Identification and assessment of water safety risk for groundwater recharge with reclaimed water in China     

Zhen?Li  Chongqing?Xu  Miao?LiEmail author  Guihuan?Yan  Xiang?LiuEmail author  Yeping?Ma 《Stochastic Environmental Research and Risk Assessment (SERRA)》2017,31(7):1671-1682

Groundwater recharge using reclaimed water has developed rapidly around the world to relieve the groundwater resource shortage and declining of the water table. Traditional water treatment systems are inefficient to remove all the types of contaminants, so it is urgent to identify the priority chemical substances (CSs) that deserve our first concern. In this study, we developed a method (EER method) to identify priority CSs in groundwater recharge by surface spreading and direct aquifer injection. Three stages were processed which were exposure assessment, effect assessment and ranking for identification of priority CSs. Fourteen cities in China were selected for data collected and 90 pollutants in reclaimed water samples were analyzed as the target pollutants for a case study. According to three stages, the 90 CSs studied were divided into five groups (primary control CSs and high, moderate and low and no risk control CSs). In the primary control CSs and high, moderate and low and no risk control CSs group there were 14, 18, 21, 21 and 16 CSs, respectively when groundwater recharged by surface spreading, while there were 15, 18, 21, 21 and 15 CSs when recharged by direct injection. This method provided an indicator of prioritizing the risk of 90 compounds in the reclaimed water for groundwater recharge.  相似文献   

Integration of multiple soft data sets in MPS thru multinomial logistic regression: a case study of gas hydrates     

Hassan?RezaeeEmail author  Denis?Marcotte 《Stochastic Environmental Research and Risk Assessment (SERRA)》2017,31(7):1727-1745

A new approach is described to allow conditioning to both hard data (HD) and soft data for a patch- and distance-based multiple-point geostatistical simulation. The multinomial logistic regression is used to quantify the link between HD and soft data. The soft data is converted by the logistic regression classifier into as many probability fields as there are categories. The local category proportions are used and compared to the average category probabilities within the patch. The conditioning to HD is obtained using alternative training images and by imposing large relative weights to HD. The conditioning to soft data is obtained by measuring the probability–proportion patch distance. Both 2D and 3D cases are considered. Synthetic cases show that a stationary TI can generate non-stationary realizations reproducing the HD, keeping the texture indicated by the TI and following the trends identified in probability maps obtained from soft data. A real case study, the Mallik methane-hydrate field, shows perfect reproduction of HD while keeping a good reproduction of the TI texture and probability trends.  相似文献   

Complexity analysis of precipitation using the Lempel–Ziv algorithm and a multi-scaling approach: a case study in Jilin province,China     

Qian?Zhang  Xiujuan?Liang  Zhang?FangEmail author  Changlai?Xiao 《Stochastic Environmental Research and Risk Assessment (SERRA)》2017,31(7):1697-1707

Precipitation is an important part of the hydrologic cycle, and its complexity is closely related to surface runoff and changing groundwater dynamics, which in turn influences the accuracy of precipitation forecasts. In this study, we used the Lempel–Ziv algorithm (LZA) and a multi-scaling approach to assess precipitation complexity for 1958–2011 by analyzing time series data from 28 gauging stations located throughout Jilin province, China. The spatial distribution of normalized precipitation complexity was measured by LZA, a symbolic dynamics algorithm, and by a multi-scaling approach, which is described by fractals. In addition, the advantages and limitations of these two methods were investigated. The results indicate that both methods are applicable and consistent for calculating precipitation complexity, and that the degree of relief is a primary factor controlling precipitation complexity in the mountainous area; in the plain terrain, however, the prominent influencing factor is climate.  相似文献   

Spectral density regression for bivariate extremes     

Daniela?Castro Camilo  Miguel?de CarvalhoEmail authorView author&#;s OrcID profile 《Stochastic Environmental Research and Risk Assessment (SERRA)》2017,31(7):1603-1613

We introduce a density regression model for the spectral density of a bivariate extreme value distribution, that allows us to assess how extremal dependence can change over a covariate. Inference is performed through a double kernel estimator, which can be seen as an extension of the Nadaraya–Watson estimator where the usual scalar responses are replaced by mean constrained densities on the unit interval. Numerical experiments with the methods illustrate their resilience in a variety of contexts of practical interest. An extreme temperature dataset is used to illustrate our methods.  相似文献   

