Machine Learning in Earth, Environmental and Planetary Sciences 1st Edition

Capture the future of geoscience with Machine Learning in Earth, Environmental and Planetary Sciences, 1st Edition by Hossein Bonakdari, Isa Ebtehaj, and Joseph D. Ladouceur — a practical, rigorous guide that bridges machine learning techniques with real-world environmental challenges.

This book draws you in with clear explanations of supervised and unsupervised learning, deep learning, time-series forecasting, spatial analysis, feature engineering, and model evaluation — all tailored to problems like climate modeling, remote sensing, hydrology, air quality, and planetary data interpretation. Case studies span global and regional scales so researchers and practitioners in North America, Europe, Asia, Africa, Latin America, and Australia can directly apply methods to local watersheds, urban air management, polar ice studies, and planetary exploration.

Written for graduate students, environmental engineers, data scientists, and geoscientists, the text balances mathematical rigor with accessible examples and practical workflows. You’ll gain the skills to design robust models, handle noisy geospatial datasets, and translate predictive insights into actionable policy and field decisions. The interdisciplinary approach empowers academics, government agencies, and private-sector teams to accelerate discovery and improve resource management.

If you want a single, dependable reference that connects modern machine learning with the pressing needs of earth, environmental, and planetary sciences, this edition belongs on your shelf. Order your copy of Machine Learning in Earth, Environmental and Planetary Sciences (1st Edition) today and start turning complex environmental data into impactful solutions.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.