基于计算智能和GIS的暴雨型泥石流分析预测研究
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摘要
泥石流是山区常见的自然灾害，具有很强的破坏性，直接威胁人民生命和财产安全，严重影响经济的可持续发展。本文基于GIS空间分析技术和计算智能理论建立模型，分析评价泥石流灾害风险，为泥石流预测预报提供科学依据和技术支持。
泥石流灾害系统属于复杂非线性系统，存在模糊性和不确定性。本文依据泥石流孕育、发展过程不同阶段的特点，以计算智能理论和GIS技术集成泥石流灾害的多元影响因子，建立泥石流灾害的风险评价预测模型。运用研究区观测数据进行仿真，验证了模型的有效性。本文的研究工作与主要结论如下：
(1)根据泥石流发展过程中的一系列动态过程事件，以事件树理论为基础构建了泥石流灾害发展过程的事件树模型，用模糊语言表达事件树节点事件的发生概率，并对泥石流灾害的发生概率进行模糊评价，最后通过解模糊方法得到泥石流灾害风险概率。事件树模型体现了泥石流孕灾过程的阶段性，计算出的泥石流灾害事件的发生概率与实际吻合。
(2)将坡度、相对高差、植被覆盖度、沿沟松散物储量、前5天累计降雨量、最大小时雨强和当日雨量作为泥石流灾害预警模型的评价指标，根据评价指标特点制定相应的关联函数来计算关联度，运用可拓学理论建立泥石流预警模型，为泥石流灾害评价提出了形式化的理论方法。
(3)利用模拟退火遗传算法改进GMDH网络模型，并用改进的GMDH模型预测泥石流灾害。用KLDA判别分析方法选取关联度大的致灾因子，将其作为输入参数，泥石流一次最大冲出量作为输出参数，运用改进的GMDH网络模型进行泥石流灾害预测，模型精度比其他模型（如BP和ANFIS）高。
(4)提出了基于本地搜索策略的混合蚁群优化方法来优化贝叶斯网络结构学习，以改进贝叶斯网络模型，并将改进的模型用于泥石流灾害风险评估，计算出的泥石流灾害危险度与实际情况吻合。该方法为地学分析中不确定性问题、数据不完整问题的研究提供了一种新技术。
Debris flow is a common natural disaster in mountain area, and it has a strongdestructive, direct threat to people's life and property security, serious impact on thesustainable development of economy. In this paper, based on GIS spatial analysistechnology and computing intelligence theory, models were constructed to analyzedebris flow disaster risk evaluation and provide technical support for debris flowforecasting.It has important theoretical significance and practical application value.
Debris flow disaster system belongs to complex nonlinear system.And there existfuzziness and uncertainty in the system. According to the characteristics of differentstages in landslide inoculation and development process, multivariate factors wereintegrated to establish debris flow disasters risk assessing and predicting model, basedon computing intelligent theory and GIS technology. Observation data in the studyarea was simulated to verify the validity of the model. In this paper, the research workand main conclusions are as follows:
(1) Based on event tree theory to build the development process of debris flowdisaster event tree model, using fuzzy language to express the probability of event treenode, and evaluate the fuzzy probability of occurrence of debris flow disasters, thedebris flow disaster risk probability is obtained by defuzzy method. Event tree modelembodies the mudslide subsquently phases of the process, and the calculatedprobability of debris flow disasters was in accordance with the actual.
(2) the slope, relative elevation difference, the vegetation coverage, loosematerial reserves along the ditch,5d-accumulated rainfall, the maximum hours ofrainfall intensity and the day rain as the assessment indexes of debris flow disasterwarning model, by using the theory of extenics to establish debris flow warningmodel, advanced the theory of formal method for debris flow disaster evaluation.According to the characteristics of the factor set corresponding correlation functionsto calculate the correlation degree, the evaluation process more scientific andreasonable.
(3) Using the simulated annealing genetic algorithm to improve GMDH（GroupMethod of Data Handling） network model, and use the improved GMDH model toforecast debris flow disasters. With KLDA discriminant analysis method to selectlager factor correlation and it as an input parameter, the biggest quantity debris flowrushing out once a time as the output parameter, using the improved GMDH networkmodel for debris flow disaster prediction, the model precision is higher than othermodels, such as BP and ANFIS).
(4)A method is proposed based on local search strategy of hybrid ant colonyoptimization algorithm to optimize the bayesian network structure learning, and toimprove the bayesian network model, and the improved model was used for debrisflow disaster risk assessment, and the calculated risk of debris flow hazards was inaccordance with the actual situation. The method provided a new technology foranalyzing the uncertainty problem and the incomplete data problem.
