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Floods of research and practical applications employ social media data for a wide range of public applications, including environmental monitoring, water resource managing, disaster and emergency response.Hydroinformatics can benefit from the social media technologies with newly emerged data, techniques and analytical tools to handle large datasets, from which creative ideas and new values could be mined.This paper first proposes a 4W (What, Why, When, hoW) model and a methodological structurearXiv:1905.03035v1 fatcat:72ignyiinjabnk2duldns475la
more »... o better understand and represent the application of social media to hydroinformatics, then provides an overview of academic research of applying social media to hydroinformatics such as water environment, water resources, flood, drought and water Scarcity management. At last,some advanced topics and suggestions of water related social media applications from data collection, data quality management, fake news detection, privacy issues, algorithms and platforms was present to hydroinformatics managers and researchers based on previous discussion.
A Novel Trend Symbolic Aggregate Approximation for Time Series Yufeng Yu, Yuelong Zhu, Dingsheng Wan,Qun Zhao Huan Liu College of Computer and Information ... Zhu, B. Hu, Y. Hao, X. Xi, L. Wei, C.A. ...arXiv:1905.00421v1 fatcat:wnhu335kmbditekqdlqcsvcxgy
Multivariate time series (MTS) datasets are very common in various financial, multimedia, and hydrological fields. In this paper, a dimension-combination method is proposed to search similar sequences for MTS. Firstly, the similarity of single-dimension series is calculated; then the overall similarity of the MTS is obtained by synthesizing each of the single-dimension similarity based on weighted BORDA voting method. The dimension-combination method could use the existing similarity searchingdoi:10.1155/2014/851017 pmid:24895665 pmcid:PMC4034716 fatcat:5dqgmdzzfzcwxjve72tmjzofnu
more »... ethod. Several experiments, which used the classification accuracy as a measure, were performed on six datasets from the UCI KDD Archive to validate the method. The results show the advantage of the approach compared to the traditional similarity measures, such as Euclidean distance (ED), cynamic time warping (DTW), point distribution (PD), PCA similarity factorSPCA, and extended Frobenius norm (Eros), for MTS datasets in some ways. Our experiments also demonstrate that no measure can fit all datasets, and the proposed measure is a choice for similarity searches.
Journal of Computers
In this paper, we evaluate some techniques for the time series similarity searching. Many distance measures have been proposed as alternatives to the Euclidean distance in the similarity searching. To verify the assumption that the combination of various similarity measures may produce more accurate similarity searching results, we propose an multi-measure algorithm to combine several measures based on weighted BORDA voting method. The proposed method is validated by the analysis results of thedoi:10.4304/jcp.9.10.2266-2273 fatcat:ypgc4ftfnfezdciwszckzxlhpe
more »... flood data obtained from Wangjiaba in the Huaihe basin of China.
YUELONG ZHU is currently a Professor with the College of Computer and Information, Hohai University, Nanjing, China. ... Zhu et al.: Flood Prediction Using Rainfall-Flow Pattern in Data-Sparse Watersheds ...doi:10.1109/access.2020.2971264 fatcat:crmi4vrzszgutdsvi3ap2cnemi
The rapid development of social networking platforms in recent years has made it possible for scholars to find partners who share similar research interests. Nevertheless, this task has become increasingly challenging with the dramatic increase in the number of scholar users over social networks. Scholar recommendation has recently become a hot topic. Thus, we propose a personalized scholar recommendation approach, Mul-RSR (Multi-dimensional features based Research Scholar Recommendation),doi:10.3390/app11188664 fatcat:gehewatlefbjhk4xwrv3agf5gy
more »... improves accuracy and interpretability. In this work, Mul-RSR aims to provide personalized recommendation for academic social platforms. Mul-RSR uses the Doc2Vec text model and the random walk algorithm to calculate textual similarity and social relevance to measure the correlation between scholars. It is able to recommend Top-N scholars for each scholar based on multi-layer perception and attention mechanism. To evaluate the proposed approach, we conduct a series of experiments based on public and self-collected ResearchGate datasets. The results demonstrate that our approach improves the recommendation hit rate, and the hit rate reaches 59.31% when the N value is 30. Through these evaluations, we show Mul-RSR can provide a more solid scientific decision-making basis and achieve a better recommendation effect.
Testing safety climate variations across levels of analysis.doi:10.3724/sp.j.1042.2014.01964 fatcat:atldiw2hxffnbfvfcn5gkeywnu
Trend detection in observations helps one to identify anthropogenic forces on natural hydrological and climatic systems. Hydrometeorological data are often persistent over time that deviates from the assumption of independence used by many statistical methods. A recently proposed Sen's trend test claimed to be free of this problem and thereby received widespread attention. However, both theoretical derivation and stochastic simulation of the current study implies that persistence inflates thedoi:10.3390/w11102119 fatcat:oisec4f3pjc7vjvawlo2jvlz3e
more »... end significance, leading to false trends. To tackle this problem, we incorporate the feature of persistence into the variance of the trend test statistic, whereby an innovative variance-corrected Sen's trend test is developed. Two theoretical variances of the trend test statistic are newly derived to account for short-term and long-term persistent behavior. The original variance for independent data is also corrected because of its negative bias. A stepwise procedure, including steps to specify the underlying persistent behavior and to test trend with new statistic, is outlined for performing the new test on factual data. Variance-corrected Sen's trend test can effectively restore the inflated trend significance back to its nominal state. This study may call for the reassessment of published results of the original Sen's trend test on data with persistence.
