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Machine Learning in Earth, Environmental and Planetary Sciences:
Theoretical and Practical Applications is a practical guide on
implementing different variety of extreme learning machine
algorithms to Earth and environmental data. The book provides
guided examples using real-world data for numerous novel and
mathematically detailed machine learning techniques that can be
applied in Earth, environmental, and planetary sciences, including
detailed MATLAB coding coupled with line-by-line descriptions of
the advantages and limitations of each method. The book also
presents common postprocessing techniques required for correct data
interpretation. This book provides students, academics, and
researchers with detailed understanding of how machine learning
algorithms can be applied to solve real case problems, how to
prepare data, and how to interpret the results.
Stochastic Modeling: A Thorough Guide to Evaluate, Pre-Process,
Model and Compare Time Series with MATLAB Software allows for new
avenues in time series analysis and predictive modeling which
summarize more than ten years of experience in the application of
stochastic models in environmental problems. The book introduces a
variety of different topics in time series in the modeling and
prediction of complex environmental systems. Most importantly, all
codes are user-friendly and readers will be able to use them for
their cases. Users who may not be familiar with MATLAB software can
also refer to the appendix. This book also guides the reader
step-by-step to learn developed codes for time series modeling,
provides required toolboxes, explains concepts, and applies
different tools for different types of environmental time series
problems.
Water Engineering Modeling and Mathematic Tools provides an
informative resource for practitioners who want to learn more about
different techniques and models in water engineering and their
practical applications and case studies. The book provides
modelling theories in an easy-to-read format verified with on-site
models for specific regions and scenarios. Users will find this to
be a significant contribution to the development of mathematical
tools, experimental techniques, and data-driven models that support
modern-day water engineering applications. Civil engineers,
industrialists, and water management experts should be familiar
with advanced techniques that can be used to improve existing
systems in water engineering. This book provides key ideas on
recently developed machine learning methods and AI modelling. It
will serve as a common platform for practitioners who need to
become familiar with the latest developments of computational
techniques in water engineering.
Making use of computational fluid dynamics and FLUENT finite volume
software, this study investigates the appropriate gridding pattern
of free surface flow by structured grids. Investigated gridding
space in each problem includes the determination of the shape of
grid cells, their compression and distribution pattern according to
users' preferences, which seems to be experimental rather than
scientific; however, a slight difference in gridding condition may
result in a great difference in numerical results. In this study,
an open channel is modeled with different three dimensional grids
and the results of model are compared by experimental results.
Modeling results show that modeling accuracy improves as the result
of a decrease in dimensions of grid cells to an extreme value;
therefore, decreasing the dimensions of grid cells and increasing
the number of cells lead to less accurate results. While the long
side of cross section in cell locates in horizontal direction,
using rectangular section cells represents more acceptable results
than that of other cells with different structures.
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