泥石流灾害系统属于复杂非线性系统，存在模糊性和不确定性。本文依据泥石流孕育、发展过程不同阶段的特点，以计算智能理论和GIS技术集成泥石流灾害的多元影响因子，建立泥石流灾害的风险评价预测模型。运用研究区观测数据进行仿真，验证了模型的有效性。本文的研究工作与主要结论如下：
(1)根据泥石流发展过程中的一系列动态过程事件，以事件树理论为基础构建了泥石流灾害发展过程的事件树模型，用模糊语言表达事件树节点事件的发生概率，并对泥石流灾害的发生概率进行模糊评价，最后通过解模糊方法得到泥石流灾害风险概率。事件树模型体现了泥石流孕灾过程的阶段性，计算出的泥石流灾害事件的发生概率与实际吻合。
(2)将坡度、相对高差、植被覆盖度、沿沟松散物储量、前5天累计降雨量、最大小时雨强和当日雨量作为泥石流灾害预警模型的评价指标，根据评价指标特点制定相应的关联函数来计算关联度，运用可拓学理论建立泥石流预警模型，为泥石流灾害评价提出了形式化的理论方法。
(3)利用模拟退火遗传算法改进GMDH网络模型，并用改进的GMDH模型预测泥石流灾害。用KLDA判别分析方法选取关联度大的致灾因子，将其作为输入参数，泥石流一次最大冲出量作为输出参数，运用改进的GMDH网络模型进行泥石流灾害预测，模型精度比其他模型（如BP和ANFIS）高。
(4)提出了基于本地搜索策略的混合蚁群优化方法来优化贝叶斯网络结构学习，以改进贝叶斯网络模型，并将改进的模型用于泥石流灾害风险评估，计算出的泥石流灾害危险度与实际情况吻合。该方法为地学分析中不确定性问题、数据不完整问题的研究提供了一种新技术。
Debris flow is a common natural disaster in mountain area, and it has a strongdestructive, direct threat to people's life and property security, serious impact on thesustainable development of economy. In this paper, based on GIS spatial analysistechnology and computing intelligence theory, models were constructed to analyzedebris flow disaster risk evaluation and provide technical support for debris flowforecasting.It has important theoretical significance and practical application value.
Debris flow disaster system belongs to complex nonlinear system.And there existfuzziness and uncertainty in the system. According to the characteristics of differentstages in landslide inoculation and development process, multivariate factors wereintegrated to establish debris flow disasters risk assessing and predicting model, basedon computing intelligent theory and GIS technology. Observation data in the studyarea was simulated to verify the validity of the model. In this paper, the research workand main conclusions are as follows:
(1) Based on event tree theory to build the development process of debris flowdisaster event tree model, using fuzzy language to express the probability of event treenode, and evaluate the fuzzy probability of occurrence of debris flow disasters, thedebris flow disaster risk probability is obtained by defuzzy method. Event tree modelembodies the mudslide subsquently phases of the process, and the calculatedprobability of debris flow disasters was in accordance with the actual.
(2) the slope, relative elevation difference, the vegetation coverage, loosematerial reserves along the ditch,5d-accumulated rainfall, the maximum hours ofrainfall intensity and the day rain as the assessment indexes of debris flow disasterwarning model, by using the theory of extenics to establish debris flow warningmodel, advanced the theory of formal method for debris flow disaster evaluation.According to the characteristics of the factor set corresponding correlation functionsto calculate the correlation degree, the evaluation process more scientific andreasonable.
(3) Using the simulated annealing genetic algorithm to improve GMDH（GroupMethod of Data Handling） network model, and use the improved GMDH model toforecast debris flow disasters. With KLDA discriminant analysis method to selectlager factor correlation and it as an input parameter, the biggest quantity debris flowrushing out once a time as the output parameter, using the improved GMDH networkmodel for debris flow disaster prediction, the model precision is higher than othermodels, such as BP and ANFIS).
(4)A method is proposed based on local search strategy of hybrid ant colonyoptimization algorithm to optimize the bayesian network structure learning, and toimprove the bayesian network model, and the improved model was used for debrisflow disaster risk assessment, and the calculated risk of debris flow hazards was inaccordance with the actual situation. The method provided a new technology foranalyzing the uncertainty problem and the incomplete data problem.
引文
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