Machine Learning Methods for Ecological Applications

This is the first text aimed at introducing machine learning methods to a readership of professional ecologists.

Machine Learning Methods for Ecological Applications

This is the first text aimed at introducing machine learning methods to a readership of professional ecologists. All but one of the chapters have been written by ecologists and biologists who highlight the application of a particular method to a particular class of problem.

Machine Learning Methods for Ecological Applications

I began to notice an increasing use of machine learning methods and I became more interested in their potential for ecological applications. I had maintained some contacts at York, in particular with David Morse and Marion Edwards.

Machine Learning Methods for Ecological Applications

This is the first text aimed at introducing machine learning methods to a readership of professional ecologists. All but one of the chapters have been written by ecologists and biologists who highlight the application of a particular method to a particular class of problem.

Artificial Intelligence Methods in the Environmental Sciences

However, spatially aware machine learning methods have recently started to emerge (Lee et al. 2005, Andrienko et al. 2005), although applications of such methods in habitat modeling are still rare. Finally, let us mention climate change ...

Artificial Intelligence Methods in the Environmental Sciences

How can environmental scientists and engineers use the increasing amount of available data to enhance our understanding of planet Earth, its systems and processes? This book describes various potential approaches based on artificial intelligence (AI) techniques, including neural networks, decision trees, genetic algorithms and fuzzy logic. Part I contains a series of tutorials describing the methods and the important considerations in applying them. In Part II, many practical examples illustrate the power of these techniques on actual environmental problems. International experts bring to life ways to apply AI to problems in the environmental sciences. While one culture entwines ideas with a thread, another links them with a red line. Thus, a “red thread“ ties the book together, weaving a tapestry that pictures the ‘natural’ data-driven AI methods in the light of the more traditional modeling techniques, and demonstrating the power of these data-based methods.

Encyclopedia of Ecology

Machine Learning Methods for Ecological Applications, pp. 89–105. Dordrecht, The Netherlands: Kluwer Academic Publishers. Breiman L, Friedman J, Olshen R, and Stone C (1984) Classification and Regression Trees. Belmont, CA: Wadsworth.

Encyclopedia of Ecology

The groundbreaking Encyclopedia of Ecology provides an authoritative and comprehensive coverage of the complete field of ecology, from general to applied. It includes over 500 detailed entries, structured to provide the user with complete coverage of the core knowledge, accessed as intuitively as possible, and heavily cross-referenced. Written by an international team of leading experts, this revolutionary encyclopedia will serve as a one-stop-shop to concise, stand-alone articles to be used as a point of entry for undergraduate students, or as a tool for active researchers looking for the latest information in the field. Entries cover a range of topics, including: Behavioral Ecology Ecological Processes Ecological Modeling Ecological Engineering Ecological Indicators Ecological Informatics Ecosystems Ecotoxicology Evolutionary Ecology General Ecology Global Ecology Human Ecology System Ecology The first reference work to cover all aspects of ecology, from basic to applied Over 500 concise, stand-alone articles are written by prominent leaders in the field Article text is supported by full-color photos, drawings, tables, and other visual material Fully indexed and cross referenced with detailed references for further study Writing level is suited to both the expert and non-expert Available electronically on ScienceDirect shortly upon publication

Machine Learning Applications

thodology is splitting the data, escaping the necessity to execute algorithms on enormous datasets. The idea of distributive machine ... Machine Learning Methods for Ecological Applications. Springer Science & Business Media. 3.

Machine Learning Applications

The publication is attempted to address emerging trends in machine learning applications. Recent trends in information identification have identified huge scope in applying machine learning techniques for gaining meaningful insights. Random growth of unstructured data poses new research challenges to handle this huge source of information. Efficient designing of machine learning techniques is the need of the hour. Recent literature in machine learning has emphasized on single technique of information identification. Huge scope exists in developing hybrid machine learning models with reduced computational complexity for enhanced accuracy of information identification. This book will focus on techniques to reduce feature dimension for designing light weight techniques for real time identification and decision fusion. Key Findings of the book will be the use of machine learning in daily lives and the applications of it to improve livelihood. However, it will not be able to cover the entire domain in machine learning in its limited scope. This book is going to benefit the research scholars, entrepreneurs and interdisciplinary approaches to find new ways of applications in machine learning and thus will have novel research contributions. The lightweight techniques can be well used in real time which will add value to practice.

