PROBABILISTIC MODELING OF RAINFALL INDUCED LANDSLIDE HAZARD ASSESSMENT IN SAN JUAN LA LAGUNA, SOLOLÁ, GUATEMALA

The municipality of San Juan La Laguna, Guatemala is home to approximately 5,200 people and located on the western side of the Lake Atitlán caldera. Steep slopes surround all but the eastern side of San Juan. The Lake Atitlán watershed is susceptible to many natural hazards, but most predictable are the landslides that can occur annually with each rainy season, especially during high-intensity events. Hurricane Stan hit Guatemala in October 2005; the resulting flooding and landslides devastated the Atitlán region. Locations of landslide and non-landslide points were obtained from field observations and orthophotos taken following Hurricane Stan. This study used data from multiple attributes, at every landslide and non-landslide point, and applied different multivariate analyses to optimize a model for landslides prediction during high-intensity precipitation events like Hurricane Stan. The attributes considered in this study are: geology, geomorphology, distance to faults and streams, land use, slope, aspect, curvature, plan curvature, profile curvature and topographic wetness index. The attributes were pre-evaluated for their ability to predict landslides using four different attribute evaluators, all available in the open source data mining software Weka: filtered subset, information gain, gain ratio and chi-squared. Three multivariate algorithms (decision tree J48, logistic regression and BayesNet) were optimized for landslide prediction using different attributes. The following statistical parameters were used to evaluate model accuracy: precision, recall, F measure and area under the receiver operating characteristic (ROC) curve. The algorithm BayesNet yielded the most accurate model and was used to build a probability map of landslide initiation points. The probability map developed in this study was also compared to the results of a bivariate landslide susceptibility analysis conducted for the watershed, encompassing Lake Atitlán and San Juan. Landslides from Tropical Storm Agatha 2010 were used to independently validate this study’s multivariate model and the bivariate model. The ultimate aim of this study is to share the methodology and results with municipal contacts from the author's time as a U.S. Peace Corps volunteer, to facilitate more effective future landslide hazard planning and mitigation.

Corrected kriging update formulae for batch-sequential data assimilation

Recently, a lot of effort has been spent in the efficient computation of kriging predictors when observations are assimilated sequentially. In particular, kriging update formulae enabling significant computational savings were derived. Taking advantage of the previous kriging mean and variance computations helps avoiding a costly matrix inversion when adding one observation to the TeX already available ones. In addition to traditional update formulae taking into account a single new observation, Emery (2009) proposed formulae for the batch-sequential case, i.e. when TeX new observations are simultaneously assimilated. However, the kriging variance and covariance formulae given in Emery (2009) for the batch-sequential case are not correct. In this paper, we fix this issue and establish correct expressions for updated kriging variances and covariances when assimilating observations in parallel. An application in sequential conditional simulation finally shows that coupling update and residual substitution approaches may enable significant speed-ups.

Effects of inspiratory muscle training in patients with heart failure with preserved ejection fraction

BACKGROUND Heart failure with preserved ejection fraction (HFpEF) is remarkably common in elderly people with highly prevalent comorbid conditions. Despite its increasing in prevalence, there is no evidence-based effective therapy for HFpEF. We sought to evaluate whether inspiratory muscle training (IMT) improves exercise capacity, as well as left ventricular diastolic function, biomarker profile and quality of life (QoL) in patients with advanced HFpEF and nonreduced maximal inspiratory pressure (MIP). DESIGN AND METHODS A total of 26 patients with HFpEF (median (interquartile range) age, peak exercise oxygen uptake (peak VO2) and left ventricular ejection fraction of 73 years (66-76), 10 ml/min/kg (7.6-10.5) and 72% (65-77), respectively) were randomized to receive a 12-week programme of IMT plus standard care vs. standard care alone. The primary endpoint of the study was evaluated by positive changes in cardiopulmonary exercise parameters and distance walked in 6 minutes (6MWT). Secondary endpoints were changes in QoL, echocardiogram parameters of diastolic function, and prognostic biomarkers. RESULTS The IMT group improved significantly their MIP (p < 0.001), peak VO2 (p < 0.001), exercise oxygen uptake at anaerobic threshold (p = 0.001), ventilatory efficiency (p = 0.007), metabolic equivalents (p < 0,001), 6MWT (p < 0.001), and QoL (p = 0.037) as compared to the control group. No changes on diastolic function parameters or biomarkers levels were observed between both groups. CONCLUSIONS In HFpEF patients with low aerobic capacity and non-reduced MIP, IMT was associated with marked improvement in exercise capacity and QoL.

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