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Artificial Intelligence and Machine Learning to Assist Climate Change Monitoring

Rahul Malik1,a, Sagar Pande1,b, Nishi2,c, Aditya Khamparia1,d,*

Corresponding Author:

Aditya Khamparia

Affiliation(s):

1 LOVELY PROFESSIONAL UNIVERSITY, Punjab, India.
2 DAV UNIVERISTY, Punjab, India.
a [email protected], b [email protected], c [email protected], d [email protected]
*Corresponding Author: Aditya Khamparia, Email: [email protected]

Abstract:

Climate change issues societal operation, likely wanting considerable adaptation to deal with doing well altered weather patterns. Machine learning (ML) algorithms have progressed considerably, triggering breakthroughs in some other investigation sectors, along with only lately suggested as helping climate evaluation. Though a significant volume of isolated Earth System functions are analyzed with ML techniques, much more generic phone system to find out better the whole temperature unit hasn't happened. For instance, ML is able to aid remote identification, in which complex feedbacks make characterization tough from instantaneous equation analysis or perhaps possibly visualization of sizes plus Earth System design (ESM) diagnostics. Artificial intelligence (AI) may thus build on determined climate associates to provide enhanced alerts of approaching eco-friendly functions, which includes intense events. While ESM development is actually completely necessary, a parallel concentrate on utilizing ML and AI to determine as well as capitalize a great deal more on pre pre-existing simulations as well as info is suggested by us.

Keywords:

Climate, glacier retreat, mass balance, lakes, sea level

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Cite This Paper:

Rahul Malik, Sagar Pande, Nishi, Aditya Khamparia (2020). Artificial Intelligence and Machine Learning to Assist Climate Change Monitoring. Journal of Artificial Intelligence and Systems, 2, 168–190. https://doi.org/10.33969/AIS.2020.21011.

References:

[1] Chaturvedi R.K., Kulkarni A., KaryakarteY., Joshi J., Bala G., (2014), Glacial      Mass Balance Changes in the Karakoram and Himalaya based on CMIP5 Multi-Model Climate Projections. Climatic Change, 2014, 315-328.
[2] A.V.Kulkarni., Glaciers as source of water: The Himalaya. Sustainable Humanity, Sustainable Nature: Our Responsibility, 2014.
[3] Sharma Kartik; Aggarwal Ashutosh; Singhania Tanay; Gupta Deepak; Khanna Ashish (2019). Hiding Data in Images Using Cryptography and Deep Neural Network. Journal of Artificial Intelligence and Systems, 1, 143–162.
[4] Raffaele Cioffi, Marta Travaglioni, Giuseppina Piscitelli, Antonella Petrillo and Fabio De Felice, Artificial Intelligence and Machine Learning Applications in Smart Production: Progress, Trends, and Directions, Sustainability 2020, 12, 492, 1-26.
[5] Levermann, A., P. U. Clark, B. Marzeion, G. A. Milne, D. Pollard, V. Radic, and A. Robinson, The multimillenial sea level commitment of global warming, Proc. Natl. Acad. Sci. U. S. A., 2013,110(3), 13745–13750.
[6] Tayal, Shresth, Climate Change Impacts on Himalayan Glaciers and Implications on Energy Security of India, TERI 2019: The Energy and Resources Institute, 1-32.
[7] Sousa, P. H. F.; Nascimento, N. M. M.; Almeida, J. S.; Rebouças Filho, P. P. and Albuquerque, V. H. C. (2019). Intelligent Incipient Fault Detection in Wind Turbines based on Industrial IoT Environment. Journal of Artificial Intelligence and Systems, 1, 1–19.
[8] Hongbo Duan, Gupeng Zhang, Shouyang Wangand Ying Fan, Robust climate change research: a review on multi-model analysis, Environ. Res. Lett. (14) 2019, 1-24.
[9] Aditya Khamparia, Gurinder Saini, Deepak Gupta, Ashish Khanna, Shrasti Tiwari, Victor Hugo C. de Albuquerque: Seasonal Crops Disease Prediction and Classification Using Deep Convolutional Encoder Network. CSSP 39(2): 818-836 (2020).
[10] J. M. Maurer, J. M. Schaefer, S. Rupper, A. Corley, Acceleration of ice loss across the Himalayas over the past 40 years. Sci. Adv. 5, eaav7266, 2019.
[11] A. Dehecq, N. Gourmelen, A. S. Gardner, F. Brun, D. Goldberg, P. W. Nienow, E. Berthier, C. Vincent, P. Wagnon, E. Trouvé, Twenty-first century glacier slowdown driven by mass loss in High Mountain Asia. Nat. Geosci., 2019, 12, 22–27.
[12] M. Begert, C. Frei, Long-term area-mean temperature series for Switzerland—Combining homogenized station data and high resolution grid data. Int. J. Climatol., 2018, 38, 2792–2807.
[13] Khamparia, A., Singh, A., Anand, D., Gupta, D., Khanna, A., Arun Kumar, N., & Tan, J. , A novel deep learning-based multi-model ensemble method for the prediction of neuromuscular disorders. Neural Computing and Applications, 2018.
[14] K. Mukherjee, A. Bhattacharya, T. Pieczonka, S. Ghosh, T. Bolch, Glacier mass budget and climate reanalysis data indicate a climatic shift around 2000 in Lahaul-Spiti, western Himalaya. Clim. Change, 2018, 148, 219–233.
[15] M. F. Azam, P. Wagnon, E. Berthier, C. Vincent, K. Fujita, J. S. Kargel, Review of the status and mass changes of Himalayan-Karakoram glaciers. J. Glaciol., 2018, 64, 61–74.
[16] Wu C, Chen Y, Peng C, Li Z and Hong X, Modeling and estimating aboveground biomass of Dacrydium pierrei in China using machine learning with climate change J. Environ. Manage, 2019, 234 167–79.
[17] Sharma, P., Gupta, A., Aggarwal, A., Gupta, D., Khanna, A., Hassanien, A. E., & de Albuquerque, V. H. C., The health of things for classification of protein structure using improved grey wolf optimization. The Journal of Supercomputing, 2018.
[18] Aditi Mukherji, Anna Sinisalo, Marcus Nüsser, Rodney Garrard & Mats Eriksson, Contributions of the cryosphere to mountain communities in the Hindu Kush Himalaya: a review, Regional Environmental Change, 2019 19:1311–1326.
[19] Huss M, Hock R, Global-scale hydrological response to future glacier mass loss. Nat Clim Chang, 2018, 8:135–140.
[20] Aditya Khamparia, Babita Pandey, Shrasti Tiwari, Deepak Gupta, Ashish Khanna, Joel J. P. C. Rodrigues: An Integrated Hybrid CNN-RNN Model for Visual Description and Generation of Captions. CSSP 39(2): 776-788 (2020).
[21] Lutz AF, Maat HW, Wijngaard RR, Biemans H, Syed A, Shrestha AB, Wester P, Immerzeel WW, 2018 South Asian river basins in a 1.5°C warmer world. Regional Environmental Change
[22] B R ARORA and RESOURCE TEAM, The Himalayan Cryosphere: Appraisal of Climate-Glacier Inter-linkages, Proc Indian Natn Sci Acad 85 No. 2 June 2019 pp. 319-342.