Machine Learning for Ecology and Sustainable Natural Resource Management

IGI Global, Hershey, pp 65–83 Crisci C, Ghattas B, Perera G (2012) A review of supervised machine learning algorithms and their applications to ecological data. Ecol Model 240:113–122 Cushman SA, Huettmann F (2010) Spatial complexity, ...

Machine Learning for Ecology and Sustainable Natural Resource Management

Ecologists and natural resource managers are charged with making complex management decisions in the face of a rapidly changing environment resulting from climate change, energy development, urban sprawl, invasive species and globalization. Advances in Geographic Information System (GIS) technology, digitization, online data availability, historic legacy datasets, remote sensors and the ability to collect data on animal movements via satellite and GPS have given rise to large, highly complex datasets. These datasets could be utilized for making critical management decisions, but are often “messy” and difficult to interpret. Basic artificial intelligence algorithms (i.e., machine learning) are powerful tools that are shaping the world and must be taken advantage of in the life sciences. In ecology, machine learning algorithms are critical to helping resource managers synthesize information to better understand complex ecological systems. Machine Learning has a wide variety of powerful applications, with three general uses that are of particular interest to ecologists: (1) data exploration to gain system knowledge and generate new hypotheses, (2) predicting ecological patterns in space and time, and (3) pattern recognition for ecological sampling. Machine learning can be used to make predictive assessments even when relationships between variables are poorly understood. When traditional techniques fail to capture the relationship between variables, effective use of machine learning can unearth and capture previously unattainable insights into an ecosystem's complexity. Currently, many ecologists do not utilize machine learning as a part of the scientific process. This volume highlights how machine learning techniques can complement the traditional methodologies currently applied in this field.

Machine Learning ECML 2001

12th European Conference on Machine Learning, Freiburg, Germany, September 5-7, 2001. Proceedings Luc de Raedt, Peter Flach ... In A. H. Fielding, editor, Machine learning methods for ecological applications, pages 185–207.

Machine Learning  ECML 2001

This book constitutes the refereed proceedings of the 12th European Conference on Machine Learning, ECML 2001, held in Freiburg, Germany, in September 2001. The 50 revised full papers presented together with four invited contributions were carefully reviewed and selected from a total of 140 submissions. Among the topics covered are classifier systems, naive-Bayes classification, rule learning, decision tree-based classification, Web mining, equation discovery, inductive logic programming, text categorization, agent learning, backpropagation, reinforcement learning, sequence prediction, sequential decisions, classification learning, sampling, and semi-supervised learning.

Machine Learning in Radiation Oncology

Fielding A. Machine learning methods for ecological applications. Boston: Kluwer Academic Publishers; 1999. Mitra S. Introduction to machine learning and bioinformatics. Boca Raton: CRC Press; 2008. Yang ZR. Machine learning approaches ...

Machine Learning in Radiation Oncology

​This book provides a complete overview of the role of machine learning in radiation oncology and medical physics, covering basic theory, methods, and a variety of applications in medical physics and radiotherapy. An introductory section explains machine learning, reviews supervised and unsupervised learning methods, discusses performance evaluation, and summarizes potential applications in radiation oncology. Detailed individual sections are then devoted to the use of machine learning in quality assurance; computer-aided detection, including treatment planning and contouring; image-guided radiotherapy; respiratory motion management; and treatment response modeling and outcome prediction. The book will be invaluable for students and residents in medical physics and radiation oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.

Machine Learning Methods in the Environmental Sciences

This is the first single-authored textbook to give a unified treatment of machine learning methods and their applications in the environmental sciences. Machine learning methods began to infiltrate the environmental sciences in the 1990s.

