灌区土壤盐渍化程度云理论改进多级模糊评价模型
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摘要
土壤盐渍化的产生过程是一个多指标参与、多层次驱动的复杂系统,针对盐渍化程度的评价的不确定性和模糊性问题,将不确定性云理论引入到土壤盐渍化程度的多级模糊评价中,通过构建土壤盐渍化程度多级模糊评价指标体系,构建了盐渍化程度的评语集云模型、诱发因子的隶属度云模型及权重云模型,进而提出了基于云理论改进的土壤盐渍化程度的多级模糊评价模型。同时,选定景泰川电力提灌灌区为研究区,对该灌区的土壤盐渍化程度进行了评价,并将评价结果和评语集云模型结合用Matlab仿真显示。研究表明:该灌区土壤的盐渍化处于轻度盐化土和中度盐化土之间,0~100 cm土壤的含盐量为0.224 2%的可能性最大;利用云理论改进多级模糊评价模型对土壤的盐渍化程度开展研究,用不确定性云参数代替精确数值,更具普遍性。相关研究可为开展盐渍化程度评估和预测的研究提供有益参考。
The producing process of soil salinization is a complex fuzzy system with participation of multi indices and multi-level driving,which involves field water transformation,field heat exchange and soil salt-water transport.Therefore,in view of this fuzzy process,the comprehensive evaluation model of salinization based on multi-level fuzzy theory can be used to reveal it quantitatively.However,this model can take good account of the fuzziness of the complex system in the comprehensive evaluation of soil salinization degree,the randomness and discreteness of the system are neglected yet,and the fuzzy nature of the randomness and volatility of fuzzy systems are not well represented.The cloud theory can describe the randomness and uncertainty of fuzzy systems well,in which qualitative concepts and quantitative values can be freely transformed,and the subjective and individual empirical effects of experts in describing the status of inducing factors of soil salinization can be well avoided.In view of this,in this paper,the uncertainty cloud theory was introduced into the multi-level fuzzy evaluation model,and the driving process of soil salinization was divided into 4 layers: The evaluation layer,driving process layer,inducing factor layer and element status layer.Regional soil salinization degree was described,the multi-level fuzzy evaluation index system of soil salinization degree was constructed by the basic principles of analytic hierarchy process and multilevel fuzzy theory,and a set of cloud model of salinity evaluation was constructed by using normal cloud generator.Meanwhile,the scale criterion of inducing factor of soil salinization based on cloud scale was constructed by improving the traditional Satty scaling principle,and a weight cloud model of induced factors was constructed.In addition,the membership cloud model of the induced factor was constructed by using the backward cloud generator.Finally,the weight cloud model and membership cloud model were weighted to determine the evaluation model of soil salinization degree,and then a multi-level fuzzy evaluation model of soil salinization based on cloud theory was proposed.Moreover,the model was used to evaluate the degree of soil salinization of Jingtaichuan electric pumping irrigation area,Gansu Province.And then the evaluation results and comments collection cloud model were combined,which was for emulation display by MATLAB software.The result shows that: 1) The salinization degree of soil in irrigated area is between slight and moderate.The expected value of soil salinity is 0.224 2%,that is to say,the likelihood of 0.224 2% soil salinity in 0-100 cm is maximum.In addition,the entropy and hyper entropy of the cloud model are 0.029 5 and 0.021 2,respectively,and the value is smaller,that is,the uncertainty of the evaluation results is small,and the evaluation results fluctuate in a small range.The evaluation results basically conform to the actual situation of irrigation area,and the evaluation results are good,which verify the feasibility of the model.2) The multi-level fuzzy evaluation model is improved by using cloud theory,the stability and reliability of the results are also given besides the expected values,and the fuzziness,randomness and discreteness are organically combined by 3 numerical characteristics of the cloud model i.e.expectation value,entropy and hyper entropy.Compared with the multi-level fuzzy evaluation model,the results are more in line with human language habits,and the information is more abundant,which provides a new method for the evaluation of soil salinization degree in irrigation area.
