成13个类别，在13个类别基础上按照九台市的指定作物的光温生产潜力指数、作物的产量比系数，进行了耕地自然质量评价。根据评价分值的大小分为3等，其中质量等级Ⅰ级占全市耕地面积42.13%，Ⅱ级占全市耕地面积30.40%，Ⅲ级占全市耕地面积27.47%。评价结果与《九台市耕地质量更新成果》比较，图斑重合率为80.78%，面积重合率为79.42%。2种评价方法可能出现差异的原因：该文评价方法增加了坡度因子，且《九台市耕地质量更新成果》采用的是全省统一的指标权重；2种方法对于一些定性描述指标均通过信息赋权值法进行量化，而2种方法中量化方法不同，赋值不同。该方法将自组织神经网络和地理信息系统相结合，有效地集成影响耕地质量相关的土壤及土壤环境信息，利用自组织神经网络在没有教师信号时自动连接权值向着更利于竞争方向调整，通过度量评价单元的相似程度，使类间差异最大而类内差异最小，逐步将评价单元划分类别。根据每个类别中图斑自然质量指数的大小进行耕地质量等别评价，提高了评价结果的可信度，为耕地质量评价提供了新思路。
耕地質量各構成要素的特點和相互間的影響，決定瞭耕地質量的外在錶現，客觀地確定耕地自然質量，對耕地分等定級具有重要的意義。該文通過對已有耕地質量評價方法的優勢與不足的分析，提齣在空間數據庫基礎上應用自組織神經網絡的耕地自然質量評價方法，併應用該方法對吉林省九檯市耕地自然質量進行瞭評價，通過步長為1000次訓練，自動生成13箇類彆，在13箇類彆基礎上按照九檯市的指定作物的光溫生產潛力指數、作物的產量比繫數，進行瞭耕地自然質量評價。根據評價分值的大小分為3等，其中質量等級Ⅰ級佔全市耕地麵積42.13%，Ⅱ級佔全市耕地麵積30.40%，Ⅲ級佔全市耕地麵積27.47%。評價結果與《九檯市耕地質量更新成果》比較，圖斑重閤率為80.78%，麵積重閤率為79.42%。2種評價方法可能齣現差異的原因：該文評價方法增加瞭坡度因子，且《九檯市耕地質量更新成果》採用的是全省統一的指標權重；2種方法對于一些定性描述指標均通過信息賦權值法進行量化，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網絡和地理信息繫統相結閤，有效地集成影響耕地質量相關的土壤及土壤環境信息，利用自組織神經網絡在沒有教師信號時自動連接權值嚮著更利于競爭方嚮調整，通過度量評價單元的相似程度，使類間差異最大而類內差異最小，逐步將評價單元劃分類彆。根據每箇類彆中圖斑自然質量指數的大小進行耕地質量等彆評價，提高瞭評價結果的可信度，為耕地質量評價提供瞭新思路。
경지질량각구성요소적특점화상호간적영향，결정료경지질량적외재표현，객관지학정경지자연질량，대경지분등정급구유중요적의의。해문통과대이유경지질량평개방법적우세여불족적분석，제출재공간수거고기출상응용자조직신경망락적경지자연질량평개방법，병응용해방법대길림성구태시경지자연질량진행료평개，통과보장위1000차훈련，자동생성13개유별，재13개유별기출상안조구태시적지정작물적광온생산잠력지수、작물적산량비계수，진행료경지자연질량평개。근거평개분치적대소분위3등，기중질량등급Ⅰ급점전시경지면적42.13%，Ⅱ급점전시경지면적30.40%，Ⅲ급점전시경지면적27.47%。평개결과여《구태시경지질량경신성과》비교，도반중합솔위80.78%，면적중합솔위79.42%。2충평개방법가능출현차이적원인：해문평개방법증가료파도인자，차《구태시경지질량경신성과》채용적시전성통일적지표권중；2충방법대우일사정성묘술지표균통과신식부권치법진행양화，이2충방법중양화방법불동，부치불동。해방법장자조직신경망락화지리신식계통상결합，유효지집성영향경지질량상관적토양급토양배경신식，이용자조직신경망락재몰유교사신호시자동련접권치향착경리우경쟁방향조정，통과도량평개단원적상사정도，사류간차이최대이류내차이최소，축보장평개단원화분유별。근거매개유별중도반자연질량지수적대소진행경지질량등별평개，제고료평개결과적가신도，위경지질량평개제공료신사로。
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 pH 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 ofⅠ,ⅡandⅢgrade 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 in the nature quality grade Ⅰwith proposed method in this paper, the ratio of fifth, sixth and seventh grade which was classified with farmland natural quality grading of update results was 33.91%, 4.25%, 3.98%, respectively;in the nature quality gradeⅡwith proposed method in this paper, the ratio of fifth, sixth and seventh grade which was classified with farmland natural quality grading of update results was 4.58%, 25.38%, 0.45%, respectively;in the nature quality gradeⅢwith proposed method in this paper, the ratio of fifth, sixth and seventh grade which was classified with farmland natural quality grading of update results was 0.85%, 6.48%, 20.13%, respectively Quality gradeⅠ,ⅡandⅢ, classified with SOFM neural network corresponded to fifth, sixth and seventh grade of national nature grades classified with farmland natural quality grading of update results, figure spot coincidence rate and area overlap rate were 80.78%and 79.42%, respectively. The seasons for the discrepancies in the results of the two kinds evaluation method were that the slope factor was considered in proposed method, at the same time, uniform weights in Jiutai city, Jilin province was used in arable land natural quality grading of update results and qualitative description of the two kinds of evaluation methods for some indicators were quantified through information weighting value method, due to different quantization methods, different assignments. In this method, by self-organizing neural network combining with geographic information systems based on MATLAB, effectively influencing factors of arable land quality that related to soil and soil environment were integrated and quantification and space for spatial and non-spatial datum was realized. Especially, weights were subjectively determined, which made evaluation results more objective and true in propose method. And this method had more advantages in evaluating for highly nonlinear relationship between the arable land quality and its affecting parameters. Overall, the study method provides a new idea for arable land quality evaluation and broadens depth and breadth of arable land quality evaluation, and enhances the results credibility of natural quality evaluation of arable land.