Machine Learning Methods in the Environmental Sciences

Machine learning methods originated from artificial intelligence and are now used in various fields in environmental sciences today. This is the first single-authored textbook providing a unified treatment of machine learning methods and their applications in the environmental sciences. Due to their powerful nonlinear modeling capability, machine learning methods today are used in satellite data processing, general circulation models(GCM), weather and climate prediction, air quality forecasting, analysis and modeling of environmental data, oceanographic and hydrological forecasting, ecological modeling, and monitoring of snow, ice and forests. The book includes end-of-chapter review questions and an appendix listing web sites for downloading computer code and data sources. A resources website containing datasets for exercises, and password-protected solutions are available. The book is suitable for first-year graduate students and advanced undergraduates. It is also valuable for researchers and practitioners in environmental sciences interested in applying these new methods to their own work. Preface Excerpt Machine learning is a major subfield in computational intelligence (also called artificial intelligence). Its main objective is to use computational methods to extract information from data. Neural network methods, generally regarded as forming the first wave of breakthrough in machine learning, became popular in the late 1980s, while kernel methods arrived in a second wave in the second half of the 1990s. This is the first single-authored textbook to give a unified treatment of machine learning methods and their applications in the environmental sciences. Machine learning methods began to infiltrate the environmental sciences in the 1990s. Today, thanks to their powerful nonlinear modeling capability, they are no longer an exotic fringe species, as they are heavily used in satellite data processing, in general circulation models (GCM), in weather and climate prediction, air quality forecasting, analysis and modeling of environmental data, oceanographic and hydrological forecasting, ecological modeling, and in the monitoring of snow, ice and forests, etc. This book presents machine learning methods and their applications in the environmental sciences (including satellite remote sensing, atmospheric science, climate science, oceanography, hydrology and ecology), written at a level suitable for beginning graduate students and advanced undergraduates. It is also valuable for researchers and practitioners in environmental sciences interested in applying these new methods to their own work. Chapters 1-3, intended mainly as background material for students, cover the standard statistical methods used in environmental sciences. The machine learning methods of chapters 4-12 provide powerful nonlinear generalizations for many of these standard linear statistical methods. End-of-chapter review questions are included, allowing readers to develop their problem-solving skills and monitor their understanding of the material presented. An appendix lists websites available for downloading computer code and data sources. A resources website is available containing datasets for exercises, and additional material to keep the book completely up-to-date. About the Author WILLIAM W. HSIEH is a Professor in the Department of Earth and Ocean Sciences and in the Department of Physics and Astronomy, as well as Chair of the Atmospheric Science Programme, at the University of British Columbia. He is internationally known for his pioneering work in developing and applying machine learning methods in environmental sciences. He has published over 80 peer-reviewed journal publications covering areas of climate variability, machine learning, oceanography, atmospheric science and hydrology.

Computational Intelligence in Intelligent Data Analysis

Journal of Ecology 78(2), 519–534 (1990) Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: The kdd process for extracting useful knowledge from volumes of data. Commun. ... Machine Learning Methods for Ecological Applications, pp. 1–35.

Computational Intelligence in Intelligent Data Analysis

Complex systems and their phenomena are ubiquitous as they can be found in biology, finance, the humanities, management sciences, medicine, physics and similar fields. For many problems in these fields, there are no conventional ways to mathematically or analytically solve them completely at low cost. On the other hand, nature already solved many optimization problems efficiently. Computational intelligence attempts to mimic nature-inspired problem-solving strategies and methods. These strategies can be used to study, model and analyze complex systems such that it becomes feasible to handle them. Key areas of computational intelligence are artificial neural networks, evolutionary computation and fuzzy systems. As only a few researchers in that field, Rudolf Kruse has contributed in many important ways to the understanding, modeling and application of computational intelligence methods. On occasion of his 60th birthday, a collection of original papers of leading researchers in the field of computational intelligence has been collected in this volume.

Machine and Deep Learning in Oncology Medical Physics and Radiology

Yu J, Tao D. Modern machine learning techniques and their applications in cartoon animation research. 1st ed. Hoboken: Wiley; 2013. 10. Fielding A. Machine learning methods for ecological applications. Boston: Kluwer Academic; 1999. 11.

Machine and Deep Learning in Oncology  Medical Physics and Radiology

This book, now in an extensively revised and updated second edition, provides a comprehensive overview of both machine learning and deep learning and their role in oncology, medical physics, and radiology. Readers will find thorough coverage of basic theory, methods, and demonstrative applications in these fields. An introductory section explains machine and deep learning, reviews learning methods, discusses performance evaluation, and examines software tools and data protection. Detailed individual sections are then devoted to the use of machine and deep learning for medical image analysis, treatment planning and delivery, and outcomes modeling and decision support. Resources for varying applications are provided in each chapter, and software code is embedded as appropriate for illustrative purposes. The book will be invaluable for students and residents in medical physics, radiology, and oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.

Integrated Water Management

El-Din, A. G., Smith, D. W., and El-Din, M. G., 2004, Application of artificial neural networks in wastewater treatment, J. Environ. Eng. Sci. ... Fielding, A. H., ed., 1999, Machine Learning Methods for Ecological Applications, ...

Integrated Water Management

Integrated Water Management (IWM) deals with the planning and management of water resources by integrating the different issues involved, including ecological, economic, technical legislative, and transboundary. This book offers a general framework for IWM. It includes both the different environmental problems that affect the very different ecosystems and the main methodologies able to face the problem of IWM.