The producing process of soil salinization is a complex fuzzy system with participation of multi indices and multi-level driving,which involves field water transformation,field heat exchange and soil salt-water transport.Therefore,in view of this fuzzy process,the comprehensive evaluation model of salinization based on multi-level fuzzy theory can be used to reveal it quantitatively.However,this model can take good account of the fuzziness of the complex system in the comprehensive evaluation of soil salinization degree,the randomness and discreteness of the system are neglected yet,and the fuzzy nature of the randomness and volatility of fuzzy systems are not well represented.The cloud theory can describe the randomness and uncertainty of fuzzy systems well,in which qualitative concepts and quantitative values can be freely transformed,and the subjective and individual empirical effects of experts in describing the status of inducing factors of soil salinization can be well avoided.In view of this,in this paper,the uncertainty cloud theory was introduced into the multi-level fuzzy evaluation model,and the driving process of soil salinization was divided into 4 layers: The evaluation layer,driving process layer,inducing factor layer and element status layer.Regional soil salinization degree was described,the multi-level fuzzy evaluation index system of soil salinization degree was constructed by the basic principles of analytic hierarchy process and multilevel fuzzy theory,and a set of cloud model of salinity evaluation was constructed by using normal cloud generator.Meanwhile,the scale criterion of inducing factor of soil salinization based on cloud scale was constructed by improving the traditional Satty scaling principle,and a weight cloud model of induced factors was constructed.In addition,the membership cloud model of the induced factor was constructed by using the backward cloud generator.Finally,the weight cloud model and membership cloud model were weighted to determine the evaluation model of soil salinization degree,and then a multi-level fuzzy evaluation model of soil salinization based on cloud theory was proposed.Moreover,the model was used to evaluate the degree of soil salinization of Jingtaichuan electric pumping irrigation area,Gansu Province.And then the evaluation results and comments collection cloud model were combined,which was for emulation display by MATLAB software.The result shows that: 1) The salinization degree of soil in irrigated area is between slight and moderate.The expected value of soil salinity is 0.224 2%,that is to say,the likelihood of 0.224 2% soil salinity in 0-100 cm is maximum.In addition,the entropy and hyper entropy of the cloud model are 0.029 5 and 0.021 2,respectively,and the value is smaller,that is,the uncertainty of the evaluation results is small,and the evaluation results fluctuate in a small range.The evaluation results basically conform to the actual situation of irrigation area,and the evaluation results are good,which verify the feasibility of the model.2) The multi-level fuzzy evaluation model is improved by using cloud theory,the stability and reliability of the results are also given besides the expected values,and the fuzziness,randomness and discreteness are organically combined by 3 numerical characteristics of the cloud model i.e.expectation value,entropy and hyper entropy.Compared with the multi-level fuzzy evaluation model,the results are more in line with human language habits,and the information is more abundant,which provides a new method for the evaluation of soil salinization degree in irrigation area.
引文
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[4]Alexakis D,Gotsis D,Giakoumakis S.Evaluation of soil salinization in a Mediterranean site(Agoulinitsa district:West Greece)[J].Arabian Journal of Geosciences,2015,8(3):1373-1383.
[5]管孝艳,王少丽,高占义,等.盐渍化灌区土壤盐分的时空变异特征及其与地下水埋深的关系[J].生态学报,2012,32(4):1202-1210.Guan Xiaoyan,Wang Shaoli,Gao Zhanyi,et al.Spatio-temporal variability of soil salinity and its relationship with the depth to groundwater in salinization irrigation district[J].Acta Ecologica Sinica,2012,32(4):1202-1210.(in Chinese with English abstract)
[6]徐存东,连海东,聂俊坤,等.基于改进层次分析法的干旱扬水灌区水土环境变迁响应评估[J].干旱区地理,2015,38(5):880-886.Xu Cundong,Lian Haidong,Nie Junkun,et al.Response evaluation for changes of water and soil environment in arid pumping-irrigation areas based on improved analytic hierarchy process[J].Arid Land Geography,2015,38(5):880-886.(in Chinese with English abstract)
[7]Azizzadeh L,Zadeh L,Zahed L,et al.Fuzzy Sets,Information and Control[J].Information&Control,1965,8(3):338-353.
[8]汪培庄.模糊集合论及其应用[M].上海:科学技术出版社,1983.
