Hydrograph clustering helps to identify dynamic patterns within aquifers systems, an important foundation of characterizing groundwater systems and their influences, which is necessary to effectively manage groundwater resources. We develope an unsupervised modeling approach to characterize and cluster hydrographs on regional scale according to their dynamics. We apply feature-based clustering to improve the exploitation of heterogeneous datasets, explore the usefulness of existing features and propose new features specifically useful to describe groundwater hydrographs. The clustering itself is based on a powerful combination of Self-Organizing Maps with a modified DS2L-Algorithm, which automatically derives the cluster number but also allows to influence the level of detail of the clustering. We further develop a framework that combines these methods with ensemble modeling, internal cluster validation indices, resampling and consensus voting to finally obtain a robust clustering result and remove arbitrariness from the feature selection process. Further we propose a measure to sort hydrographs within clusters, useful for both interpretability and visualization. We test the framework with weekly data from the Upper Rhine Graben System, using more than 1800 hydrographs from a period of 30 years (1986-2016). The results show that our approach is adaptively capable of identifying homogeneous groups of hydrograph dynamics. The resulting clusters show both spatially known and unknown patterns, some of which correspond clearly to external controlling factors, such as intensive groundwater management in the northern part of the test area. This framework is easily transferable to other regions and, by adapting the describing features, also to other time series-clustering applications.
In this paper, a novel hybrid structure of Pd doped ZnO/SnO2 heterojunction nanofibers with hexagonal ZnO columns was one step synthesized from electrospun precursor nanofibers. Due to the synergistic effect of hexagonal ZnO, SnO2 and Pd, the structure exhibited excellent hydrogen (H2) gas sensing properties. At low-temperature of 120 °C, the response (Ra/Rg) to 100 ppm H2 gas exceeded 160, the response/recovery time was only 20 s and 6 s respectively and the limit of detection was only 0.5 ppm. Meanwhile, it also had good selectivity for H2 gas and excellent linearity. In addition, the materials were characterized by XRD, FESEM, HRTEM, XPS, and the synthesis mechanism and gas sensing mechanism were proposed. 相似文献