[23] Aditya Khamparia, Sagar Pande, Deepak Gupta, Ashish Khanna, Arun K. Sangaiah, Multilevel Framework for Anomaly Detection in Social Networking, Library Hi Tech (Emerald), 2019.
[24] Ajai, Inventory and Monitoring of Snow and Glaciers of the Himalaya using Space Data In: Science and Geopolitics of the white world Arctic, Antarctic and Himalaya (SaGAA) (Eds) P S Goel, Rasik Ravindra and Sulagna Chattopadhyay, 2018, pp101-130.
[25] Khan A A, Pant N C, Ravindra R, Alok A, Gupta M and Gupta S, A precipitation perspective of the Hydrospherecryosphere interaction in the Himalaya Geological Society London Special Publications, 2018, pp 462-473.
[26] Sachi Nandan Mohanty, K. C. Ramya, S. Sheeba Rani, Deepak Gupta, K. Shankar, S. K. Lakshmanaprabu, Ashish Khanna: An efficient Lightweight integrated Blockchain (ELIB) model for IoT security and privacy. Future Gener. Comput. Syst. 102: 1027-1037 (2020).
[27] Pant N C, Ravindra R, Srivastava D and Thompson L G (eds), The Himalayan Cryosphere: Past and Present. Geological Society, London, Special Publications, 2018, 462.
[28] Boers N et al., Complex networks reveal global pattern of extreme-rainfall remotes Nature, 2019, 566 373–77.
[29] Buckland C E, Bailey R M and Thomas D S G, Using artificial neural networks to predict future dryland responses to human and climate disturbances Sci. Rep., 2019, 9 3855.
[30] Akshi Kumar, Himanshu Ahuja, Nikhil Kumar Singh, Deepak Gupta, Ashish Khanna, Joel J. P. C. Rodrigues: Supported matrix factorization using distributed representations for personalised recommendations on twitter. Comput. Electr. Eng. 71: 569-577 (2018).
[31] Ghiggi G, Humphrey V, Seneviratne S I and Gudmundsson L, GRUN: an observations-based global gridded runoff dataset from 1902 to 2014 Earth Syst. Sci. Data Discuss. 2019, 1–32.
[32] Germanno Teles, Joel J. P. C. Rodrigues, Ricardo A. L. Rabê, Sergei A. Kozlov (2020). Artificial neural network and Bayesian network models for credit risk prediction. Journal of Artificial Intelligence and Systems, 2, 118–132.
[33] Knusel B et al., Applying big data beyond small problems in climate research Nat. Clim. Change, 2019, 9 196–202.
[34] Kornhuber Ket al., Extreme weather events in early summer 2018 connected by a recurrent hemispheric wave-7 pattern Environ. Res. Lett., 2019, 14 054002.
[35] Anvita Saxena, Ashish Khanna, Deepak Gupta (2020). Emotion Recognition and Detection Methods: A Comprehensive Survey. Journal of Artificial Intelligence and Systems, 2, 53–79.
[36] Raissi M, Perdikaris P and Karniadakis G E, Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equationsJ. Comput. Phys., 2019, 378 686–707.
[37] Reichstein M et al., Deep learning and process understanding for data-driven Earth system science Nature, 2019, 566 195–204.
[38] Yang H et al., Strong but intermittent spatial covariations in tropical land temperature Geophys. Res. Lett., 2019 46 356–64.
[39] Ricardo Vinuesa, Hossein Azizpour Iolanda Leite, Madeline Balaam, Virginia Dignum, Sami Domisch, Anna Felländer , Simone Daniela Langhans, Max Tegmark & Francesco Fuso Nerini, The role of artificial intelligence in achieving the Sustainable Development Goals, Nature Communications, 2020, 11:233.
[40] Kulkarni, A. V. and Suja Alex, Estimation of recent glacial variations in Baspa basin using remote sensing technique. Journal of Indian Society of Remote Sensing, 2003, 31(2), 81-90.
[41] Duan, Y.; Edwards, J.S.; Dwivedi, Y.K. Artificial intelligence for decision making in the era of Big Data—Evolution, challenges and research agenda. Int. J. Inf. Manag. 2019, 48, 63–71.
[42] Ngozi H. Arihilam, E. C. Arihilam, Impact and control of anthropogenic pollution on the ecosystem – A review, 54-59, 2019.
[43] Aditya Khamparia, Babita Pandey,Devendra Kr.Pandey,Deepak Gupta,Ashish Khanna, Victor Hugo Cde Albuquerque, Comparison of RSM, ANN and Fuzzy Logic for extraction of Oleonolic Acid from Ocimum sanctum, Volume 117, May 2020.
[44] Chouhan, V.; Singh, S.K.; Khamparia, A.; Gupta, D.; Tiwari, P.; Moreira, C.; Damaševičius, R.; de Albuquerque, V.H.C., A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images. Appl. Sci., 10, 559, 2020.
[45] Huntingford, C., Jeffers, E. S., Bonsall, M. B., Christensen, H. M., Lees, T., and Yang, H.: Machine learning and artificial intelligence to aid climate change research and preparedness, Environ. Res. Lett., 14, 124007, https://doi.org/10.1088/1748-9326/ab4e55, 2019.