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Sinopsis
As natural disasters grow in frequency and intensity, the need for faster and more accurate prediction has become increasingly urgent. Traditional physics-based models, while foundational, often struggle with computational limitations and incomplete or noisy data. In response, advances in machine learning and algorithmic techniques are opening new pathways for analyzing complex patterns and improving predictive capabilities. By harnessing data-driven approaches, researchers are beginning to transform how geo-hazards such as earthquakes, volcanic eruptions, and tsunamis are understood and anticipated. Predicting Earthquakes, Eruptions, and Tsunamis With Machine Learning Forecasting addresses the critical need to improve the accuracy and speed of natural disaster prediction by leveraging advanced machine learning (ML) and algorithmic techniques. Through a comprehensive, interdisciplinary approach, the book demonstrates how ML methods can be applied to complex geophysical datasets such as seismic waveforms, GPS deformation data, satellite imagery, thermal signals, and ocean buoy readings to enhance predictive capabilities. Covering topics such as aftershock sequence forecasting, eruption prediction, and volcanic seismicity, this book is a fundamental academic resource for graduate and doctoral students, disaster risk management practitioners, technology developers, data scientists, policy makers and more.
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Acerca del autor
Professor Froilan D. Mobo, DPA, Ph.D., is an internationally recognized and multi-awarded researcher, educator, and academic leader with expertise in public administration, development education, and social studies. He currently serves as Professor II at the Philippine Merchant Marine Academy (PMMA), San Narciso, Zambales, where he contributes to faculty development, research innovation, and curriculum advancement. Dr. Mobo holds double doctorate degrees: a Ph.D. in Development Education from Central Luzon State University and a Doctorate in Public Administration from Urdaneta City University. He has also earned multiple master's degrees in social studies, business administration, and computer science, showcasing his interdisciplinary expertise. A prolific scholar, Dr. Mobo has published numerous research papers in Scopus-indexed and ACI-indexed journals, earning him recognition as the top scientist from the Philippine Merchant Marine Academy in Education/Educational Technology. His groundbreaking work on AI in education, sustainable workforce management, and digital transformation has made a significant impact on academia and policy-making. Dr. Mobo has been invited as a keynote speaker and resource expert in national and international conferences, particularly in faculty development, research impact assessment, and digital education trends. His dedication to academic excellence has been recognized through prestigious international awards from institutions such as the International Internship University and the Video Tijaswini Foundation. Beyond academia, Dr. Mobo is a passionate advocate for sustainability, technology-driven education, and research mentorship, guiding the next generation of scholars toward academic and professional success. Key Areas of Expertise: ✅ Research & Publication Excellence (Scopus, ACI, and peer-reviewed journals) ✅ Faculty Development & Higher Education Leadership ✅ Public Administration & Sustainable Development ✅ AI & Digital Transformation in Education ✅ Curriculum Development & Educational Technology
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Predicting Earthquakes, Eruptions, and Tsunamis With Machine Learning Forecasting (Hardcover)
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Hardcover. Condición: new. Hardcover. As natural disasters grow in frequency and intensity, the need for faster and more accurate prediction has become increasingly urgent. Traditional physics-based models, while foundational, often struggle with computational limitations and incomplete or noisy data. In response, advances in machine learning and algorithmic techniques are opening new pathways for analyzing complex patterns and improving predictive capabilities. By harnessing data-driven approaches, researchers are beginning to transform how geo-hazards such as earthquakes, volcanic eruptions, and tsunamis are understood and anticipated. Predicting Earthquakes, Eruptions, and Tsunamis With Machine Learning Forecasting addresses the critical need to improve the accuracy and speed of natural disaster prediction by leveraging advanced machine learning (ML) and algorithmic techniques. Through a comprehensive, interdisciplinary approach, the book demonstrates how ML methods can be applied to complex geophysical datasets such as seismic waveforms, GPS deformation data, satellite imagery, thermal signals, and ocean buoy readings to enhance predictive capabilities. Covering topics such as aftershock sequence forecasting, eruption prediction, and volcanic seismicity, this book is a fundamental academic resource for graduate and doctoral students, disaster risk management practitioners, technology developers, data scientists, policy makers and more. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9798337380421
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Predicting Earthquakes, Eruptions, and Tsunamis With Machine Learning Forecasting
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Predicting Earthquakes, Eruptions, and Tsunamis With Machine Learning Forecasting
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Predicting Earthquakes, Eruptions, and Tsunamis With Machine Learning Forecasting (Hardcover)
