ALBERT

Description: In order to analyze the dynamic processes of the Earth interior and the effect of the propagation of the seismic waves to the surface, a comprehensive study of the Earth crust kinematics is necessary. Although the Global Positing System (GPS) is a powerful method to measure ground displacements and velocities both horizontally and vertically as well as to infer the tectonic stress regime generated by the subsurface processes (from local fault systems to huge tectonic plate movements and active volcanoes), the complexity of the deformation pattern generated during such movements is not always easy to be interpreted. Therefore, it is necessary to work on new methodologies and modifying the previous approaches in order to improve the current methods and better understand the crustal movements. In this paper, we focus on western Alaska area, where many complex faults and active volcanoes exist. In particular, we analyze the data acquired each 30 seconds by three GPS stations located in western Alaska (AC31, AB09 and AB11) from January 1, 2012 to December 31, 2012 in order to compute their displacements in horizontal and vertical components by vectorial summation of the average daily and annual velocities components. Furthermore, we design non-parametric DMeyer and Haar wavelets for horizontal and vertical velocities directions in order to identify significant and homogenous displacements during the year 2012. Finally, the non-parametric decomposition of total horizontal and vertical normalized velocities based on level 1 and level 2 coefficients have been applied to compute normal and cumulative probability histograms related to the accuracy and statistical evolution of each applied wavelet. The results present a very good agreement between the designed non-parametric wavelets and their decomposition functions for each of the three above mentioned GPS stations displacements and velocities during the year 2012.

Description: The comprehensive study of seismic waves is very important in order to understand the complex dynamic processes of the Earth’s interior as well as its signals emerged to the physical surface. In the last three decades, observational Global Positing System (GPS) products through determining the displacements of ground GPS station in horizontal and vertical directions have widely been applied to infer the tectonic stress regimes generated by the subsurface processes ranging from the local fault systems to the huge tectonic plate movements. However, the complex patterns generated during such movements are not always easy to interpret. Therefore, it is necessary to develop new approaches by modifying the previous strategies and improve the current methodologies to understand better such sudden crustal movements. In this paper, we employed 5 years GPS stations displacements data from January 1, 2008 to December 31, 2012 in the seismically active central Alaska area, in order to get the average daily and annual velocities of the GPS stations. Then, vector summation for horizontal and vertical velocities has been applied to yield the total velocities of GPS stations displacements. Moreover, we applied the Cross-Correlation Functions (CCFs) analysis to recognize the significant and homogenous displacements among the total displacements of GPS stations located in this region to be employed in next step for the superimposed decomposition of wavelet analysis at level number 1 and 2. Finally, the normal probability histograms related to the accuracy of each analysis are calculated and presented in details. The results show a very good agreement between the CCFs reorganizations, proposed wavelet decomposition methodology, and simultaneous earthquakes regimes occurred in central Alaska from 2008 to 2012 year.

Description: IJERPH, Vol. 15, Pages 77: Socio-Demographic and Mental Health Profile of Admitted Cases of Self-Inflicted Harm in the US Population International Journal of Environmental Research and Public Health doi: 10.3390/ijerph15010077 Authors: Chris Hanuscin Golara Zahmatkesh Anaheed Shirazi Deyu Pan Senait Teklehaimanot Shahrzad Bazargan-Hejazi Self-inflicted harm (SIH) has a substantial lifetime prevalence, it is associated with tremendous costs, and its rate is increasing on a national scale. To examine the characteristics of those admitted for SIH in the US and to investigate the factors that potentially modify the methods used for SIH. This was a retrospective analysis of admitted cases of SIH including suicide attempts between 2007 and 2012 using the National Trauma Data Bank. We included a total of 204,633 cases admitted for SIH. Our participants were 75.1% males. Those aged 15–24 (21%), 25–34 (22%), 35–44 (19%), 45–54 (19%), and 55–64 (10%) years comprised the largest age groups among our cases—70.8%, 11.5%, 11.1%, and 6.6% were, respectively, Caucasians, Hispanics, Blacks, and Asian/Others. Analyses of the SIH methods revealed that Blacks were less likely to self-poison [Odds Ratio (OR): 0.78] compared to Whites, whereas individuals with psychiatric disorders or substance abuse carried 2.5 and 2.0-fold higher risk, respectively. Blacks were also less likely to use anoxic methods (OR: 0.69), whereas patients with psychiatric disorders or substance abuse carried 1.5-fold higher risk. Being Black, Hispanic, and Asian (OR: 0.58, 0.55, and 0.55, respectively) as well as having psychiatric disorders (OR: 0.80) were associated with lower risks of using firearms, whereas its risk was increased with increasing age. Blacks (OR: 0.77) were less likely to cut or pierce in contrast to Hispanics (OR: 1.4), Asians/Others (OR: 1.29), and those with psychiatric disorders (2.5-fold higher risk) or drug abuse (2-fold higher risk). Blacks (OR: 1.11), Hispanics (OR: 1.13), and Asians/Others (OR: 1.57) were more likely to jump from high places, whereas those with substance abuse were less likely (OR: 0.77). Among patients admitted for SIH, males, those aged 15–64 years, and Whites comprised the largest sex, age, and racial/ethnic groups, respectively. We also found that several factors including race/ethnicity, gender, age, and having concurrent psychiatric or drug abuse disorders can potentially influence the methods used for SIH.

