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首页> 外文会议>2004 CIGR International Conference : Collection of Extent Abstracts >The Study on Prediction of Regional Farmland WaterEnvironment Change after the Water Saving ReconstructionProject Practice- Case Study a Large-Scale IrrigationDistrict of China
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The Study on Prediction of Regional Farmland WaterEnvironment Change after the Water Saving ReconstructionProject Practice- Case Study a Large-Scale IrrigationDistrict of China

机译：节水改造工程实施后区域农田水环境变化预测研究-以中国大型灌区为例

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To meet the need of water environment evaluation and management decision forplanning and design in the water saving reconstruction project of large-scale irrigation district,this paper based on BP model and RBF model of artificial neural networks theory, which isimportant branch of artificial intelligence technology, to build the forecast models of regionalsoil-water (salt) environment. The ANN technology is systematically applied in the forecastand evaluation of regional soil-water (salt) environment based on the two different scalesexperimental zones (Jiefangzha irrigation area and Shahaoqu experimental area) of HetaoIrrigation district of Inner Mongolia. According to the selected BP model and related watertable depth records and other information, the annual and monthly mean water table depthtrends in the future planning year (2005, 2010) are forecasted on condition that water savingreconstruction projects are accomplished in the larger scale experimental zone (Jiefangzha)of Hetao Irrigation district. Then, the results of BP are compared with that of RBF model andregional water equilibrium method. From the prediction results of several methods we haveknown that: the annual mean water level in the 2010 will decrease about 0.6m comparing withpresent average level. The decrease range will relatively significant. Based on five yearsregional water-salt monitoring data and related hydrological and weather information in thesmaller scale experiment zone (Shahaoqu), the regional water-soil (salt) environment regimeare simulated, tested and predicted using BP and RBF model. We draw the conclusions asfollows from prediction results: when the total water quantity is reduced 30% of presentaverage amount in the 2010, the total salinity of groundwater will have significant increase inthe different period (pre-summer irrigation, post-summer irrigation, pre-autumn irrigation and
机译：满足水环境评价与管理决策的需要 大型灌区节水改造工程的规划设计， 本文基于人工神经网络理论的BP模型和RBF模型， 人工智能技术的重要分支，建立区域预测模型 土壤-水（盐）环境。人工神经网络技术被系统地应用到预测中 两种不同尺度的区域土壤水（盐）环境评价与评价 河套实验区（解放闸灌区，沙好渠实验区） 内蒙古灌区。根据所选的BP模型和相关水量 记录表水深等信息，年平均月水深 在节水的前提下，对未来计划年度（2005年，2010年）的趋势进行了预测 在较大的实验区（解放闸）完成了重建项目 河套灌区。然后，将BP的结果与RBF模型的结果进行比较， 区域水平衡法。根据几种方法的预测结果，我们得出 已知：与2010年相比，2010年的年平均水位将下降约0.6m。 目前的平均水平。下降幅度将相对较大。基于五年 区域的水盐监测数据以及相关的水文和气象信息 小规模试验区（Shahaoqu），区域水土（盐）环境体系 使用BP和RBF模型进行模拟，测试和预测。我们得出的结论是 从预测结果得出：当总水量减少时，占目前的30％ 如果以2010年的平均水平计算，地下水的总盐度将显着增加。 不同时期（夏季灌溉，夏季灌溉，秋季灌溉和

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