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Noise-Induced Phenomena in the Environmental Sciences
Randomness is ubiquitous in nature. Random drivers are generally considered a source of disorder in environmental systems. However, the interaction between noise and nonlinear dynamics may lead to the emergence of a number of ordered behaviors (in time and space) that would not exist in the absence of noise. This counterintuitive effect of randomness may play a crucial role in environmental processes. For example, seemingly 'random' background events in the atmosphere can grow into larger instabilities that have great effects on weather patterns. This book presents the basics of the theory of stochastic calculus and its application to the study of noise-induced phenomena in environmental systems. It will be an invaluable reference text for ecologists, geoscientists and environmental engineers interested in the study of stochastic environmental dynamics.
eBook
Environmental Data Analysis
Most environmental data involve a large degree of complexity and uncertainty. Environmental Data Analysis is created to provide modern quantitative tools and techniques designed specifically to meet the needs of environmental sciences and related fields. This book has an impressive coverage of the scope. Main techniques described in this book are models for linear and nonlinear environmental systems, statistical & numerical methods, data envelopment analysis, risk assessments and life cycle assessments. These state-of-the-art techniques have attracted significant attention over the past decades in environmental monitoring, modeling and decision making. Environmental Data Analysis explains carefully various data analysis procedures and techniques in a clear, concise, and straightforward language and is written in a self-contained way that is accessible to researchers and advanced students in science and engineering. This is an excellent reference for scientists and engineers who wish to analyze, interpret and model data from various sources, and is also an ideal graduate-level textbook for courses in environmental sciences and related fields.
Contents:
Preface
Time series analysis
Chaos and dynamical systems
Approximation
Interpolation
Statistical methods
Numerical methods
Optimization
Data envelopment analysis
Risk assessments
Life cycle assessments
Index
eBook
Numerical ecology
The book describes and discusses the numerical methods which are successfully being used for analysing ecological data, using a clear and comprehensive approach. These methods are derived from the fields of mathematical physics, parametric and nonparametric statistics, information theory, numerical taxonomy, archaeology, psychometry, sociometry, econometry and others. An updated, 3rd English edition of the most widely cited book on quantitative analysis of multivariate ecological dataRelates ecological questions to methods of statistical analysis, with a clear description of complex numerical methodsAll methods are illustrated by examples from the ecological literature so that ecologists clearly see how to use the methods and approaches in their own researchAll calculations are available in R language functions
eBook
Estimating water budget in a regional aquifer using HSPF-modflow integrated model
Integrated water resources management is important, especially in watersheds where substantial interactions exist between the ground and surface water sources. This management warrants the need for reliable estimates of both an overall basin water budget and hydrologic fluctuations between ground water and surface water sources. The objectives of this study were to estimate the total water budget and to simulate the effects of the management of water in the Big Lost River Basin in Idaho. The study used the FIPR Hydrological Model (FHM), a hydrological model developed by the University of South Florida for the Florida Institute of Phosphate Research (FIPR). The FHM is an integrated model that simulates the full water budget of the surface and ground water systems. It has two public domain components: Hydrological Simulation Program - FORTRAN (HSPF) and Modular Three-Dimensional Finite-Difference Ground Water Flow Model (MODFLOW). This study quantified the hydrologic fluxes between ground water and surface water and determined a comprehensive and accurate water budget for the Big Lost River. The study showed an annual amount of 10.44 m3/sec leaves the basin and never to return to the system. The study is useful in developing and calculating the annual water budget in the Big Lost River, and this process should be applicable to estimating water budgets in other basins.
Journal Article
A computational framework to systematize uncertainty analysis in the sediment fingerprinting approach using least square methods
Simulating sediment transfer processes in catchments has contributed significantly to solving environmental problems due to its importance in the silting of rivers and reservoirs and for controlling the pollution of water bodies. Among the methods used to improve data collection and modelling, the “sediment fingerprinting approach” uses tracers reflecting the composition of eroded soils and sediments in multivariate statistical analyses and mathematical models for optimizing equation systems. Based on generalized least squares (GLS) method and Mahalanobis distance, this study sought to present a computational framework to solve over-determined systems applied to sediment tracing, systematize the uncertainty analysis and sample number optimization. Hence, this approach takes into account the influence of collinearity among the chemical variables that compose the tracer set to be evaluated by the presence of the variance-covariance matrix. A dataset from the Arvorezinha experimental catchment in southern Brazil was used to validate the modeling, and our findings confirmed the assumption of increased uncertainty as the number of target samples decreases in the sources or eroded sediment samples. Sharing the code files with the PySASF (Python package for Source Apportionment with Sediment Fingerprinting) contributes to improving the technique as it allows other researchers to systematically improve the definition of the number of samples required based on the uncertainty analysis.
Journal Article
A neural network-based framework for financial model calibration
A data-driven approach called CaNN (Calibration Neural Network) is proposed to calibrate financial asset price models using an Artificial Neural Network (ANN). Determining optimal values of the model parameters is formulated as training hidden neurons within a machine learning framework, based on available financial option prices. The framework consists of two parts: a forward pass in which we train the weights of the ANN off-line, valuing options under many different asset model parameter settings; and a backward pass, in which we evaluate the trained ANN-solver on-line, aiming to find the weights of the neurons in the input layer. The rapid on-line learning of implied volatility by ANNs, in combination with the use of an adapted parallel global optimization method, tackles the computation bottleneck and provides a fast and reliable technique for calibrating model parameters while avoiding, as much as possible, getting stuck in local minima. Numerical experiments confirm that this machine-learning framework can be employed to calibrate parameters of high-dimensional stochastic volatility models efficiently and accurately.
Journal Article
Pre-service teachers’ preparations for designing and implementing creativity-directed mathematical tasks and instructional practices
The purpose of this study is to observe the changes on pre-service teachers’ cognitive (i.e. fluency, flexibility, and originality) and affective outcomes (e.g. perceptions towards creativity-directed tasks, beliefs about the nature of mathematics) related to mathematical creativity after they participated in a creativity-focused mathematics method course. The pre-service teachers (n = 40) were randomly assigned into two groups as the intervention (n = 21) and the control (n = 19). The results revealed that the pre-service teachers who received the intervention developed all of the cognitive and affective outcomes more than the pre-service teachers in the control group (p < .05). The implication of these findings is that integrating creativity-directed tasks into mathematics education college courses can better equip pre-service teachers to develop mathematical creative abilities in their future students.
Journal Article