Advanced Modelling Techniques Studying Global Changes in Environmental Sciences

Ecology. 58, 551–561. Fielding, A. (Ed.), 1999. Machine Learning Methods for Ecological Applications. Springer Science & Business Media, New York. Friedel, M.J., 2012. Data-driven modeling of surface temperature anomaly and solar ...

Advanced Modelling Techniques Studying Global Changes in Environmental Sciences

Advanced Modelling Techniques Studying Global Changes in Environmental Sciences discusses the need for immediate and effective action, guided by a scientific understanding of ecosystem function, to alleviate current pressures on the environment. Research, especially in Ecological Modeling, is crucial to support the sustainable development paradigm, in which the economy, society, and the environment are integrated and positively reinforce each other. Content from this book is drawn from the 2013 conference of the International Society for Ecological Modeling (ISEM), an important and active research community contributing to this arena. Some progress towards gaining a better understanding of the processes of global change has been achieved, but much more is needed. This conference provides a forum to present current research using models to investigate actions towards mitigating and adapting to change. Presents state-of-the-art modeling techniques Drawn from the 2013 conference of the International Society for Ecological Modeling (ISEM), an important and active research community contributing to this arena Integrates knowledge of advanced modeling techniques in ecological and environmental sciences Describes new applications for sustainability

Innovations and Advances in Computing Informatics Systems Sciences Networking and Engineering

This paper presented a comprehensive review of applications of various Machine Learning techniques to bioclimatic modelling and broadly to ecological modelling. Some of the statistical techniques popular in this application domain have ...

Innovations and Advances in Computing  Informatics  Systems Sciences  Networking and Engineering

Innovations and Advances in Computing, Informatics, Systems Sciences, Networking and Engineering This book includes a set of rigorously reviewed world-class manuscripts addressing and detailing state-of-the-art research projects in the areas of Computer Science, Informatics, and Systems Sciences, and Engineering. It includes selected papers from the conference proceedings of the Eighth and some selected papers of the Ninth International Joint Conferences on Computer, Information, and Systems Sciences, and Engineering (CISSE 2012 & CISSE 2013). Coverage includes topics in: Industrial Electronics, Technology & Automation, Telecommunications and Networking, Systems, Computing Sciences and Software Engineering, Engineering Education, Instructional Technology, Assessment, and E-learning. · Provides the latest in a series of books growing out of the International Joint Conferences on Computer, Information, and Systems Sciences, and Engineering; · Includes chapters in the most advanced areas of Computing, Informatics, Systems Sciences, and Engineering; · Accessible to a wide range of readership, including professors, researchers, practitioners and students.

Handbook of Ecological Modelling and Informatics

Džeroski, S., Todorovski, L., Bratko, I., Kompare, B. & Križman, V., Equation discovery with ecological applications. Machine Learning Methods for Ecological Applications, ed. A.H. Fielding, Kluwer: Boston, pp. 185–207, 1999.

Handbook of Ecological Modelling and Informatics

The book gives a comprehensive overview of all available types of ecological models. It is the first book of its kind that gives an overview of different model types and will be of interest to all those involved in ecological and environmental modelling and ecological informatics.

Ecological Informatics

Ecosystems, 1:457-463 Jeffers JNR (1999) Genetic Algorithms I. In A. H. Fielding (editor) Machine Learning Methods for Ecological Applications. Kluwer Academic Press, Massachusetts, 261 pp Jorgensen SE (1999) State-of-the-art of ...

Ecological Informatics

Ecological Informatics is defined as the design and application of computational techniques for ecological analysis, synthesis, forecasting and management. The book provides an introduction to the scope, concepts and techniques of this newly emerging discipline. It illustrates numerous applications of Ecological Informatics for stream systems, river systems, freshwater lakes and marine systems as well as image recognition at micro and macro scale. Case studies focus on applications of artificial neural networks, genetic algorithms, fuzzy logic and adaptive agents to current ecological management issues such as toxic algal blooms, eutrophication, habitat degradation, conservation of biodiversity and sustainable fishery.

Relational Data Mining

Equation discovery with ecological applications. In A.H. Fielding, editor, Machine Learning Methods for Ecological Applications, pages 185–207. Kluwer, Boston, 1999. U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth.

