Uncertainty and sensitivity analysis of deep learning models for diurnal temperature range (DTR) forecasting over five Indian cities-Reference-Cited by-同舟云学术

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Uncertainty and sensitivity analysis of deep learning models for diurnal temperature range (DTR) forecasting over five Indian cities

Published:2023-01-12 Issue:2 Volume:195 Page:
ISSN:0167-6369
Container-title:Environmental Monitoring and Assessment
language:en
Short-container-title:Environ Monit Assess

Author:

Sankalp Sovan^ORCID,Sahoo Bibhuti Bhusan,Sahoo Sanat Nalini

Funder

Science and Engineering Research Board

Publisher

Springer Science and Business Media LLC

Subject

Management, Monitoring, Policy and Law,Pollution,General Environmental Science,General Medicine

Link

https://link.springer.com/content/pdf/10.1007/s10661-022-10844-9.pdf

Reference66 articles.

1. Aghelpour, P., Mohammadi, B., & Biazar, S. M. (2019). Long-term monthly average temperature forecasting in some climate types of Iran, using the models SARIMA, SVR, and SVR-FA. Theoretical and Applied Climatology, 138(3), 1471–1480.

2. Bini, S. A. (2018). Artificial intelligence, machine learning, deep learning, and cognitive computing: What do these terms mean and how will they impact health care? Journal of Arthroplasty, 33(8), 2358–2361. https://doi.org/10.1016/j.arth.2018.02.067

3. Chen, J., Jing, H., Chang, Y., & Liu, Q. (2019). Gated recurrent unit based recurrent neural network for remaining useful life prediction of nonlinear deterioration process. Reliability Engineering and System Safety, 185(January), 372–382. https://doi.org/10.1016/j.ress.2019.01.006

4. Cheng, J., Xu, Z., Zhu, R., Wang, X., Jin, L., Song, J., & Su, H. (2014). Impact of diurnal temperature range on human health: A systematic review. International Journal of Biometeorology, 58(9), 2011–2024. https://doi.org/10.1007/s00484-014-0797-5

5. Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference, 1724–1734. https://doi.org/10.3115/v1/d14-1179

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