Author: Clara Antón Fernández
Publisher:
ISBN:
Category : Trees
Languages : en
Pages : 58

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Author: David A. Hamilton
Publisher:
ISBN:
Category : Log-linear models
Languages : en
Pages : 30

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Author: Biing Tzuang Guan
Publisher:
ISBN:
Category :
Languages : en
Pages :

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The main objective of this study is to seek new modeling techniques to improve the ability of projecting tree mortality in forest growth and yield simulation. Multi-layer feed-forward artificial neural networks (ANN) are adopted to achieve the goal. The premise of ANN modeling approach is the ability of such networks to approximate any measurable or continuous function to any desired degree of accuracy, given enough complexity and training. In this study, two types of tree mortality models are developed based on red pine (Pinus resinosa Ait.) data collected from the Great Lakes region. Diameter at breast height (DBH) and annual diameter growth (ADG) are the explanatory variables in both types of models, and annual survival rate is the response variable. For the first type of models, training set consists of data obtained based on a cross-classified scheme. For the second type of models, individual tree records are used to construct training set. Training method for the first type of models is the back-propagation method, and networks are trained on serial computers. A method based on the fast simulated annealing is used to train models of the second, and the trainings are performed on a massively parallel computer. In addition to several goodness-of-fit and performance statistics, a model-based comparison approach is also developed to assess the performance of ANN mortality models against a benchmark statistical model. Results from this dissertation suggest that ANN mortality models not only fit the training data better than the benchmark model, but also expect to perform better in the future, provided that the training set are representative. Model-based comparisons show that ANN mortality model in general have lower prediction biases, but with larger prediction variances, than the benchmark model. Mean squared error criterion suggests that ANN mortality models are expected to perform better in the future, provided the training data are representative. A brief review of modeling tree mortality in forestry growth and yield projection, as well as an overview of neural computing approach, is also presented in this study. Other issues related to the use of artificial neural networks in forestry related modeling are also discussed.

Author: Elizabeth D. Reinhardt
Publisher:
ISBN:
Category : Fire ecology
Languages : en
Pages : 72

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A First Order Fire Effects Model (FOFEM) was developed to predict the direct consequences of prescribed fire and wildfire. FOFEM computes duff and woody fuel consumption, smoke production, and fire-caused tree mortality for most forest and rangeland types in the United States. The model is available as a computer program for PC or Data General computer.

Author: Annikki Mäkelä
Publisher: Springer Nature
ISBN: 3030357619
Category : Science
Languages : en
Pages : 320

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Book Description
The book is designed to be a textbook for university students (MSc-PhD level) and a reference for researchers and practitioners. It is an introduction to dynamic modelling of forest growth based on ecological theory but aiming for practical applications for forest management under environmental change. It is largely based on the work and research findings of the authors, but it also covers a wide range of literature relevant to process-based forest modelling in general. The models presented in the book also serve as tools for research and can be elaborated further as new research findings emerge. The material in the book is arranged such that the student starts from basic concepts and formulations, then moves towards more advanced theories and methods, finally learning about parameter estimation, model testing, and practical application. Exercises with solutions and hands-on R-code are provided to help the student digest the concepts and become proficient with the methods. The book should be useful for both forest ecologists who want to become modellers, and for applied mathematicians who want to learn about forest ecology. The basic concepts and theory are formulated in the first four chapters, including a review of traditional descriptive forest models, basic concepts of carbon balance modelling applied to trees, and theories and models of tree and forest structure. Chapter 5 provides a synthesis in the form of a core model which is further elaborated and applied in the subsequent chapters. The more advanced theories and methods in Chapters 6 and 7 comprise aspects of competition through tree interactions, and eco-evolutionary modelling, including optimisation and game theory, a topical and fast developing area of ecological modelling under climate change. Chapters 8 and 9 are devoted to parameter estimation and model calibration, showing how empirical and process-based methods and related data sources can be bridged to provide reliable predictions. Chapter 10 demonstrates some practical applications and possible future development paths of the approach. The approach in this book is unique in that the models presented are based on ecological theory and research findings, yet sufficiently simple in structure to lend themselves readily to practical application, such as regional estimates of harvest potential, or satellite-based monitoring of growth. The applicability is also related to the objective of bridging empirical and process-based approaches through data assimilation methods that combine research-based ecological measurements with standard forestry data. Importantly, the ecological basis means that it is possible to build on the existing models to advance the approach as new research findings become available.

