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991.
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
992.
山东地磁台网受宁东高压直流输电干扰预处理质量分析 总被引:2,自引:0,他引:2
为进一步提高山东地磁台网受高压直流输电干扰的预处理质量,通过中国地震前兆数据处理系统中相对差值检测和预处理检测功能,对山东地磁台网多年来受宁东高压直流输电干扰预处理情况进行了总结分析。结果表明,经过数据预处理,宁东高压直流输电干扰基本被去除,但仍然存在高压直流输电干扰误判、起止时间错判、干扰幅度计量不精确等问题。针对上述问题,对数据进行复核和校对,应用"缓变台阶"功能重新处理,并加强对H、D分量干扰特征的研究。 相似文献
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镇江地震台网理论监测能力评估 总被引:1,自引:1,他引:0
基于近震震级公式,对镇江地震台网理论监测能力进行评估,绘制地震监测能力图,以确定监测区不同地点发生地震时能被台网有效分析和定位的地震最小震级。镇江市句容县在该台网理论监测震级最小,为ML1.2—1.3;在镇江及相邻城市,发生ML 1.8分析认为,以上地震均能被有效监测。 相似文献
994.
针对桥梁的非线性下沉问题,引用了混沌理论,首先求取时间序列的两重构参数时间延迟τ和嵌入维数m进行相空间重构;随后进行混沌特性判别,确定该时间序列存在混沌迹象;最后根据所求参数建立加权零阶局域预计模型和RBF神经网络混沌预计模型对观测数据进行预计分析,并与系数为0.9的指数平滑预测模型进行比较,结果显示混沌预计模型值更接... 相似文献
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Approaches for delineating landslide hazard areas using different training sites in an advanced artificial neural network model 总被引:10,自引:0,他引:10
The current paper presents landslide hazard analysis around the Cameron area, Malaysia, using advanced artificial neural networks
with the help of Geographic Information System (GIS) and remote sensing techniques. Landslide locations were determined in
the study area by interpretation of aerial photographs and from field investigations. Topographical and geological data as
well as satellite images were collected, processed, and constructed into a spatial database using GIS and image processing.
Ten factors were selected for landslide hazard including: 1) factors related to topography as slope, aspect, and curvature;
2) factors related to geology as lithology and distance from lineament; 3) factors related to drainage as distance from drainage;
and 4) factors extracted from TM satellite images as land cover and the vegetation index value. An advanced artificial neural
network model has been used to analyze these factors in order to establish the landslide hazard map. The back-propagation
training method has been used for the selection of the five different random training sites in order to calculate the factor’s
weight and then the landslide hazard indices were computed for each of the five hazard maps. Finally, the landslide hazard
maps (five cases) were prepared using GIS tools. Results of the landslides hazard maps have been verified using landslide
test locations that were not used during the training phase of the neural network. Our findings of verification results show
an accuracy of 69%, 75%, 70%, 83% and 86% for training sites 1, 2, 3, 4 and 5 respectively. GIS data was used to efficiently
analyze the large volume of data, and the artificial neural network proved to be an effective tool for landslide hazard analysis.
The verification results showed sufficient agreement between the presumptive hazard map and the existing data on landslide
areas. 相似文献
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