Spatial models for probabilistic prediction of wind power with application to annual-average and high temporal resolution data   总被引：1，自引：1，他引：0  

Amanda?LenziEmail authorView author&#;s OrcID profile  Pierre?Pinson  Line?H.?Clemmensen  Gilles?Guillot 《Stochastic Environmental Research and Risk Assessment (SERRA)》2017,31(7):1615-1631

Producing accurate spatial predictions for wind power generation together with a quantification of uncertainties is required to plan and design optimal networks of wind farms. Toward this aim, we propose spatial models for predicting wind power generation at two different time scales: for annual average wind power generation, and for a high temporal resolution (typically wind power averages over 15-min time steps). In both cases, we use a spatial hierarchical statistical model in which spatial correlation is captured by a latent Gaussian field. We explore how such models can be handled with stochastic partial differential approximations of Matérn Gaussian fields together with Integrated Nested Laplace Approximations. We demonstrate the proposed methods on wind farm data from Western Denmark, and compare the results to those obtained with standard geostatistical methods. The results show that our method makes it possible to obtain fast and accurate predictions from posterior marginals for wind power generation. The proposed method is applicable in scientific areas as diverse as climatology, environmental sciences, earth sciences and epidemiology.  相似文献   

Forecasting monthly inflow with extreme seasonal variation using the hybrid SARIMA-ANN model     

Hamid?Moeeni  Hossein?BonakdariEmail author 《Stochastic Environmental Research and Risk Assessment (SERRA)》2017,31(8):1997-2010

The optimal operation of dam reservoirs can be programmed and managed by predicting the inflow to these structures more accurately. To this end, there are various linear and nonlinear models. However, some hydrological problems like inflow with extreme seasonal variation are not purely linear or nonlinear. To improve the forecasting accuracy of this phenomenon, a linear Seasonal Auto Regressive Integrated Moving Average (SARIMA) model is combined with a nonlinear Artificial Neural Network (ANN) model. This new model is used to predict the monthly inflow to the Jamishan dam reservoir in West Iran. A comparison of the SARIMA and ANN models with the proposed hybrid model’s results is provided accordingly. More specifically, the models’ performance in forecasting base and flood flows is evaluated. The effect of changing the forecasting period length on the models’ accuracy is studied. The results of increasing the number of SARIMA model parameters up to five are investigated to achieve more accurate forecasting. The hybrid model predicts peak flood flows much better than the individual models, but SARIMA outperforms the other models in predicting base flow. The obtained results indicate that the hybrid model reduces the overall forecast error more than the ANN and SARIMA models. The coefficient of determination of the hybrid, ANN and SARIMA models were 0.72, 0.64 and 0.58, and the root mean squared error values were 1.02, 1.16 and 1.27 respectively, during the forecast period. Changing the forecasting length also indicated that these models can be used in the long term without increasing the forecast error.  相似文献   

Estimation of extreme Component-wise Excess design realization: a hydrological application     

E.?Di?Bernardino  F.?Palacios-RodríguezEmail author 《Stochastic Environmental Research and Risk Assessment (SERRA)》2017,31(10):2675-2689

The classic univariate risk measure in environmental sciences is the Return Period (RP). The RP is traditionally defined as “the average time elapsing between two successive realizations of a prescribed event”. The notion of design quantile related with RP is also of great importance. The design quantile represents the “value of the variable(s) characterizing the event associated with a given RP”. Since an individual risk may strongly be affected by the degree of dependence amongst all risks, the need for the provision of multivariate design quantiles has gained ground. In contrast to the univariate case, the design quantile definition in the multivariate setting presents certain difficulties. In particular, Salvadori, G., De Michele, C. and Durante F. define in the paper called “On the return period and design in a multivariate framework” (Hydrol Earth Syst Sci 15:3293–3305, 2011) the design realization as the vector that maximizes a weight function given that the risk vector belongs to a given critical layer of its joint multivariate distribution function. In this paper, we provide the explicit expression of the aforementioned multivariate risk measure in the Archimedean copula setting. Furthermore, this measure is estimated by using Extreme Value Theory techniques and the asymptotic normality of the proposed estimator is studied. The performance of our estimator is evaluated on simulated data. We conclude with an application on a real hydrological data-set.  相似文献   