Author: David Alexander Hamilton Jr.
Publisher: Forgotten Books
ISBN: 9781390423839
Category : Mathematics
Languages : en
Pages : 34

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Excerpt from Modeling the Probability of Individual Tree Mortality The mortality model can be used to help schedule management activities. Stands of trees for which the model predicts high mortality rates should be considered high risk stands susceptible to insect and disease outbreaks and should be logged, salvaged, or otherwise treated before stands composed of trees with low predicted mortality rates. Mortality models also reveal relationships that may provide a better understanding of how and why mortality occurs in a stand. This information is valuable in management planning and in developing strategies to control mortality. The models discussed in this paper were developed by means of a three - step analy sis. The first step is to screen the independent variables for those that show the strongest relationships to the probability of mortality. The variables selected as most significant by the screening algorithm are used as predictors of the probability of mortality in a logistic model. The regression coefficients for this model are then estimated by a nonlinear regression program, risk (hamilton The final step, model verification, employs a chi-square statistic to evaluate the accuracy with which the proposed model predicts the mortality that occurred on an independent data set. About the Publisher Forgotten Books publishes hundreds of thousands of rare and classic books. Find more at www.forgottenbooks.com This book is a reproduction of an important historical work. Forgotten Books uses state-of-the-art technology to digitally reconstruct the work, preserving the original format whilst repairing imperfections present in the aged copy. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in our edition. We do, however, repair the vast majority of imperfections successfully; any imperfections that remain are intentionally left to preserve the state of such historical works.

Author: Albert R. Stage
Publisher:
ISBN:
Category : Lodgepole pine
Languages : en
Pages : 42

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Author: MIT Critical Data
Publisher: Springer
ISBN: 3319437429
Category : Medical
Languages : en
Pages : 435

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This book trains the next generation of scientists representing different disciplines to leverage the data generated during routine patient care. It formulates a more complete lexicon of evidence-based recommendations and support shared, ethical decision making by doctors with their patients. Diagnostic and therapeutic technologies continue to evolve rapidly, and both individual practitioners and clinical teams face increasingly complex ethical decisions. Unfortunately, the current state of medical knowledge does not provide the guidance to make the majority of clinical decisions on the basis of evidence. The present research infrastructure is inefficient and frequently produces unreliable results that cannot be replicated. Even randomized controlled trials (RCTs), the traditional gold standards of the research reliability hierarchy, are not without limitations. They can be costly, labor intensive, and slow, and can return results that are seldom generalizable to every patient population. Furthermore, many pertinent but unresolved clinical and medical systems issues do not seem to have attracted the interest of the research enterprise, which has come to focus instead on cellular and molecular investigations and single-agent (e.g., a drug or device) effects. For clinicians, the end result is a bit of a “data desert” when it comes to making decisions. The new research infrastructure proposed in this book will help the medical profession to make ethically sound and well informed decisions for their patients.

Author: Aaron R. Weiskittel
Publisher: John Wiley & Sons
ISBN: 1119971500
Category : Science
Languages : en
Pages : 431

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Forest Growth and Yield Modeling synthesizes current scientific literature and provides insights in how models are constructed. Giving suggestions for future developments, and outlining keys for successful implementation of models the book provides a thorough and up-to-date, single source reference for students, researchers and practitioners requiring a current digest of research and methods in the field. The book describes current modelling approaches for predicting forest growth and yield and explores the components that comprise the various modelling approaches. It provides the reader with the tools for evaluating and calibrating growth and yield models and outlines the steps necessary for developing a forest growth and yield model. Single source reference providing an evaluation and synthesis of current scientific literature Detailed descriptions of example models Covers statistical techniques used in forest model construction Accessible, reader-friendly style

Author: Albert R. Stage
Publisher:
ISBN:
Category : Trees
Languages : en
Pages : 32

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