Motivation: The virulence of influenza viruses is a complex multigenic trait. Previous studies about the virulence determinants of influenza viruses mainly focused on amino acid sites, ignoring the influence of nucleotide mutations. Results: We collected more than 200 viral strains from 21 subtypes of influenza A viruses with virulence in mammals and obtained over 100 mammalian virulence-related nucleotide sites across the genome by computational analysis. Interestingly, 50 of these nucleotidedoi:10.1101/416586 fatcat:st7mthp2nfhvjay2mdjogzceru
more »... ites only experienced synonymous mutations. Further experiments showed that synonymous mutations in the top two of these nucleotide sites, i.e., PB1-2031 and PB1-633, enhanced the pathogenicity of the viruses in mice. Finally, machine-learning models with accepted accuracy for predicting mammalian virulence of influenza A viruses were built. Overall, this study highlighted the importance of nucleotide mutations, especially synonymous mutations in viral virulence, and provided rapid methods for evaluating the virulence of influenza A viruses. It could be helpful for early warning of newly emerging influenza A viruses.
YUELONG ZHU was born in Jianhu, Jiangsu, in December 1959. He received the B.S. degree in automation engineering from Hohai University, Nanjing, China, in 1982. He taught in Hohai University. ...doi:10.1109/access.2020.2990181 fatcat:4uum33izfjbqfjqu7gjzxnygvu
With significant development of sensors and Internet of things, researchers nowadays can easily know what happens in physical space by acquiring time-varying values of various factors. Essentially, growing data category and size greatly contribute to solve problems happened in physical space. In this paper, we aim to solve a complex problem that affects both cities and villages, i.e., flood. To reduce impacts induced by floods, hydrological factors acquired from physical space and data-drivendoi:10.1155/2020/7670382 fatcat:ktmexp3spvgepoxdl2nrdlpdsy
more »... dels in cyber space have been adopted to accurately forecast floods. Considering the significance of modeling attention capability among hydrology factors, we believe extraction of discriminative hydrology factors not only reflect natural rules in physical space, but also optimally model iterations of factors to forecast run-off values in cyber space. Therefore, we propose a novel data-driven model named as STA-LSTM by integrating Long Short-Term Memory (LSTM) structure and spatiotemporal attention module, which is capable of forecasting floods for small- and medium-sized rivers. The proposed spatiotemporal attention module firstly explores spatial relationship between input hydrological factors from different locations and run-off outputs, which assigns time-varying weights to various factors. Afterwards, the proposed attention module allocates temporal-dependent weights to hidden output of each LSTM cell, which describes significance of state output for final forecasting results. Taking Lech and Changhua river basins as cases of physical space, several groups of comparative experiments show that STA-LSTM is capable to optimize complexity of mathematically modeling floods in cyber space.
Forecasting is one of the important research topics in the analysis of the hydrological time series. In order to improve the prediction accuracy for complex flood process, this paper presents a hybrid prediction method, which is based on combining multiple support vector machine (SVM) models. According to different discharge levels, multiple submodels are established respectively, from which the final result is integrated. For each sub-model, the input is optimally determined by elaboratedoi:10.1016/j.proeng.2012.01.695 fatcat:mkuyzd6dtrh5remmik4vz7p4oa
more »... ation analysis. Experimental results on the discharge prediction of Wangjiaba station on Huaihe River of China show that the hybrid model can significantly improve the prediction accuracy, compared to the single model without partitioning of the discharge.
In order to detect outliers in hydrological time series data for improving data quality and decision-making quality related to design, operation, and management of water resources, this research develops a time series outlier detection method for hydrologic data that can be used to identify data that deviate from historical patterns. The method first built a forecasting model on the history data and then used it to predict future values. Anomalies are assumed to take place if the observeddoi:10.1155/2014/879736 fatcat:gvozebwylzfstevt6m5uysfy4a
more »... fall outside a given prediction confidence interval (PCI), which can be calculated by the predicted value and confidence coefficient. The use ofPCIas threshold is mainly on the fact that it considers the uncertainty in the data series parameters in the forecasting model to address the suitable threshold selection problem. The method performs fast, incremental evaluation of data as it becomes available, scales to large quantities of data, and requires no preclassification of anomalies. Experiments with different hydrologic real-world time series showed that the proposed methods are fast and correctly identify abnormal data and can be used for hydrologic time series analysis.
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