Predicting daily maximum temperature over Andhra Pradesh using machine learning techniques
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Prediction accuracy and heatwave projection greatly depend on the persistence of events in terms of temporal dependencies of the time series data.
“the prediction accuracy and heatwave projection greatly depend on the persistence of events in terms of temporal dependencies of the time series data.”
From Comparative analysis of machine learning approaches for heatwave event prediction in India (2025) · cites this paper as [53]
Cites
The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500
Meinshausen, M., Nicholls, Z. R. J., Lewis, J., Gidden, M. J., Vogel, E., Freund, M., Beyerle, U., Gessner, C., Nauels, A., Bauer, N., et al. (2020) · Geoscientific Model Development · doi:10.5194/gmd-13-3571-2020 · Cited by 1,815 · cited 1× · see citing works
“SSP5–8.5 may be the optimal choice in research applications if the objective is to determine a relationship between climate change and another event because the climate signal is strongest under this emissions scenario (Meinshausen et al. 2020; Lee et al. 2021; Vinodhkumar et al. 2024).”
InfluentialAdded value of CMIP6 over CMIP5 models in simulating Indian summer monsoon rainfall
Gusain, A., Ghosh, S., & Karmakar, S. (2020) · Atmospheric Research · doi:10.1016/j.atmosres.2019.104680 · Cited by 408 · see citing works
Analog forecasting of extreme-causing weather patterns using deep learning
Chattopadhyay, A., Nabizadeh, E., & Hassanzadeh, P. (2020) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2019ms001958 · Cited by 224 · see citing works
Improving Subseasonal Forecasting in the Western U.S. with Machine Learning
Hwang, J., Orenstein, P., Cohen, J., Pfeiffer, K., & Mackey, L. (2019) · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3292500.3330674 · Cited by 85 · see citing works
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Comparative analysis of machine learning approaches for heatwave event prediction in India
V, R. C., Johnvictor, A. C., & N, P. S. (2025) · Scientific Reports · doi:10.1038/s41598-025-04634-9 · Cited by 9 · see citing works
- Authors
- Velivelli, S., Satyanarayana, G. C., & Ali, M. M.
- Affiliations
- Koneru Lakshmaiah Education Foundation (IN)
- Venue
- Theoretical and Applied Climatology
- Published
- 2024-08-19
- Volume
- 155
- Issue
- 9
- Pages
- 8567-8585
- ISSN
- 0066-6424 (ISSN-L)
- 0177-798X
- 1434-4483
- OpenAlex
- W4401709324
- Cited by
- 9
- References
- 4
- Citations by year
- 2026: 7 · 2025: 2
- FWCI
- 1.41
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Theoretical and Applied Climatology (publishedVersion)
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
- Physical Sciences › Environmental Science › Environmental Engineering › Hydrological Forecasting Using AI
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
- Keywords
- surface air temperature, heat waves, hot days, artificial neural networks, future temperature projections, Andhra Pradesh, climate change adaptation
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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