Description: Estimating maximum possible rainfall is of great value for flood prediction and protection, particularly for regions, such as Canada, where urban and fluvial floods from extreme rainfalls have been known to be a major concern. In this study, a methodology is proposed to forecast real-time rainfall (with one month lead time) using different number of spatial inputs with different orders of lags. For this purpose, two types of models are used. The first one is a machine learning data driven-based model, which uses a set of hydrologic variables as inputs, and the second one is an empirical-statistical model that employs the multi-criteria decision analysis method for rainfall forecasting. The data driven model is built based on Artificial Neural Networks (ANNs), while the developed multi-criteria decision analysis model uses Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach. A comprehensive set of spatially varying climate variables, including geopotential height, sea surface temperature, sea level pressure, humidity, temperature and pressure with different orders of lags is collected to form input vectors for the forecast models. Then, a feature selection method is employed to identify the most appropriate predictors. Two sets of results from the developed models, i.e., maximum daily rainfall in each month (RMAX) and cumulative value of rainfall for each month (RCU), are considered as the target variables for forecast purpose. The results from both modeling approaches are compared using a number of evaluation criteria such as Nash-Sutcliffe Efficiency (NSE). The proposed models are applied for rainfall forecasting for a coastal area in Western Canada: Vancouver, British Columbia. Results indicate although data driven models such as ANNs work well for the simulation purpose, developed TOPSIS model considerably outperforms ANNs for the rainfall forecasting. ANNs show acceptable simulation performance during the calibration period (NSE up to 0.9) but they fail for the validation (NSE of 0.2) and forecasting (negative NSE). The TOPSIS method delivers better rainfall forecasting performance with the NSE of about 0.7. Moreover, the number of predictors that are used in the TOPSIS model are significantly less than those required by the ANNs to show an acceptable performance (7 against 47 for forecasting RCU and 6 against 32 for forecasting RMAX). Reliable and precise rainfall forecasting, with adequate lead time, benefits enhanced flood warning and decision making to reduce potential flood damages.

Description: Hydrology, Vol. 5, Pages 10: Comparing Machine Learning and Decision Making Approaches to Forecast Long Lead Monthly Rainfall: The City of Vancouver, Canada Hydrology doi: 10.3390/hydrology5010010 Authors: Zahra Zahmatkesh Erfan Goharian Estimating maximum possible rainfall is of great value for flood prediction and protection, particularly for regions, such as Canada, where urban and fluvial floods from extreme rainfalls have been known to be a major concern. In this study, a methodology is proposed to forecast real-time rainfall (with one month lead time) using different number of spatial inputs with different orders of lags. For this purpose, two types of models are used. The first one is a machine learning data driven-based model, which uses a set of hydrologic variables as inputs, and the second one is an empirical-statistical model that employs the multi-criteria decision analysis method for rainfall forecasting. The data driven model is built based on Artificial Neural Networks (ANNs), while the developed multi-criteria decision analysis model uses Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach. A comprehensive set of spatially varying climate variables, including geopotential height, sea surface temperature, sea level pressure, humidity, temperature and pressure with different orders of lags is collected to form input vectors for the forecast models. Then, a feature selection method is employed to identify the most appropriate predictors. Two sets of results from the developed models, i.e., maximum daily rainfall in each month (RMAX) and cumulative value of rainfall for each month (RCU), are considered as the target variables for forecast purpose. The results from both modeling approaches are compared using a number of evaluation criteria such as Nash-Sutcliffe Efficiency (NSE). The proposed models are applied for rainfall forecasting for a coastal area in Western Canada: Vancouver, British Columbia. Results indicate although data driven models such as ANNs work well for the simulation purpose, developed TOPSIS model considerably outperforms ANNs for the rainfall forecasting. ANNs show acceptable simulation performance during the calibration period (NSE up to 0.9) but they fail for the validation (NSE of 0.2) and forecasting (negative NSE). The TOPSIS method delivers better rainfall forecasting performance with the NSE of about 0.7. Moreover, the number of predictors that are used in the TOPSIS model are significantly less than those required by the ANNs to show an acceptable performance (7 against 47 for forecasting RCU and 6 against 32 for forecasting RMAX). Reliable and precise rainfall forecasting, with adequate lead time, benefits enhanced flood warning and decision making to reduce potential flood damages.