Reproduction of secondary data in projection pursuit transformation     

John?G.?ManchukEmail author  Ryan?M.?Barnett  Clayton?V.?Deutsch 《Stochastic Environmental Research and Risk Assessment (SERRA)》2017,31(10):2585-2605

A challenge when working with multivariate data in a geostatistical context is that the data are rarely Gaussian. Multivariate distributions may include nonlinear features, clustering, long tails, functional boundaries, spikes, and heteroskedasticity. Multivariate transformations account for such features so that they are reproduced in geostatistical models. Projection pursuit as developed for high dimensional data exploration can also be used to transform a multivariate distribution into a multivariate Gaussian distribution with an identity covariance matrix. Its application within a geostatistical modeling context is called the projection pursuit multivariate transform (PPMT). An approach to incorporate exhaustive secondary variables in the PPMT is introduced. With this approach the PPMT can incorporate any number of secondary variables with any number of primary variables. A necessary alteration to the approach to make this numerically practical was the implementation of a continuous probability estimator that relies on Bernstein polynomials for the transformation that takes place in the projections. Stopping criteria were updated to incorporate a bootstrap t test that compares data sampled from a multivariate Gaussian distribution with the data undergoing transformation.  相似文献   

Multi-models for SPI drought forecasting in the north of Haihe River Basin,China     

Yuhu?Zhang  Weiwei?LiEmail author  Qiuhua?Chen  Xiao?Pu  Liu?Xiang 《Stochastic Environmental Research and Risk Assessment (SERRA)》2017,31(10):2471-2481

Drought is one of the most devastating climate disasters. Hence, drought forecasting plays an important role in mitigating some of the adverse effects of drought. Data-driven models are widely used for drought forecasting such as ARIMA model, artificial neural network (ANN) model, wavelet neural network (WANN) model, support vector regression model, grey model and so on. Three data-driven models (ARIMA model; ANN model; WANN model) are used in this study for drought forecasting based on standard precipitation index of two time scales (SPI; SPI-6 and SPI-12). The optimal data-driven model and time scale of SPI are then selected for effective drought forecasting in the North of Haihe River Basin. The effectiveness of the three data-models is compared by Kolmogorov–Smirnov (K–S) test, Kendall rank correlation, and the correlation coefficients (R²). The forecast results shows that the WANN model is more suitable and effective for forecasting SPI-6 and SPI-12 values in the north of Haihe River Basin.  相似文献   

<Emphasis Type="Italic">Clostridium difficile</Emphasis> infection incidence prediction in hospitals (CDIIPH): a predictive model based on decision tree and fuzzy techniques     

Gyan?Chhipi-ShresthaEmail author  Julie?Mori  Kasun?Hewage  Rehan?Sadiq 《Stochastic Environmental Research and Risk Assessment (SERRA)》2017,31(2):417-430

Several risk factors associated with the increased likelihood of healthcare-associated Clostridium difficile infection (CDI) have been identified in the literature. These risk factors are mainly related to age, previous CDI, antimicrobial exposure, and prior hospitalization. No model is available in the published literature that can be used to predict the CDI incidence using healthcare administration data. However, the administrative data can be imprecise and may challenge the building of classical statistical models. Fuzzy set theory can deal with the imprecision inherent in such data. This research aimed to develop a model based on deterministic and fuzzy mathematical techniques for the prediction of hospital-associated CDI by using the explanatory variables controllable by hospitals and health authority administration. Retrospective data on CDI incidence and other administrative data obtained from 22 hospitals within a regional health authority in British Columbia were used to develop a decision tree (deterministic technique based) and a fuzzy synthetic evaluation model (fuzzy technique based). The decision tree model had a higher prediction accuracy than that of the fuzzy based model. However, among the common results predicted by two models, 72 % were correct. Therefore, this relationship was used to combine their results to increase the precision and the strength of evidence of the prediction. These models were further used to develop an Excel-based tool called C. difficile Infection Incidence Prediction in Hospitals (CDIIPH). The tool can be utilized by health authorities and hospitals to predict the magnitude of CDI incidence in the following quarter.  相似文献