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乌兰布和灌域有效灌溉面积预测模型研究与应用 被引量:3

Effective Irrigation Area of Ulan Buh Irrigation Domain Forecast Model Research and Application
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摘要 为了使乌兰布和灌域有效灌溉面积得以健康发展,同时了解乌兰布和灌域有效灌溉面积的发展趋势,引入了支持向量机回归滑动预测模型,以归一化处理后的2007-2016年的有效灌溉面积数据作为训练样本,以2017年有效灌溉面积数据作为检验样本,结合新陈代谢法,对乌兰布和灌域有效灌溉面积进行预测。同时引入Logistic灰色预测模型,与支持向量机回归滑动预测模型进行对比分析,结果表明支持向量机回归滑动模型的预测精度明显高于Logistic灰色预测模型。在此基础上,应用支持向量机回归滑动预测模型对2018-2025年乌兰布和灌域各年份的有效灌溉面积进行了预测,结果表明乌兰布和灌域有效灌溉面积将于2022年达到其上限5.36万hm^2,2017年有效灌溉面积为5.244万hm^2,已经接近其上限,且乌兰布和灌域有效灌溉面积增速将于2018年开始减缓,在某些年份甚至出现负增长。因此,需要对有效灌溉面积发展过程中的影响因素进行分析和调整,为其健康发展奠定基础。 In order to promote the healthy development of the effective irrigation area of Ulanbuh and understand the development trend of effective irrigation area of Ulanbuh,this study introduces the regression sliding prediction model of Support Vector Machine,the effective irrigation area data of 2007-2016 is used as a training sample,the effective irrigation area data for 2017 is used as a test sample,and combined with metabolic method,the effective irrigation area of Ulanbuh irrigation field is predicted. In order to verify the effectiveness and superiority of the model. The Logistic grey prediction model is introduced and compared with Support Vector Machine regression sliding prediction model. The results show that the prediction accuracy of the Support Vector Machine regression sliding model is significantly higher than that of the Logistic grey prediction model. On this basis,the regression model of Support Vector Regression is used to predict the effective irrigated area in each year of Ulanbu and Irrigation from 2018 to 2025,the effective irrigation area of Ulanbuh irrigation field in 2022 will reach its upper limit of 53 600 hectares; the effective irrigation area of Ulanbuh irrigation field in 2017 was 52 440 hectares,which has been close to its upper limit. It can be seen from the predicted results that the growth rate of effective irrigation area in Ulanbuh irrigation field will start to slow in 2018,and the effective irrigation area will even have negative growth in some years. Therefore,it is necessary to analyze the factors affecting the development of effective irrigation area and lay the foundation for healthy development of Ulanbuh irrigation field.
作者 田鑫 李瑞平 王艳明 王思楠 范雷雷 樊爱霞 TIAN Xin;LI Rui-ping;WANG Yan-ming;WANG Si-nan;FAN Lei-lei;FAN Ai-xia(College of Water Conservancy and Civil Engineering,Inner Mongolia Agricultural University,Hohhot 010018,China;The Ulanbuh Irrigation Authority of Inner Mongolia Hetao Irrigation District,Bayannur 015200,Inner Mongolia,China;Inner Mongolia Water Conservancy and Hydropower Survey and Design Institute,Hohhot 010020,China)
出处 《节水灌溉》 北大核心 2018年第11期115-119,共5页 Water Saving Irrigation
基金 国家自然科学基金项目(51769021)
关键词 支持向量机 新陈代谢法 灰色预测模型 有效灌溉面积 Support Vector Machine (SVM) metabolism method grey prediction model effective irrigation area
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