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Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer 总被引：1，自引：0，他引：1
As threats of landslide hazards have become gradually more severe in recent decades,studies on landslide prevention and mitigation have attracted widespread attention in relevant domains.A hot research topic has been the ability to predict landslide susceptibility,which can be used to design schemes of land exploitation and urban development in mountainous areas.In this study,the teaching-learning-based optimization(TLBO)and satin bowerbird optimizer(SBO)algorithms were applied to optimize the adaptive neuro-fuzzy inference system(ANFIS)model for landslide susceptibility mapping.In the study area,152 landslides were identified and randomly divided into two groups as training(70%)and validation(30%)dataset.Additionally,a total of fifteen landslide influencing factors were selected.The relative importance and weights of various influencing factors were determined using the step-wise weight assessment ratio analysis(SWARA)method.Finally,the comprehensive performance of the two models was validated and compared using various indexes,such as the root mean square error(RMSE),processing time,convergence,and area under receiver operating characteristic curves(AUROC).The results demonstrated that the AUROC values of the ANFIS,ANFIS-TLBO and ANFIS-SBO models with the training data were 0.808,0.785 and 0.755,respectively.In terms of the validation dataset,the ANFISSBO model exhibited a higher AUROC value of 0.781,while the AUROC value of the ANFIS-TLBO and ANFIS models were 0.749 and 0.681,respectively.Moreover,the ANFIS-SBO model showed lower RMSE values for the validation dataset,indicating that the SBO algorithm had a better optimization capability.Meanwhile,the processing time and convergence of the ANFIS-SBO model were far superior to those of the ANFIS-TLBO model.Therefore,both the ensemble models proposed in this paper can generate adequate results,and the ANFIS-SBO model is recommended as the more suitable model for landslide susceptibility assessment in the study area considered due to its excellent accuracy and efficiency. 相似文献
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贴体网格在地质数值模拟中具有广阔的应用前景,为解决贴体网格生成时边界离散问题,提出了最大长度准则和最大面积准则,把曲线逼近和曲面网格优化问题转化为数学优化问题,为求解该问题,提出了改进的单粒子优化算法.试验表明,最大长度准则和最大面积准则的优化效果好于常规方法;以改进的单粒子优化算法求解该问题时,计算效率是智能单粒子优化算法的30倍左右（节点量为200）,从而实现最大长度准则和最大面积准则在贴体网格生成中的应用.针对最大面积准则优化曲面网格不能控制网格步长的情况,提出了限定步长的网格优化算法,使网格步长合理化,并通过实例验证了该算法的有效性.研究成果提供了生成贴体网格时边界优化准则和求解方法,对今后复杂边界的贴体网格生成具有重要意义. 相似文献
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传统的优化器缺乏空间数据分布统计信息和空间算子的支持,不能评估和生成有效的查询执行计划。对此,提出了包含空间执行算子的一体化评估框架,根据重要属性及元组流属性实现了执行计划自底向上的代价推演计算。通过服务器端编程实现了利用概率累计扩展空间直方图模型描述空间数据的统计信息,并通过优化器接口计算空间谓词选择率,为优化器提供更为准确的代价参数,改进了评估的准确度。实验结果表明,采用此方法进行涉及空间查询的计算,理论代价估计与实际执行代价具有相对一致性,在统计值完整的情况下能够准确地估算实际较优的执行计划。 相似文献
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Geochemical discrimination of tectonic settings of basalts has been an important research direction of geochemistry for decades. Olivine is one of the earliest crystallized minerals of basaltic magma, which records a lot of hidden information of the formation and evolution of the magma. Therefore, basic elements in olivine are used to discriminate three tectonic settings, including the mid-ocean ridge basalt (MORB), ocean island basalt (OIB) and island arc basalt (IAB). However, it is still difficult to accurately discriminate the tectonic settings by using these diagrams. The machine learning algorithm is introduced to solve the aforementioned problem. The classification performance of the machine learning discrimination method largely depends on the rationality of parameter determination. To this end, the paper proposes a coupling intelligent method for geochemical discrimination of tectonic settings using olivine composition of the basalts based on the grey wolf optimizer (GWO)-optimized support vector machine (SVM), or GWO-SVM for short. GWO is used to seek the optimal parameter combination of SVM to form the optimal mapping relationship between basic elements in olivine and basalt tectonic settings, so as to realize the accurate discrimination of MORB, OIB and IAB. In addition, according to the published geochemical data of basalt samples, the discrimination performance of GWO-SVM is evaluated by means of the simulation experiment, hold-out validation and k-fold cross-validation. The evaluation results are represented by the confusion matrix and its derived evaluation indicators. The results show that GWO-SVM can discriminate the tectonic settings of the basalts based on olivine compositions with overall classification accuracy of up to 85%. Thus, in comparison with the traditional discrimination diagram method, the machine learning discrimination method based on multi-algorithm fusion can significantly improve the discrimination accuracy of basalt tectonic settings. © 2020, Science Press. All right reserved. 相似文献
