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排序方式: 共有1319条查询结果,搜索用时 734 毫秒
181.
为实现精准的旅游景区客流量的高时频预测,本研究构建了一套基于LBS和深度学习模型的预测方法。此方法可通过对LBS数据的转换实现预测的空间范围与时频控制,并通过方法的核心模型——基于双向循环神经网络和GRU算法构建的深度双向GRU(DBi-GRU)模型完成预测。为检验方法的有效性,研究以深圳大梅沙海滨公园为例对方法进行实验测试。实验使用拟合曲线、误差指标及DM检验3种方法评估DBi-GRU模型的预测效果。此外,实验还设置了其他五种深度学习模型作为DBi-GRU的对照模型,测试基于不同深度学习算法的模型之间的预测水平差异。实验结果表明:(1)本研究提出的DBi-GRU模型在景区客流量高时频预测中具有理想的预测效果,在高峰时段的客流量预测方面也具有较高准确性,预测效果明显优于其他深度学习模型;(2)基于双向循环网络的模型的效果普遍优于基于常规循环网络的模型。尤其是基于双向LSTM算法的模型,虽然预测的准确度略逊色于DBi-GRU模型,但在模型性能上与其的差异并不显著;(3)在相同网络参数下,GRU算法较前人采用的LSTM和RNN算法有着更高的预测准确性。本研究为客流量预测领域的研究提供了一种... 相似文献
182.
Data-based modelling approach for variable density flow and solute transport simulation in a coastal aquifer 总被引:1,自引:1,他引:0
Data-based models, namely artificial neural network (ANN), support vector machine (SVM), genetic programming (GP) and extreme learning machine (ELM), were developed to approximate three-dimensional, density-dependent flow and transport processes in a coastal aquifer. A simulation model, SEAWAT, was used to generate data required for the training and testing of the data-based models. Statistical analysis of the simulation results obtained by the four models show that the data-based models could simulate the complex salt water intrusion process successfully. The selected models were also compared based on their computational ability, and the results show that the ELM is the fastest technique, taking just 0.5 s to simulate the dataset; however, the SVM is the most accurate, with a Nash-Sutcliffe efficiency (NSE) ≥ 0.95 and correlation coefficient R ≥ 0.92 for all the wells. The root mean square error (RMSE) for the SVM is also significantly less, ranging from 12.28 to 77.61 mg/L. 相似文献
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软件模块故障倾向预测方法研究 总被引:2,自引:0,他引:2
研究了在区分故障严重程度下的软件模块故障倾向预测方法,将故障分为高严重程度和低严重程度两种类型,用统计分析和机器学习方法分析静态代码度量与故障倾向之间的关系。以公开和私有两种类型的失效数据集作为实验数据,分析发现,故障的严重程度影响预测性能,预测不同严重程度的故障需要选择不同的度量和分类模型,预测低严重程度故障的性能好于预测高严重程度故障的性能。 相似文献
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"干中学"与产业集群核心能力的形成 总被引:2,自引:0,他引:2
本文构建了产业集群核心能力的分析框架,从理论上说明学习效应是产业集群发展的内生动力,产业集群的学习是一个“干中学”的过程. 相似文献
189.
悬索桥隧道式锚碇的设计理念为锚碇夹持岩体协同承载,因而承载能力远超同体积的重力式锚碇。但因目前对围岩协同作用认识尚不充分,在当前隧道式锚碇设计中仍保守地忽略锚碇和岩体间的挤压效应。为弄清锚碇?岩体协同承载的机制,揭示隧道锚承载能力提高的本质,通过分析隧道式锚碇建设至成桥全过程受力,建立隧道锚的简化力学模型,并引用Mindlin应力解分析了荷载沿锚碇轴向的传递规律以及荷载产生的作用于锚碇?岩体间的挤压应力分布,最终给出了隧道式锚碇极限承载力的简化估算方法,并通过伍家岗大桥隧道锚工程实例分析了结果的合理性。所得结论主要有:锚碇?岩体界面力主要由锚碇自重和锚碇?岩体相互挤压产生;锚碇?岩体界面附加应力自后锚面向前锚面呈先增后减的变化趋势,在距后锚面约1/3L处达到应力峰值;以容许抗剪强度为破坏判据解得的伍家岗长江大桥隧道式锚碇的极限承载力为3 504 MN,约为16倍的设计荷载,与室内试验值基本吻合。 相似文献
190.
《地学前缘(英文版)》2020,11(6):2207-2219
This investigation assessed the efficacy of 10 widely used machine learning algorithms (MLA) comprising the least absolute shrinkage and selection operator (LASSO), generalized linear model (GLM), stepwise generalized linear model (SGLM), elastic net (ENET), partial least square (PLS), ridge regression, support vector machine (SVM), classification and regression trees (CART), bagged CART, and random forest (RF) for gully erosion susceptibility mapping (GESM) in Iran. The location of 462 previously existing gully erosion sites were mapped through widespread field investigations, of which 70% (323) and 30% (139) of observations were arbitrarily divided for algorithm calibration and validation. Twelve controlling factors for gully erosion, namely, soil texture, annual mean rainfall, digital elevation model (DEM), drainage density, slope, lithology, topographic wetness index (TWI), distance from rivers, aspect, distance from roads, plan curvature, and profile curvature were ranked in terms of their importance using each MLA. The MLA were compared using a training dataset for gully erosion and statistical measures such as RMSE (root mean square error), MAE (mean absolute error), and R-squared. Based on the comparisons among MLA, the RF algorithm exhibited the minimum RMSE and MAE and the maximum value of R-squared, and was therefore selected as the best model. The variable importance evaluation using the RF model revealed that distance from rivers had the highest significance in influencing the occurrence of gully erosion whereas plan curvature had the least importance. According to the GESM generated using RF, most of the study area is predicted to have a low (53.72%) or moderate (29.65%) susceptibility to gully erosion, whereas only a small area is identified to have a high (12.56%) or very high (4.07%) susceptibility. The outcome generated by RF model is validated using the ROC (Receiver Operating Characteristics) curve approach, which returned an area under the curve (AUC) of 0.985, proving the excellent forecasting ability of the model. The GESM prepared using the RF algorithm can aid decision-makers in targeting remedial actions for minimizing the damage caused by gully erosion. 相似文献