Extreme Precipitation Modelling Using Geostatistics and Machine Learning Algorithms

The paper presents an approach for mapping of precipitation data. The main goal is to perform spatial predictions and simulations of precipitation fields using geostatistical methods (ordinary kriging, kriging with external drift) as well as machine learning algorithms (neural networks). More practically, the objective is to reproduce simultaneously both the spatial patterns and the extreme values. This objective is best reached by models integrating geostatistics and machine learning algorithms. To demonstrate how such models work, two case studies have been considered: first, a 2-day accumulation of heavy precipitation and second, a 6-day accumulation of extreme orographic precipitation. The first example is used to compare the performance of two optimization algorithms (conjugate gradients and Levenberg-Marquardt) of a neural network for the reproduction of extreme values. Hybrid models, which combine geostatistical and machine learning algorithms, are also treated in this context. The second dataset is used to analyze the contribution of radar Doppler imagery when used as external drift or as input in the models (kriging with external drift and neural networks). Model assessment is carried out by comparing independent validation errors as well as analyzing data patterns.

Validation of Logistic Regression Models for Landslide Susceptibility Maps

A wide range of numerical models and tools have been developed over the last decades to support the decision making process in environmental applications, ranging from physical models to a variety of statistically-based methods. In this study, a landslide susceptibility map of a part of Three Gorges Reservoir region of China was produced, employing binary logistic regression analyses. The available information includes the digital elevation model of the region, geological map and different GIS layers including land cover data obtained from satellite imagery. The landslides were observed and documented during the field studies. The validation analysis is exploited to investigate the quality of mapping.

Detection of Optimal Models in Parameter Space with Support Vector Machines

The paper proposes an approach aimed at detecting optimal model parameter combinations to achieve the most representative description of uncertainty in the model performance. A classification problem is posed to find the regions of good fitting models according to the values of a cost function. Support Vector Machine (SVM) classification in the parameter space is applied to decide if a forward model simulation is to be computed for a particular generated model. SVM is particularly designed to tackle classification problems in high-dimensional space in a non-parametric and non-linear way. SVM decision boundaries determine the regions that are subject to the largest uncertainty in the cost function classification, and, therefore, provide guidelines for further iterative exploration of the model space. The proposed approach is illustrated by a synthetic example of fluid flow through porous media, which features highly variable response due to the parameter values' combination.

Machine learning for spatial environmental data. Theory, applications and software

The book presents the state of the art in machine learning algorithms (artificial neural networks of different architectures, support vector machines, etc.) as applied to the classification and mapping of spatially distributed environmental data. Basic geostatistical algorithms are presented as well. New trends in machine learning and their application to spatial data are given, and real case studies based on environmental and pollution data are carried out. The book provides a CD-ROM with the Machine Learning Office software, including sample sets of data, that will allow both students and researchers to put the concepts rapidly to practice.

Influence of age, risk factors, and cardiovascular and renal disease on arterial stiffness: clinical applications.

Age is the main clinical determinant of large artery stiffness. Central arteries stiffen progressively with age, whereas peripheral muscular arteries change little with age. A number of clinical studies have analyzed the effects of age on aortic stiffness. Increase of central artery stiffness with age is responsible for earlier wave reflections and changes in pressure wave contours. The stiffening of aorta and other central arteries is a potential risk factor for increased cardiovascular morbidity and mortality. Arterial stiffening with aging is accompanied by an elevation in systolic blood pressure (BP) and pulse pressure (PP). Although arterial stiffening with age is a common situation, it has now been confirmed that older subjects with increased arterial stiffness and elevated PP have higher cardiovascular morbidity and mortality. Increase in aortic stiffness with age occurs gradually and continuously, similarly for men and women. Cross-sectional studies have shown that aortic and carotid stiffness (evaluated by the pulse wave velocity) increase with age by approximately 10% to 15% during a period of 10 years. Women always have 5% to 10% lower stiffness than men of the same age. Although large artery stiffness increases with age independently of the presence of cardiovascular risk factors or other associated conditions, the extent of this increase may depend on several environmental or genetic factors. Hypertension may increase arterial stiffness, especially in older subjects. Among other cardiovascular risk factors, diabetes type 1 and 2 accelerates arterial stiffness, whereas the role of dyslipidemia and tobacco smoking is unclear. Arterial stiffness is also present in several cardiovascular and renal diseases. Patients with heart failure, end stage renal disease, and those with atherosclerotic lesions often develop central artery stiffness. Decreased carotid distensibility, increased arterial thickness, and presence of calcifications and plaques often coexist in the same subject. However, relationships between these three alterations of the arterial wall remain to be explored.

Fishery-induced selection on an Alpine whitefish: quantifying genetic and environmental effects on individual growth rate

Size-selective fishing, environmental changes and reproductive strategies are expected to affect life-history traits such as the individual growth rate. The relative contribution of these factors is not clear, particularly whether size-selective fishing can have a substantial impact on the genetics and hence on the evolution of individual growth rates in wild populations. We analysed a 25-year monitoring survey of an isolated population of the Alpine whitefish Coregonus palaea. We determined the selection differentials on growth rate, the actual change of growth rate over time and indicators of reproductive strategies that may potentially change over time. The selection differential can be reliably estimated in our study population because almost all the fish are harvested within their first years of life, i.e. few fish escape fishing mortality. We found a marked decline in average adult growth rate over the 25 years and a significant selection differential for adult growth, but no evidence for any linear change in reproductive strategies over time. Assuming that the heritability of growth in this whitefish corresponds to what was found in other salmonids, about a third of the observed decline in growth rate would be linked to fishery-induced evolution. Size-selective fishing seems to affect substantially the genetics of individual growth in our study population.

Use of flow cytometric methods for single-cell analysis in environmental microbiology

Flow cytometry (FCM) is emerging as an important tool in environmental microbiology. Although flow cytometry applications have to date largely been restricted to certain specialized fields of microbiology, such as the bacterial cell cycle and marine phytoplankton communities, technical advances in instrumentation and methodology are leading to its increased popularity and extending its range of applications. Here we will focus on a number of recent flow cytometry developments important for addressing questions in environmental microbiology. These include (i) the study of microbial physiology under environmentally relevant conditions, (ii) new methods to identify active microbial populations and to isolate previously uncultured microorganisms, and (iii) the development of high-throughput autofluorescence bioreporter assays

Whole-cell living biosensors--are they ready for environmental application?

Since the development of the first whole-cell living biosensor or bioreporter about 15 years ago, construction and testing of new genetically modified microorganisms for environmental sensing and reporting has proceeded at an ever increasing rate. One and a half decades appear as a reasonable time span for a new technology to reach the maturity needed for application and commercial success. It seems, however, that the research into cellular biosensors is still mostly in a proof-of-principle or demonstration phase and not close to extensive or commercial use outside of academia. In this review, we consider the motivations for bioreporter developments and discuss the suitability of extant bioreporters for the proposed applications to stimulate complementary research and to help researchers to develop realistic objectives. This includes the identification of some popular misconceptions about the qualities and shortcomings of bioreporters.