Stochastic Modeling

A Thorough Guide to Evaluate, Pre-Process, Model and Compare Time Series with MATLAB Software
Erscheinungsjahr 2022
ISBN: 978-0-323-91748-3
Verlag: William Andrew Publishing

Buch, Englisch, 366 Seiten, Format (B × H): 191 mm x 235 mm, Gewicht: 450 g

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.

Zielgruppe

<p>Environments</p> <p>Soil science</p> <p>Water engineering, hydrology, statistics</p>

Autoren/Hrsg.

Bonakdari, Hossein

Zeynoddin, Mohammad

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik Stochastische Prozesse
• Geowissenschaften Umweltwissenschaften Umweltpolitik, Umweltprotokoll
• Geowissenschaften Umweltwissenschaften Umweltwissenschaften
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Mathematische Modellierung
• Wirtschaftswissenschaften Volkswirtschaftslehre Umweltökonomie

Inhaltsverzeichnis

1. Introduction
2. Preparation and Stationarizing
3. Distribution evaluation and Normalization
4. Stochastic Modeling
5. Goodness-Of-Fit and Precision Criteria
Appendix
MATLAB introduction and basic commands
Introduction
How to execute commands in MATLAB: Frequently used commands
Using MATLAB's help

Über Autor(innen)

Bonakdari, Hossein
Prof. Hossein Bonakdari obtained his PhD in civil engineering from the University of Caen Normandy, France. He has worked for several organizations, most recently as Professor at the Department of Civil Engineering, University of Ottawa, Canada. He is one of the most influential scientists in the field of developing novel algorithms for solving practical problems through the decision-making abilities of artificial intelligence. His research also focuses on creating comprehensive methodologies in the areas of simulation modeling, optimization, and machine learning algorithms. The results obtained from his research have been published in international journals and presented at international conferences. He was included in the list of the world's top 2% scientists, published by Stanford University, and is on the editorial board of several journals.

Zeynoddin, Mohammad
Mohammad Zeynoddin is currently Ph.D. candidate in the field of Soil and Environments at Department of Soils and Agri-Food Engineering, Laval University, Québec, Canada. He holds Master of Water Engineering and Hydraulic Structure and Bachelor of Civil Engineering diploma.

His research has primarily been focused on time series modeling to improve the accuracy of calculations of hydrological variables for monitoring, real time prediction, optimization, and automation of hydrological and environmental systems. Results of his research was 12 published papers in international journals with high Impact Factors. He received several awards and honors from universities during of his Master and PhD studies. He has a passion for art and sports. He holds several international sport certificates and championships.