Data Analytics and Artificial Intelligence for Earth Resource Management
◆日本地質学会 第131年学術大会 特別割引セール開催中!:2024年10月18日(金)ご注文分まで
※上記表示の販売価格は割引適用後の価格です 未刊 ご予約承ります。 Author: Kumar, Deepak (Research Scientist Center Of Excellence in Weather & Climate Analytics,Atmospheric Sciences Research Center (ASRC) & Amity Institute of Geoinformatics and Remote Sensing (AIGIRS)University at Albany, State University of New York, NY USA) / Publisher: Elsevier USA ISBN: 9780443235955 Cover: PAPERBACK Date: 2024年11月 DESCRIPTION Data Analytics and Artificial Intelligence for Earth Resource Management offers a detailed look at the different ways data analytics and artificial intelligence can help organizations make better-informed decisions, improve operations, and minimize the negative impacts of resource extraction on the environment. The book explains several different ways data analytics and artificial intelligence can improve and support earth resource management. Predictive modeling can help organizations understand the impacts of different management decisions on earth resources, such as water availability, land use, and biodiversity. Resource monitoring tracks the state of earth resources in real-time, identifying issues and opportunities for improvement. Providing managers with real-time data and analytics allows them to make more informed choices. Optimizing resource management decisions help to identify the most efficient and effective ways to allocate resources. Predictive maintenance allows organizations to anticipate when equipment might fail and take action to prevent it, reducing downtime and maintenance costs. Remote sensing with image processing and analysis can be used to extract information from satellite images and other remote sensing data, providing valuable information on land use, water resources, and other earth resources. TABLE OF CONTENTS 1:Introduction to Data Analytics and Artificial Intelligence in Earth Resource Management 2:Basics of Earth Resource Management 1.Overview of Earth Resources 2.Resource Management Techniques 3.Challenges in Earth Resource Management 3:Data Analytics for Earth Resource Management 1.Data Analytics Concepts 2.Applications of Data Analytics in Earth Resource Management 3.Challenges and Limitations of Data Analytics in Earth Resource Management 4:Artificial Intelligence for Earth Resource Management 1.AI Concepts 2.Applications of AI in Earth Resource Management 3.Challenges and Limitations of AI in Earth Resource Management 5:Data Preprocessing Techniques 1.Data Collection and Integration 2.Data Cleaning 3.Data Transformation 4.Data Reduction 6:Analytics for Earth Resource Management 1.Predictive Modeling Techniques 2.Time Series Analysis 3.Regression Analysis 4.Forecasting 7:Machine Learning for Earth Resource Management 1.Introduction to Machine Learning 2.Types of Machine Learning 3.Supervised Learning Techniques 4.Unsupervised Learning Techniques 8:Deep Learning for Earth Resource Management 1.Introduction to Deep Learning 2.Neural Networks 3.Convolutional Neural Networks 4.Recurrent Neural Networks 9:Natural Language Processing for Earth Resource Management 1.Introduction to Natural Language Processing 2.Text Mining 3.Sentiment Analysis 4.Language Translation 10:Remote Sensing and Geographic Information System for Earth Resource Management 1.Overview of Remote Sensing and GIS 2.Introduction to Remote Sensing Techniques 3.Spatial Data Management and Spatial Analysis 4.Applications of Remote Sensing and GIS in Earth Resource Management 11:Case Studies 1.Real-world Case Studies 2.Practical Applications of Data Analytics and AI in Earth Resource Management 12:Future Trends in Data Analytics and AI for Earth Resource Management 1.Emerging Technologies 2.Potential Impact on Earth Resource Management 3.Future Directions
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