This book begins by exploring the fundamental concepts of dynamical systems and machine learning modeling, elucidating the workflow of these two modeling approaches. While primarily tailored as an introductory textbook for both undergraduate and graduate students, its broader aim is to captivate the interest of seasoned ecologists and life scientists, beckoning them to explore the realm of modeling. The introduction and development of each section adhere to a practical problem-driven approach, aiming to address real-world issues. The focus is on addressing how to establish and evolve appropriate models based on practical problems or data.

Throughout the book, the authors deliver rich content and diverse models. A detailed overview of the workflow for both machine learning and dynamical system modeling is provided, covering topics such as stability and bifurcation theory, fundamentals of machine learning algorithms, data processing, and visualization methods. Regarding dynamical systems, the authors encompass various types of models, including delay, diffusion, continuous, and discrete models. For machine learning, both black-box and interpretable models are covered in this book, including neural network model, ensemble learning model, SHAP, LIME, and more.

Ecologists, life scientists, and applied mathematicians might find this book helpful. It can be also used as a textbook for both undergraduate and graduate students.

This book is related to SDG 15: Life on Land

Provides a unified and coherent account of the methods through dynamical systems and machine learning Provides the reader with the tools to construct and interpret models in the fields of ecology and life sciences Includes specific theory and application of the models and methods described in the book

Autorentext

Jingli Ren is a Professor of Applied Mathematics at Zhengzhou University, and serves as the Deputy Dean of the School of Mathematics and Statistics & Henan Academy of Big Data. She received the Ph.D. degree in applied mathematics from Beijing Institute of Technology, Beijing, China, in 2004. Her research interests include data science, applied mathematics, and applied statistics.

Yiwen Tao is an Associate Professor of Applied Mathematics at Zhengzhou University. She received her Ph.D. degree in applied mathematics from Zhengzhou University, Zhengzhou, China, in 2021. She has been a visiting scholar at University of Waterloo and College of William & Mary. Her research interests are in the field of mathematical biology and data science.

Inhalt

1. Introduction to Dynamical Systems.- 2. Introduce of Machine Learning.- 3. Ecological Modeling with Nonlocal Delay.- 4. Physiological Modeling with Dynamic Systems.- 5. Machine Learning in Clinical Medicine.- 6. Machine Learning in Drug discovery.- 7. Machine Learning in Ecology.-8. Spatiotemporal Environmental Health Modelling.