Automatic Frequency-Based Flood Forecast From Numerical Weather Prediction Using A Service-Oriented Architecture

Destructive floods occurred more frequently in mountainous regions in China in recent years. However, the meteorological and hydrological station network in such regions is usually poor, and no long-series observations are available. Therefore, it is difficult to determine the hydrological parameters for flood discharge and stage forecast. This paper aims to propose an automatic frequency-based flood forecast framework from numerical weather prediction (NWP) using a Service Oriented Architecture (SOA). The proposed framework has 4 main steps. First, historical flood discharge is simulated by using a distributed hydrological model and satellite-derived rainfall dataset (e.g., the CMORPH and the TRMM), and the relationship between flood frequency and simulated flood discharge (i.e., the frequency curve) is established for each river reach. Second, by taking the advantages of the highly automatic SOA technology, the predicted rainfall data from the NWP (e.g., the TIGGE ensemble) are downloaded and interpreted automatically in real time. Third, a distributed hydrological model is automatically executed in the SOA environment to predict flow discharges of each river reach. And finally, the flood frequency is obtained from the simulated flow discharges by looking up the frequency curves, and warning information of possible floods is generated for potential sufferers. By using Web service in a social network, users can be informed such warning information at any time, and can make better preparation for the possible floods. Along with the real-time updates of the NWP, the latest warning information will always be available for users. From a sample demonstration, it can be concluded that the frequency-based flood forecast from the NWP is highly useful to enhance user awareness of flood risk, and the SOA and social network techniques are regarded as a feasible way for developing the automatic system

Learning to Infer User Hidden States for Online Sequential Advertising

To drive purchase in online advertising, it is of the advertiser's great interest to optimize the sequential advertising strategy whose performance and interpretability are both important. The lack of interpretability in existing deep reinforcement learning methods makes it not easy to understand, diagnose and further optimize the strategy. In this paper, we propose our Deep Intents Sequential Advertising (DISA) method to address these issues. The key part of interpretability is to understand a consumer's purchase intent which is, however, unobservable (called hidden states). In this paper, we model this intention as a latent variable and formulate the problem as a Partially Observable Markov Decision Process (POMDP) where the underlying intents are inferred based on the observable behaviors. Large-scale industrial offline and online experiments demonstrate our method's superior performance over several baselines. The inferred hidden states are analyzed, and the results prove the rationality of our inference.Comment: to be published in CIKM 202

A global assessment of the impact of school closure in reducing COVID-19 spread.

Prolonged school closure has been adopted worldwide to control COVID-19. Indeed, UN Educational, Scientific and Cultural Organization figures show that two-thirds of an academic year was lost on average worldwide due to COVID-19 school closures. Such pre-emptive implementation was predicated on the premise that school children are a core group for COVID-19 transmission. Using surveillance data from the Chinese cities of Shenzhen and Anqing together, we inferred that compared with the elderly aged 60 and over, children aged 18 and under and adults aged 19-59 were 75% and 32% less susceptible to infection, respectively. Using transmission models parametrized with synthetic contact matrices for 177 jurisdictions around the world, we showed that the lower susceptibility of school children substantially limited the effectiveness of school closure in reducing COVID-19 transmissibility. Our results, together with recent findings that clinical severity of COVID-19 in children is lower, suggest that school closure may not be ideal as a sustained, primary intervention for controlling COVID-19. This article is part of the theme issue 'Data science approach to infectious disease surveillance'

Psychometric assessment of HIV/STI sexual risk scale among MSM: A Rasch model approach

<p>Abstract</p> <p>Background</p> <p>Little research has assessed the degree of severity and ordering of different types of sexual behaviors for HIV/STI infection in a measurement scale. The purpose of this study was to apply the Rasch model on psychometric assessment of an HIV/STI sexual risk scale among men who have sex with men (MSM).</p> <p>Methods</p> <p>A cross-sectional study using respondent driven sampling was conducted among 351 MSM in Shenzhen, China. The Rasch model was used to examine the psychometric properties of an HIV/STI sexual risk scale including nine types of sexual behaviors.</p> <p>Results</p> <p>The Rasch analysis of the nine items met the unidimensionality and local independence assumption. Although the person reliability was low at 0.35, the item reliability was high at 0.99. The fit statistics provided acceptable infit and outfit values. Item difficulty invariance analysis showed that the item estimates of the risk behavior items were invariant (within error).</p> <p>Conclusions</p> <p>The findings suggest that the Rasch model can be utilized for measuring the level of sexual risk for HIV/STI infection as a single latent construct and for establishing the relative degree of severity of each type of sexual behavior in HIV/STI transmission and acquisition among MSM. The measurement scale provides a useful measurement tool to inform, design and evaluate behavioral interventions for HIV/STI infection among MSM.</p

Exploring Drought Conditions in the Three River Headwaters Region from 2002 to 2011 Using Multiple Drought Indices

