Modelling of a medium-term dynamics in a shallow tidal sea, based on combined physical and neural network methods     

Agnieszka Herman  Ralf Kaiser  Hanz D. Niemeyer 《Ocean Modelling》2007,17(4):277-299

The paper presents an approach towards a medium-term (decades) modelling of water levels and currents in a shallow tidal sea by means of combined hydrodynamic and neural network models. The two-dimensional version of the hydrodynamic model Delft3D, forced with realistic water level and wind fields, is used to produce a two-year-database of water levels and currents in the study area. The linear principal component analysis (PCA) of the results is performed to reveal dominating spatial patterns in the analyzed dataset and to significantly reduce the dimensionality of the data. It is shown that only a few principal components (PCs) are necessary to reconstruct the data with high accuracy (over 95% of the original variance). Feed-forward neural networks are set up and trained to effectively simulate the leading PCs based on water level and wind speed and direction time series in a single, arbitrarily chosen point in the study area. Assuming that the spatial modes resulting from the PCA are ‘universally’ applicable to the data from time periods not modelled with Delft3D, the trained neural networks can be used to very effectively and reliably simulate temporal and spatial variability of water levels and currents in the study area. The approach is shown to be able to accurately reproduce statistical distribution of water levels and currents in various locations inside the study area and thus can be viewed as a reliable complementary tool e.g., for computationally expensive hydrodynamic modelling. Finally, a detailed analysis of the leading PCs is performed to estimate the role of tidal forcing and wind (including its seasonal and annual variability) in shaping the water level and current climate in the study area.  相似文献   

遗传小波神经网络在变形预报中的应用     

邓勇  张冠宇  李宗春  杨振  薛志宏 《测绘科学》2012,37(5):183-186

目前常用的变形预报方法有BP人工神经网络和小波神经网络,但是都存在收敛速度慢且易受局部极值的影响。针对这两种算法的不足,本文利用遗传算法的全局寻优特性,将遗传算法与小波神经网络结合,形成遗传小波神经网,将其应用于变形预报,取得了良好的效果;并将算法的预报精度、稳定性、有效区间及运算时间作为评价算法优劣的4个标准,对BP神经网络、小波神经网络及遗传小波神经网络进行对比,结果表明遗传小波神经网络具有明显的优势。  相似文献   

遥感、GIS结合与区域天然滑坡调查   总被引：4，自引：0，他引：4       下载免费PDF全文

单新建  李焯芬等 《地质论评》2001,47(6):648-652

以香港大屿山岛中部为试验研究区，探讨了遥感与GIS结合在区域滑坡调查中应用的可能性。在详细分析滑坡与环境因子关系后，将岩土类型、地貌、侵蚀、坡度等环境因子引入到影像中，与影像组成多源复合图像，可使地物的空间属性信息得到补充。同时采用适合高维复合数据的BP神经元网络对研究区天然滑坡进行识别，结果表明，区域滑坡的识别能力有较大提高。该方法的应用，将为大区域滑坡调查与监测奠定基础。  相似文献   

第23太阳活动周的黑子数峰值特征预报     

田剑华  范权福 《地球物理学进展》1999,14(Z1)