| 基于SOFM网络的山东省水资源承载力评价 |
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| 引用本文： | 王艳,曹俊茹,吴佩林.基于SOFM网络的山东省水资源承载力评价[J].安徽农业科学,2009,37(33):16494-16495. |
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| 作者姓名： | 王艳 曹俊茹 吴佩林 |
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| 作者单位： | 王艳,曹俊茹(山东理工大学建筑工程学院,山东淄博,255049);吴佩林(山东大学威海分校商学院,山东威海,264209) |
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| 基金项目： | 国家社科基金项目，山东省自然科学基金项目，山东理工大学科技基金项目 |
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| 摘 要： | 水资源承载能力与评价指标组成了一个复杂的非线性系统，评价的难点在于确定各评价指标的权值。结合地理信息系统（GIS）和人工神经网络（ANN）技术，提出对水资源承载力进行评价的一种新方法；根据构建的水资源承载力评价指标体系，以山东省为例，利用自组织神经网络模型（SOFM）对水资源承载力进行了评价。结果表明，山东省17地市水资源承载力可划分为5类，模拟结果比较理想。
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| 关 键 词： | 人工神经网络(ANN) 自组织神经网络(SOFM) 水资源承载力 评价 山东省 |
Evaluation on Water Resources Carrying Capacity in Shandong Province Based on SOFM Network |
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| WANG Yan et al.Evaluation on Water Resources Carrying Capacity in Shandong Province Based on SOFM Network[J].Journal of Anhui Agricultural Sciences,2009,37(33):16494-16495. |
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| Authors: | WANG Yan |
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| Institution: | WANG Yan et al(School of Architectural Engineering,Sh,ong University of Technology,Zibo,Sh,ong 255049) |
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| Abstract: | The water resources carrying capacity(WRCC)and its evaluating indicators consisted of a non-linear system.The major difficulties for WRCC assessment were to determine the weight of each indicator.A new assessment method for WRCC evaluation based on geographic information system(GIS)and artificial neural network(ANN)was proposed.In order to properly evaluate WRCC,a new analysis index system was put forward.Finally,the method was used to evaluate WRCC of Shandong by using SOFM,and the experiment results showe... |
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| Keywords: | Artificial neural network (ANN) Self-organizing feature maps (SOFM) Water resources carrying capacity Evaluation Shandong Province |
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