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@article{153382,
        author = {R. Palson kennedy  and P. Kiran Sai },
        title = {Deriving scientific insights from Machine Learning for Geosciences data},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {7},
        pages = {20-31},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=153382},
        abstract = {Geo-sciences is a one of the vital social area that demands solutions to numerous pressing issues confronting humanity and the whole world. As the geo-sciences enter the era of big data, machine learning (ML)—which has been extensively successful in commercial domains—offers enormous promise to help solve geo-sciences challenges.   This article introduces machine learning (ML) researchers to the challenges posed by geo-sciences problems and the potential for both machine learning and geo-sciences advancement. We begin by highlighting common sources of geo-sciences data and outlining their shared characteristics. Data science is gaining traction across a broad range of geo-sciences fields and applications.   To meet that requirement, this article presents a review from a data life cycle viewpoint. Numerous facets of the geo-sciences present unique difficulties for the study of intelligent systems. Geo-sciences data is notoriously difficult to analyze since it is frequently unpredictable, intermittent, sparse, multi-resolution, and multiscale. The spatiotemporal boundaries of geo-sciences processes and objects are frequently amorphous. Across academia, industry, and government, there is a strong desire to learn more about the current state of data science in geo-sciences as well as its potential. To address that need, this article provides a review from a data life cycle perspective. The data life cycle's critical steps include concept generation, data collection, preprocessing, analysis, archiving, distribution, discovery, and repurposing. Initially we discusses the fundamental concepts and theoretical underpinnings of data science, while the second section summarizes key points and shareable experiences from existing publications centered on each stage of the data life cycle. In conclusion, a future vision for data science applications in geo-science is discussed, including topics such as open science, smart data, and team science. We hope that this review will be beneficial to data science practitioners in the geo-science community and flash additional discussion about data science best practices and future trends in geo-sciences and data science.},
        keywords = {Geo-science, Data Science, scientific insights, Machine learning, Big data, data life cycle. },
        month = {},
        }