[9]肖龙,戴宗坤.信息系统风险的多级模糊综合评判模型[J].四川大学学报:工程科学版,2004,36(5):98-102.Xiao Long,Dai Zongkun.Model of multilevel fuzzy comprehensive risk evaluation of information system[J].Journal of Sichuan University:Engineering Science Edition,2004,36(5):98-102.(in Chinese with English abstract)
[10]刘沐宇,林驰,高宏伟.桥梁生命周期环境影响的多级模糊综合评价[J].土木工程学报,2009(1):55-59.Liu Muyu,Lin Chi,Gao Hongwei.Comprehensive fuzzy assessment on the life-cycle environment impact of bridges[J].China Civil Engineering Journal,2009(1):55-59.(in Chinese with English abstract)
[11]刘旭娜,肖先勇,刘阳,等.工业过程电压暂降风险等级层次化多级模糊综合评估[J].电网技术,2014,38(7):1984-1988.Liu Xuna,Xiao Xianyong,Liu Yang,et al.Hierarchical multi-level fuzzy comprehensive evaluation on risk level of voltage sag during industrial process[J].Power System Technology,2014,38(7):1984-1988.(in Chinese with English abstract)
[12]沈进昌,杜树新,罗祎,等.基于云模型的模糊综合评价方法及应用[J].模糊系统与数学,2012,26(6):115-123.Hen Jinchang,Du Shuxin,Luo Yi,et al.Method and application research on fuzzy comprehensive evaluation based on cloud model[J].Fuzzy Systems and Mathematics,2012,26(6):115-123.(in Chinese with English abstract)
[13]李德毅,刘常昱,杜鹢,等.不确定性人工智能[J].软件学报,2004,15(11):1583-1594.Li Deyi,Liu Changyu,Du Ye,et al.Artificial intelligence with uncertainty[J].Journal of Software,2004,15(11):1583-1594.(in Chinese with English abstract)
[14]周启刚,张晓媛,王兆林.基于正态云模型的三峡库区土地利用生态风险评价[J].农业工程学报,2014,30(23):289-297.Zhou Qigang,Zhang Xiaoyuan,Wang Zhaolin.Land use ecological risk evaluation in Three Gorges Reservoir area based on normal cloud model[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2014,30(23):289-297.(in Chinese with English abstract)
[15]王遵亲,祝寿泉,俞人培,等.中国盐渍土[M].北京:科学出版社,1993.
[16]汪培庄,李洪兴.模糊系统理论与模糊计算机[M].北京:科学出版社,1996.
[17]何金平,高全,施玉群.基于云模型的大坝安全多层次综合评价方法[J].系统工程理论与实践,2016,36(11):2977-2983.He Jinping,Gao Quan,Shi Yuqun.A multi-hierarchical comprehensive evaluation method of dam safety based on cloud model[J].Systems Engineering-Theory&Practice,2016,36(11):2977-2983.(in Chinese with English abstract)
[18]关山,康振兴,彭昶.车削刀具磨损声发射信号的云特征分析[J].农业工程学报,2016,32(20):63-69.Guan Shan,Kang Zhenxing,Peng Chang.Analysis on cloud characteristics of wear acoustic emission signal for vehicle cutting tool[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2016,32(20):63-69.(in Chinese with English abstract)
[19]张光卫,何锐,刘禹,等.基于云模型的进化算法[J].计算机学报,2008,31(7):1082-1091.Zhang Guangwei,He Rui,Liu Yu,et al.An evolutionary algorithm based on cloud model[J].Chinese Journal of Computers,2008,31(7):1082-1091.(in Chinese with English abstract)
[20]张杨,严金明,江平,等.基于正态云模型的湖北省土地资源生态安全评价[J].农业工程学报,2013,29(22):252-258.Zhang Yang,Yan Jinming,Jiang Ping,et al.Normal cloud model based evaluation of land resources ecological security in Hubei province[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2013,29(22):252-258.(in Chinese with English abstract)
[21]Nguyen T,Nahavandi S.Modified AHP for gene selection and cancer classification using type-2 fuzzy logic[J].IEEE Transactions on Fuzzy Systems,2016,24(2):273-287.
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