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Artificial intelligence and machine learning in earth system sciences with special reference to climate science and meteorology in South Asia


Affiliations
1 Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune 411 008, India; Jackson School of Geosciences, The University of Texas at Austin, Austin 78712, USA; IDP in Climate Studies, Indian Institute of Technology Bombay, Powai, Mumbai 400 076, India
2 Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune 411 008, India
3 National Centre for Medium Range Weather Forecasting, Ministry of Earth Sciences, Noida 201 309, India
4 Borehole Geophysics Research Laboratory, Ministry of Earth Sciences, Karad 415 114, India
5 Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune 411 008, India; Centre for Atmospheric and Oceanic Sciences, Indian Institute of Science, Bengaluru 560 012, India; Divecha Centre for Climate Change, Indian Institute of Science, Bengaluru 560 012, India
 

This study focuses on the current problems in earth system science (ESS), where machine learning (ML) algorithms can be applied. It provides an overview of previous studies, ongoing work at the Ministry of Earth Sciences, Government of India, and future applications of ML algorithms to some significant earth science problems. We compare previous studies, a mind map of multidimensional areas related to ML and Gartner’s hype cycle for ML in ESS. We mainly focus on the cri­tical components in earth sciences, including studies on the atmosphere, oceans, biosphere, hydrogeology, human health and seismology. Various artificial intelligence (AI)/ML applications to problems in the core fields of earth sciences are discussed, in addition to gap areas and the potential for AI techniques.

Keywords

Artificial intelligence, climate science, earth sciences, machine learning, meteorology, mind map.
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  • Artificial intelligence and machine learning in earth system sciences with special reference to climate science and meteorology in South Asia

Abstract Views: 334  |  PDF Views: 116

Authors

Manmeet Singh
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune 411 008, India; Jackson School of Geosciences, The University of Texas at Austin, Austin 78712, USA; IDP in Climate Studies, Indian Institute of Technology Bombay, Powai, Mumbai 400 076, India
Bipin Kumar
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune 411 008, India
Rajib Chattopadhyay
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune 411 008, India
K. Amarjyothi
National Centre for Medium Range Weather Forecasting, Ministry of Earth Sciences, Noida 201 309, India
Anup K. Sutar
Borehole Geophysics Research Laboratory, Ministry of Earth Sciences, Karad 415 114, India
Sukanta Roy
Borehole Geophysics Research Laboratory, Ministry of Earth Sciences, Karad 415 114, India
Suryachandra A. Rao
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune 411 008, India
Ravi S. Nanjundiah
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune 411 008, India; Centre for Atmospheric and Oceanic Sciences, Indian Institute of Science, Bengaluru 560 012, India; Divecha Centre for Climate Change, Indian Institute of Science, Bengaluru 560 012, India

Abstract


This study focuses on the current problems in earth system science (ESS), where machine learning (ML) algorithms can be applied. It provides an overview of previous studies, ongoing work at the Ministry of Earth Sciences, Government of India, and future applications of ML algorithms to some significant earth science problems. We compare previous studies, a mind map of multidimensional areas related to ML and Gartner’s hype cycle for ML in ESS. We mainly focus on the cri­tical components in earth sciences, including studies on the atmosphere, oceans, biosphere, hydrogeology, human health and seismology. Various artificial intelligence (AI)/ML applications to problems in the core fields of earth sciences are discussed, in addition to gap areas and the potential for AI techniques.

Keywords


Artificial intelligence, climate science, earth sciences, machine learning, meteorology, mind map.

References





DOI: https://doi.org/10.18520/cs%2Fv122%2Fi9%2F1019-1030