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Hardcover. Condición: new. Hardcover. As natural disasters grow in frequency and intensity, the need for faster and more accurate prediction has become increasingly urgent. Traditional physics-based models, while foundational, often struggle with computational limitations and incomplete or noisy data. In response, advances in machine learning and algorithmic techniques are opening new pathways for analyzing complex patterns and improving predictive capabilities. By harnessing data-driven approaches, researchers are beginning to transform how geo-hazards such as earthquakes, volcanic eruptions, and tsunamis are understood and anticipated. Predicting Earthquakes, Eruptions, and Tsunamis With Machine Learning Forecasting addresses the critical need to improve the accuracy and speed of natural disaster prediction by leveraging advanced machine learning (ML) and algorithmic techniques. Through a comprehensive, interdisciplinary approach, the book demonstrates how ML methods can be applied to complex geophysical datasets such as seismic waveforms, GPS deformation data, satellite imagery, thermal signals, and ocean buoy readings to enhance predictive capabilities. Covering topics such as aftershock sequence forecasting, eruption prediction, and volcanic seismicity, this book is a fundamental academic resource for graduate and doctoral students, disaster risk management practitioners, technology developers, data scientists, policy makers and more. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Nº de ref. del artículo: 9798337380421
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Predicting Earthquakes, Eruptions, and Tsunamis With Machine Learning Forecasting (Hardcover)
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Hardcover. Condición: new. Hardcover. As natural disasters grow in frequency and intensity, the need for faster and more accurate prediction has become increasingly urgent. Traditional physics-based models, while foundational, often struggle with computational limitations and incomplete or noisy data. In response, advances in machine learning and algorithmic techniques are opening new pathways for analyzing complex patterns and improving predictive capabilities. By harnessing data-driven approaches, researchers are beginning to transform how geo-hazards such as earthquakes, volcanic eruptions, and tsunamis are understood and anticipated. Predicting Earthquakes, Eruptions, and Tsunamis With Machine Learning Forecasting addresses the critical need to improve the accuracy and speed of natural disaster prediction by leveraging advanced machine learning (ML) and algorithmic techniques. Through a comprehensive, interdisciplinary approach, the book demonstrates how ML methods can be applied to complex geophysical datasets such as seismic waveforms, GPS deformation data, satellite imagery, thermal signals, and ocean buoy readings to enhance predictive capabilities. Covering topics such as aftershock sequence forecasting, eruption prediction, and volcanic seismicity, this book is a fundamental academic resource for graduate and doctoral students, disaster risk management practitioners, technology developers, data scientists, policy makers and more. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Nº de ref. del artículo: 9798337380421
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Predicting Earthquakes, Eruptions, and Tsunamis With Machine Learning Forecasting
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Predicting Earthquakes, Eruptions, and Tsunamis With Machine Learning Forecasting
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Buch. Condición: Neu. Predicting Earthquakes, Eruptions, and Tsunamis With Machine Learning Forecasting | Froilan D. Mobo | Buch | Englisch | 2026 | IGI GLOBAL SCIENTIFIC PUBLISHING | EAN 9798337380421 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. Nº de ref. del artículo: 135459832
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Predicting Earthquakes, Eruptions, and Tsunamis With Machine Learning Forecasting
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Buch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - As natural disasters grow in frequency and intensity, the need for faster and more accurate prediction has become increasingly urgent. Traditional physics-based models, while foundational, often struggle with computational limitations and incomplete or noisy data. In response, advances in machine learning and algorithmic techniques are opening new pathways for analyzing complex patterns and improving predictive capabilities. By harnessing data-driven approaches, researchers are beginning to transform how geo-hazards such as earthquakes, volcanic eruptions, and tsunamis are understood and anticipated. Predicting Earthquakes, Eruptions, and Tsunamis With Machine Learning Forecasting addresses the critical need to improve the accuracy and speed of natural disaster prediction by leveraging advanced machine learning (ML) and algorithmic techniques. Through a comprehensive, interdisciplinary approach, the book demonstrates how ML methods can be applied to complex geophysical datasets such as seismic waveforms, GPS deformation data, satellite imagery, thermal signals, and ocean buoy readings to enhance predictive capabilities. Covering topics such as aftershock sequence forecasting, eruption prediction, and volcanic seismicity, this book is a fundamental academic resource for graduate and doctoral students, disaster risk management practitioners, technology developers, data scientists, policy makers and more. Nº de ref. del artículo: 9798337380421
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