The Three River Headwaters Region (TRHR) has great uncertainty on drought conditions under climate change. The aim of this study is to compare the drought conditions detected by multiple drought indices across the TRHR. We applied four single drought indices, i.e., Precipitation Condition Index (PCI), Temperature Condition Index (TCI), Soil Moisture Condition Index (SMCI), and Vegetation Condition Index (VCI), and two combined drought indices, i.e., Combined Meteorological Drought Index (CMDI) and Combined Vegetation drought index (CVDI), to explore the drought conditions across the TRHR. Three in situ drought indices, Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Non-Parametric Index (SNPI) were used to evaluate the performances of multiple drought indices. The results include various drought conditions detected by multiple drought indices, as well as a comparative study among different drought indices. Through the comparative study, we found that PCI was a desirable single index to monitor meteorological drought. TCI was suitable for monitoring agricultural/vegetation drought. SMCI and VCI should be avoided for monitoring drought in this region. CMDI was an appropriate meteorological drought index, and CVDI was a promising indicator in monitoring agricultural/vegetation drought

Comparison of Same Carbon Chain Length Cationic and Anionic Surfactant Adsorption on Silica

Adsorption of a cationic surfactant dodecyl pyridinium chloride (DPC) on silica was studied to show a comparison with the adsorption of an anionic surfactant sodium dodecyl sulfate (SDS), whose carbon chain length is the same and on the same silica. Results provided a better understanding of the adsorption mechanism of cationic and anionic surfactant on negatively charged silica. The experiment covered different electrolyte concentrations and pH values. Results indicated that at the same pH, the DPC adsorption amounts are higher when the electrolyte concentration is higher; at a higher DPC equilibrium concentration, the adsorption amount difference is larger than that at low DPC equilibrium concentration, and when DPC equilibrium concentration is lower than 0.1 mmol/L, the adsorption amount difference cannot be observed. At charge compensation point (CCP, 0 zeta potential), the negative surface charge of silica was compensated by DP+, a continuous increasing zeta potential indicated a bilayer adsorption of DPC on silica. The adsorption amount increased with increasing pH. The calculated lines by Gu and Zhu model show a two-step property, including a bilayer and hemi-micelle adsorption. DPC adsorbed more strongly on silica than SDS due to the combination of electrostatic and hydrophobic attraction

An Efficient Method for Mapping High-Resolution Global River Discharge Based on the Algorithms of Drainage Network Extraction

River discharge, which represents the accumulation of surface water flowing into rivers and ultimately into the ocean or other water bodies, may have great impacts on water quality and the living organisms in rivers. However, the global knowledge of river discharge is still poor and worth exploring. This study proposes an efficient method for mapping high-resolution global river discharge based on the algorithms of drainage network extraction. Using the existing global runoff map and digital elevation model (DEM) data as inputs, this method consists of three steps. First, the pixels of the runoff map and the DEM data are resampled into the same resolution (i.e., 0.01-degree). Second, the flow direction of each pixel of the DEM data (identified by the optimal flow path method used in drainage network extraction) is determined and then applied to the corresponding pixel of the runoff map. Third, the river discharge of each pixel of the runoff map is calculated by summing the runoffs of all the pixels in the upstream of this pixel, similar to the upslope area accumulation step in drainage network extraction. Finally, a 0.01-degree global map of the mean annual river discharge is obtained. Moreover, a 0.5-degree global map of the mean annual river discharge is produced to display the results with a more intuitive perception. Compared against the existing global river discharge databases, the 0.01-degree map is of a generally high accuracy for the selected river basins, especially for the Amazon River basin with the lowest relative error (RE) of 0.3% and the Yangtze River basin within the RE range of &plusmn;6.0%. However, it is noted that the results of the Congo and Zambezi River basins are not satisfactory, with RE values over 90%, and it is inferred that there may be some accuracy problems with the runoff map in these river basins

Prediction of Short-Time Cloud Motion Using a Deep-Learning Model

A cloud image can provide significant information, such as precipitation and solar irradiation. Predicting short-time cloud motion from images is the primary means of making intra-hour irradiation forecasts for solar-energy production and is also important for precipitation forecasts. However, it is very challenging to predict cloud motion (especially nonlinear motion) accurately. Traditional methods of cloud-motion prediction are based on block matching and the linear extrapolation of cloud features; they largely ignore nonstationary processes, such as inversion and deformation, and the boundary conditions of the prediction region. In this paper, the prediction of cloud motion is regarded as a spatiotemporal sequence-forecasting problem, for which an end-to-end deep-learning model is established; both the input and output are spatiotemporal sequences. The model is based on gated recurrent unit (GRU)- recurrent convolutional network (RCN), a variant of the gated recurrent unit (GRU), which has convolutional structures to deal with spatiotemporal features. We further introduce surrounding context into the prediction task. We apply our proposed Multi-GRU-RCN model to FengYun-2G satellite infrared data and compare the results to those of the state-of-the-art method of cloud-motion prediction, the variational optical flow (VOF) method, and two well-known deep-learning models, namely, the convolutional long short-term memory (ConvLSTM) and GRU. The Multi-GRU-RCN model predicts intra-hour cloud motion better than the other methods, with the largest peak signal-to-noise ratio and structural similarity index. The results prove the applicability of the GRU-RCN method for solving the spatiotemporal data prediction problem and indicate the advantages of our model for further applications