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It is well recognized that the time series of hydrologic variables, such as rainfall and streamflow are significantly influenced by various large‐scale atmospheric circulation patterns. The influence of El Niño‐southern oscillation (ENSO) on hydrologic variables, through hydroclimatic teleconnection, is recognized throughout the world. Indian summer monsoon rainfall (ISMR) has been proved to be significantly influenced by ENSO. Recently, it was established that the relationship between ISMR and ENSO is modulated by the influence of atmospheric circulation patterns over the Indian Ocean region. The influences of Indian Ocean dipole (IOD) mode and equatorial Indian Ocean oscillation (EQUINOO) on ISMR have been established in recent research. Thus, for the Indian subcontinent, hydrologic time series are significantly influenced by ENSO along with EQUINOO. Though the influence of these large‐scale atmospheric circulations on large‐scale rainfall patterns was investigated, their influence on basin‐scale stream‐flow is yet to be investigated. In this paper, information of ENSO from the tropical Pacific Ocean and EQUINOO from the tropical Indian Ocean is used in terms of their corresponding indices for stream‐flow forecasting of the Mahanadi River in the state of Orissa, India. To model the complex non‐linear relationship between basin‐scale stream‐flow and such large‐scale atmospheric circulation information, artificial neural network (ANN) methodology has been opted for the present study. Efficient optimization of ANN architecture is obtained by using an evolutionary optimizer based on a genetic algorithm. This study proves that use of such large‐scale atmospheric circulation information potentially improves the performance of monthly basin‐scale stream‐flow prediction which, in turn, helps in better management of water resources. Copyright © 2007 John Wiley & Sons, Ltd. 相似文献
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针对普通神经网络的梯度消失和易陷入局部极值的问题,提出一种基于多元宇宙优化算法(multi-verse optimizer, MVO)的BP神经网络优化方法(MVO-BP),利用MVO全局寻优的特性求取BP神经网络各层之间可靠的神经元阈值与连接权,从而使神经网络预测模型具备更高的预测精度。建立基于MVO-BP算法的GNSS高程异常拟合预测模型,并采用实际工程中少量高程异常数据进行算法可行性检验。结果表明,相较于常规的BP神经网络法及多面函数法,MVO-BP法精度更高、适用性更强,可为实际工程测量中正常高的求取提供参考。 相似文献
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Application of a parameter-shifted grey wolf optimizer for earthquake dynamic rupture inversion 
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下载免费PDF全文 Global optimization is an essential approach to any inversion problem. Recently, the grey wolf optimizer (GWO) has been proposed to optimize the global minimum, which has been quickly used in a variety of inversion problems. In this study, we proposed a parameter-shifted grey wolf optimizer (psGWO) based on the conventional GWO algorithm to obtain the global minimum. Compared with GWO, the novel psGWO can effectively search targets toward objects without being trapped within the local minimum of the zero value. We confirmed the effectiveness of the new method in searching for uniform and random objectives by using mathematical functions released by the Congress on Evolutionary Computation. The psGWO algorithm was validated using up to 10,000 parameters to demonstrate its robustness in a large-scale optimization problem. We successfully applied psGWO in two-dimensional (2D) synthetic earthquake dynamic rupture inversion to obtain the frictional coefficients of the fault and critical slip-weakening distance using a homogeneous model. Furthermore, this algorithm was applied in inversions with heterogeneous distributions of dynamic rupture parameters. This implementation can be efficiently applied in 3D cases and even in actual earthquake inversion and would deepen the understanding of the physics of natural earthquakes in the future. 相似文献
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