Relational Data Mining

As the first book devoted to relational data mining, this coherently written multi-author monograph provides a thorough introduction and systematic overview of the area. The first part introduces the reader to the basics and principles of classical knowledge discovery in databases and inductive logic programming; subsequent chapters by leading experts assess the techniques in relational data mining in a principled and comprehensive way; finally, three chapters deal with advanced applications in various fields and refer the reader to resources for relational data mining. This book will become a valuable source of reference for R&D professionals active in relational data mining. Students as well as IT professionals and ambitioned practitioners interested in learning about relational data mining will appreciate the book as a useful text and gentle introduction to this exciting new field.

Ecological Modelling for Sustainable Development Penerbit USM

Machine Learning Methods for Ecological Applications. Massachusetts, USA: Kluwer, 1–262. Foody, G. (2000). Soft mapping of coastal vegetation from remotely sensed imagery with a feedforward neural network.

Ecological Modelling for Sustainable Development  Penerbit USM

In view of the current global scenario, which highlighted the importance of sustainable development and sustaining natural resources, the theme selected for the 2nd Regional ECOMOD 2007 Conference was indeed appropriate. This conference has generated overwhelming interest and I am sure the participants have focussed diligently on the serious issues concerning important environmental issues and steps needed to be taken towards a sustainable development and management of our natural resources and environment. As governments in the Asian region introduce new initiatives and development policies to rejuvenate and protect their environment and natural resources, it is imperative that universities and research institutions play a fundamental role in ensuring that the objectives of these policies are realized. Such institutions can complement government proposals by embarking on research that is relevant and valuable to the needs of respective nations and pursuing extensive research so that the outcome and technology generated can be transferred effectively to the end users. This concerted effort by all the researchers from different fields to improve and manage our natural resources should be lauded. I strongly believe that this conference is an extraordinary testimony to our capacity building at regional and local levels. I believe USM has something interesting to share with all of you in this area. Finally, on behalf of the Organizing Committee, I hope readers will find this book of proceedings useful, informative and stimulating.

Inductive Logic Programming

Integrating experimentation and guidance in relational reinforcement learning. In Proc. 19th International Conference on Machine Learning (pp. ... In A.H. Fielding, editor, Machine Learning Methods for Ecological Applications (pp.

Inductive Logic Programming

The Twelfth International Conference on Inductive Logic Programming was held in Sydney, Australia, July 9–11, 2002. The conference was colocated with two other events, the Nineteenth International Conference on Machine Learning (ICML2002) and the Fifteenth Annual Conference on Computational Learning Theory (COLT2002). Startedin1991,InductiveLogicProgrammingistheleadingannualforumfor researchers working in Inductive Logic Programming and Relational Learning. Continuing a series of international conferences devoted to Inductive Logic Programming and Relational Learning, ILP 2002 was the central event in 2002 for researchers interested in learning relational knowledge from examples. The Program Committee, following a resolution of the Community Me- ing in Strasbourg in September 2001, took upon itself the issue of the possible change of the name of the conference. Following an extended e-mail discussion, a number of proposed names were subjected to a vote. In the ?rst stage of the vote, two names were retained for the second vote. The two names were: Ind- tive Logic Programming, and Relational Learning. It had been decided that a 60% vote would be needed to change the name; the result of the vote was 57% in favor of the name Relational Learning. Consequently, the name Inductive Logic Programming was kept.

Building and Delivering Sustainability Solutions Insights Methods and Case Studies

... to use machine learning in combination with satellite imagery in a series of ecological applications in the past. ... They also compare the genetic programming methodology to state-of-the-art machine learning techniques in fire ...

Building and Delivering Sustainability Solutions  Insights  Methods  and Case Studies

Sustaining ecosystems to deliver what people need and value, while mitigating and adapting to global climate change and extreme event impacts, presents a complex set of environmental, economic, and social challenges in ensuring resilient and sustainable food production. The Climate Smart Landscape (CSL) approach has emerged as an integrated management strategy to address the increasing pressures on agricultural production, ecosystem conservation, rural livelihoods, climate change mitigation and adaptation. Deploying cheaper, more accurate, and efficient technology enables the harnessing of big data for use in solving sustainability challenges. With improved integrated analytical frameworks, statistical approaches, spatially- explicit models and indices, the CSL approach can be further developed and applied for more resilient, productive, and sustainable ecosystems. This eBook brings together original research, review, hypothesis, theory, and technology report articles, involving 87 authors from 9 countries across Asia, Europe, and North America. These articles present new methodological and technological innovation, findings, and insights across four themes: (1) landscape productivity and crop suitability, (2) variable crop requirements for water and nutrients, (3) crop health status, phenology, and phenotyping, and (4) crop disease assessment and prediction under integrated pest management (IPM).