基于人工神经网络的喀斯特地区水资源安全评价

刘丽颖; 官冬杰; 杨清伟; 苏维词

doi:10.13961/j.cnki.stbctb.2017.02.032

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基于人工神经网络的喀斯特地区水资源安全评价

试验研究

- 基于人工神经网络的喀斯特地区水资源安全评价
- Assessment of Water Resources Security in Karst Area Based on Artificial Neural Network
- 水土保持通报 2017年37卷第2期页码：207-214
- 作者机构：
  
  1. 重庆交通大学河海学院,重庆,400074
  2. 重庆工商大学数学与统计学院,重庆,400067
  3. 重庆交通大学建筑与城市规划学院,重庆,400074
  4. 重庆师范大学地理与旅游学院,重庆,400047
  5. 贵州科学院山地资源研究所,贵州,贵阳,550001
- 作者简介：
- 基金信息：
  
  国家自然科学基金项目“喀斯特城市边缘带土地利用/覆盖变化及其环境效应”（41261038），“三峡库区生态安全后续发展动态模拟及其可视化预警评价”（41201546）;国家科技支撑计划项目（2014BAB03B01）;重庆市自然科学基金项目（cstc2012jjA20010）;重庆工商大学2015年校级科研项目（670101577）
- DOI：10.13961/j.cnki.stbctb.2017.02.032
  中图分类号：
- 纸质出版：2017
- 稿件说明：
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刘丽颖, 官冬杰, 杨清伟, 等. 基于人工神经网络的喀斯特地区水资源安全评价[J]. 水土保持通报, 2017,37(2):207-214.

LIU Liying, GUAN Dongjie, YANG Qingwei, et al. Assessment of Water Resources Security in Karst Area Based on Artificial Neural Network[J]. Bulletin of Soiland Water Conservation, 2017, 37(2): 207-214.
刘丽颖, 官冬杰, 杨清伟, 等. 基于人工神经网络的喀斯特地区水资源安全评价[J]. 水土保持通报, 2017,37(2):207-214. DOI： 10.13961/j.cnki.stbctb.2017.02.032.

LIU Liying, GUAN Dongjie, YANG Qingwei, et al. Assessment of Water Resources Security in Karst Area Based on Artificial Neural Network[J]. Bulletin of Soiland Water Conservation, 2017, 37(2): 207-214. DOI： 10.13961/j.cnki.stbctb.2017.02.032.

摘要

[目的] 对喀斯特地区水资源安全状况进行评价，为喀斯特地区水环境管理与可持续发展提供科学依据。[方法] 以喀斯特典型分布区贵州省为例，建立喀斯特地区水资源安全评价指标体系，构建BP人工神经网络模型，对贵州省9个州市水资源安全进行评价，并与熵权物元模型相比较。[结果] 贵州省9个州市的水资源安全，有2个处于比较安全状态，6个处于一般状态，1个处于比较不安全状态。评价结果与熵权物元模型评价结果基本一致。[结论] 该体系评价结果合理，评价方法简便直观，该模型对类似地区水资源安全评价有一定的参考价值。

Abstract

[Objective] Water resource security was evaluated to provide scientific basis for water environment management and sustainable development in Karst area.[Methods] A case study of Guizhou Province in karst was carried out and an evaluation index system of water resources security was established. The BP artificial neural network model was constructed to evaluate the water resources securities of 9 cities in Guizhou Province

and the results were compared with entropy weight matter element model.[Results] Water resource securities of 9 cities were evaluated in Guizhou Province

among which two cities were assessed at relatively safe level

six cities at general level

and one city at relatively unsafe level. The evaluation results were basically the same to the results of the entropy weighted matter element model. [Conclusion] The results are reasonable and the method is simple and intuitive. The model has certain reference value for similar areas of water resources security evaluation.

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Keywords

references

张翔,夏军,贾绍凤.水安全定义及其评价指数的应用[J].资源科学,2005,27(3):145-149.

贾绍凤,张军岩,张士锋.区域水资源压力指数与水资源安全评价指标体系[J].地理科学进展,2002,21(6):538-545.

Waughrayd. Water Security:The Water-Food-Energy-Climate Nexus[M]. Washington:Island Press, 2011.

Lindhe A, Rosén L, Norberg T, et al. Uncertainty modelling in multi-criteria analysis of water safety measures[J]. Environment Systems and Decisions, 2013,33(2):195-208.

Yong Jiang. China's water security:Current status, emerging challenges and future prospects[J]. Environmental Science & Policy, 2015,54:106-125.

Yang Xiaohua, Sun Boyang, Zhang Jian, et al. Hierarchy evaluation of water resources vulnerability under climate change in Beijing, China[J]. Natural Hazards, 2015,75(5):1-14.

Dai Jing, Qi Jing, Chi Jingjing, et al. Integrated water resource security evaluation of Beijing based on GRA and topsis[J].Frontiers of Earth Science in China, 2010,4(3):357-362.

Zhang Junyi, Wang Lachun. Assessment of water resource security in Chongqing City of China:What has been done and what remains to be done?[J]. Natural Hazards, 2015,75(3):2751-2772.

张凤太,王腊春,苏维词,等.基于熵权集对耦合模型的表层岩溶带"二元"水资源安全评价[J].水力发电学报,2012,31(6):70-76.

杜向润,冯民权,张建龙.基于改进模糊物元模型的水安全评价研究[J].西北农林科技大学学报:自然科学版,2015,43(8):222-228.

Chen Zhihe, Wei Shuai. Application of system dynamics to water security research[J]. Water Resources Management, 2014,28(2):287-300.

来海亮,汪党献,吴涤非.水资源及其开发利用综合评价指标体系[J].水科学进展,2006,17(1):95-101.

袁曾任.人工神经元网络及其应用[M].北京:清华大学出版社,1999:1-12.

Liu Zelin, Peng Changhui, Xiang Wenhua, et al. Application of artificial neural networks in global climate change and ecological research:An overview[J].Chinese Science Bulletin, 2010,55(34):3853-3863.

Yirgalem C, Ghinwa M N. Water quality monitoring using remote sensing and an artificial neural Network[J]. Water, Air,& Soil Pollution, 2012,223(8):4875-4887.

Yang Fang, Wang Meng, Ye Min. Study on the Evaluation Model of Water Environment Quality based on Artificial Neural Network[C]//Arabia, United Arab Emirates, Dubai:2012 International Conference on Electrical and Computer Engineering(ICECE2012), 2012.

Najah A, El-Shafie A, Karim O A, et al. Application of artificial neural networks for water quality prediction[J]. Neural Computing and Applications, 2013, 22(1):187-201.

Singh K P, Basant A, Malik A, et al. Artificial neural network modeling of the river water quality:A case study[J]. Ecological Modelling, 2009,220(6):888-895.

Mazzocchi Chiara, Corsi Stefano, Sali Guido. Agricultural land consumption in Periurban areas:A methodological approach for risk assessment using artificial neural networks and spatial correlation in Northern Italy[J]. Applied Spatial Analysis and Policy, 2017,10(1):3-20.

Peng Yu. Study on the assessment of water resources carrying capacity in strategic environmental assessment[J].Computer Science for Environmental Engineering and EcoInformatics, 2011,158(1):269-274.

余健,房莉,仓定帮,等.熵权模糊物元模型在土地生态安全评价中的应用[J].农业工程学报,2012,28(5):260-266.

施开放,刁承泰,左太安,等.基于熵权物元模型的耕地占补平衡生态安全评价[J].中国生态农业学报,2013,21(2):243-250.

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