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381.
Accessible high-quality observation datasets and proper modeling process are critically required to accurately predict sea level rise in coastal areas. This study focuses on developing and validating a combined least squares-neural network approach applicable to the short-term prediction of sea level variations in the Yellow Sea, where the periodic terms and linear trend of sea level change are fitted and extrapolated using the least squares model, while the prediction of the residual terms is performed by several different types of artificial neural networks. The input and output data used are the sea level anomalies (SLA) time series in the Yellow Sea from 1993 to 2016 derived from ERS-1/2, Topex/Poseidon, Jason-1/2, and Envisat satellite altimetry missions. Tests of different neural network architectures and learning algorithms are performed to assess their applicability for predicting the residuals of SLA time series. Different neural networks satisfactorily provide reliable results and the root mean square errors of the predictions from the proposed combined approach are less than 2?cm and correlation coefficients between the observed and predicted SLA are up to 0.87. Results prove the reliability of the combined least squares-neural network approach on the short-term prediction of sea level variability close to the coast. 相似文献
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383.
ESCOBAR Luis E. ROMERO-ALVAREZ Daniel LARKIN Daniel J. PHELPS Nicholas B. D. 《海洋湖沼学报(英文)》2019,(3):1037-1041
Often facilitated by human-mediated pathways,aquatic invasive species are a threat to the health and biodiversity of global ecosystems.We present a novel approach incorporating survey data of watercraft movement in a social network analysis to reconstruct potential pathways of aquatic invasive species spread between lakes.As an example,we use the green alga Nitellopsis obtusa,also known as starry stonewort,an aquatic invasive species affecting the Great Lakes region in the United States and Canada.The movement of algal fragments via human-mediated pathways(i.e.,watercraft)has been hypothesized as the primary driver of starry stonewort invasion.We used survey data collected at boat ramps during the 2013 and 2014 openwater seasons to describe the flow of watercraft from Lake Koronis,where N.obtusa was first detected in Minnesota,to other lakes in the state.Our results suggest that the risk of N.obtusa expansion is not highly constrained by geographic proximity and management efforts should consider highly connected lakes.Estimating human movement via network analysis may help to explain past and future routes of aquatic invasive species infestation between lakes and can improve evidence-based prevention and control efforts. 相似文献
384.
ESCOBAR Luis E. ROMERO-ALVAREZ Daniel LARKIN Daniel J. PHELPS Nicholas B. D. 《海洋湖沼学报(英文)》2019,(3):1037-1041
Often facilitated by human-mediated pathways,aquatic invasive species are a threat to the health and biodiversity of global ecosystems.We present a novel approach incorporating survey data of watercraft movement in a social network analysis to reconstruct potential pathways of aquatic invasive species spread between lakes.As an example,we use the green alga Nitellopsis obtusa,also known as starry stonewort,an aquatic invasive species affecting the Great Lakes region in the United States and Canada.The movement of algal fragments via human-mediated pathways(i.e.,watercraft)has been hypothesized as the primary driver of starry stonewort invasion.We used survey data collected at boat ramps during the 2013 and 2014 openwater seasons to describe the flow of watercraft from Lake Koronis,where N.obtusa was first detected in Minnesota,to other lakes in the state.Our results suggest that the risk of N.obtusa expansion is not highly constrained by geographic proximity and management efforts should consider highly connected lakes.Estimating human movement via network analysis may help to explain past and future routes of aquatic invasive species infestation between lakes and can improve evidence-based prevention and control efforts. 相似文献
385.
针对大面积海底地形数据缺失或异常的复杂及多变性特点,结合条件变分自编码器(CVAE)与深度卷积生成对抗网络(DCGAN),构建了条件变分自编码生成对抗网络(CVAE-GAN)大面积海底伪地形的检测与剔除方法。本文方法利用条件变分自编码算法改变原有的样本分布,通过对训练样本的学习重新构建样本之间的分布规律,有效提高了高维到低维映射的稳定性;结合生成对抗网络,提高了整体算法的稳健性,最终得到较优的检测与剔除结果。采用水深格网数据进行试验,并与中值滤波法、趋势面滤波法进行比较。结果表明,本文方法在精度、稳定性及噪声稳健性方面有所提高,验证了本文方法在海底地形数据处理上具有可行性。 相似文献
386.
针对传统路网采集和更新需要昂贵的实地测量以及大量的后续内业处理问题,提出了一种从大规模粗糙轨迹数据中自动生成路网的方法。该方法包含轨迹滤选和路网增量构建两步:第1步通过构建空间、时间、逻辑约束的规则模型,在消除数据中的噪音和冗余的同时,将原始轨迹进行合理分割,滤选形成规范轨迹集合;第2步基于信息熵计算轨迹点周围道路的复杂度,据此自动调节道路分割参数,不断将新产生的路段加入到路网,同时计算道路平均交通流量和速度等路况信息,遍历各规范轨迹的定位点重复以上处理过程,最终得到完整路网。通过昆明市200辆出租车采集的约6851万条轨迹数据进行路网构建试验,并与OpenStreetMap数据比较,证明了本文方法的有效性。与已有典型方法比较,本文方法能用更少节点提取更高质量的路网。 相似文献
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388.
Simulation of landscape spatial layout evolution in rural-urban fringe areas: a case study of Ganjingzi District 总被引:2,自引:0,他引:2
In recent years, the rapid expansion of urban spaces has accelerated the mutual evolution of landscape types. Analyzing and simulating spatio-temporal dynamic features of urban landscape can help to reveal its driving mechanisms and facilitate reasonable planning of urban land resources. The purpose of this study was to design a hybrid cellular automata model to simulate dynamic change in urban landscapes. The model consists of four parts: a geospatial partition, a Markov chain (MC), a multi-layer perceptron artificial neural network (MLP-ANN), and cellular automata (CA). This study employed multivariate land use data for the period 2000–2015 to conduct spatial clustering for the Ganjingzi District and to simulate landscape status evolution via a divisional composite cellular automaton model. During the period of 2000–2015, construction land and forest land areas in Ganjingzi District increased by 19.43% and 15.19%, respectively, whereas farmland, garden lands, and other land areas decreased by 43.42%, 52.14%, and 75.97%, respectively. Land use conversion potentials in different sub-regions show different characteristics in space. The overall land-change prediction accuracy for the subarea-composite model is 3% higher than that of the non-partitioned model, and misses are reduced by 3.1%. Therefore, by integrating geospatial zoning and the MLP-ANN hybrid method, the land type conversion rules of different zonings can be obtained, allowing for more effective simulations of future urban land use change. The hybrid cellular automata model developed here will provide a reference for urban planning and policy formulation. 相似文献
389.
Availability of reliable delineation of urban lands is fundamental to applications such as infrastructure management and urban planning. An accurate semantic segmentation approach can assign each pixel of remotely sensed imagery a reliable ground object class. In this paper, we propose an end-to-end deep learning architecture to perform the pixel-level understanding of high spatial resolution remote sensing images. Both local and global contextual information are considered. The local contexts are learned by the deep residual net, and the multi-scale global contexts are extracted by a pyramid pooling module. These contextual features are concatenated to predict labels for each pixel. In addition, multiple additional losses are proposed to enhance our deep learning network to optimize multi-level features from different resolution images simultaneously. Two public datasets, including Vaihingen and Potsdam datasets, are used to assess the performance of the proposed deep neural network. Comparison with the results from the published state-of-the-art algorithms demonstrates the effectiveness of our approach. 相似文献
390.