Maximum Temperature Forecasting by Random Forest Approach Elman Recurrent Neural Network Statistical Distributions and Extreme Value Theory-Reference-Cited by-同舟云学术

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Maximum Temperature Forecasting by Random Forest Approach Elman Recurrent Neural Network Statistical Distributions and Extreme Value Theory

Published:2024-04-18 Issue: Volume:33 Page:1-7
ISSN:
Container-title:Proceedings of the 7th International Conference on Networking, Intelligent Systems and Security
language:
Short-container-title:

Author:

Amellas ¹^ORCID,Echchelh ¹^ORCID,Djebli ²^ORCID

Affiliation:

1. Physics Laboratory of Electronics systems Information processing Mechanic and Energy Faculty of Science, Ibn Tofail University, Morocco

2. Fluid mechanics and Energy laboratory Department of physics Faculty of Sciences , Abdelmalek Essaadi University, Morocco

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3659677.3659753

Reference28 articles.

1. Gabriel, Filippelli. (2023). Chapter Two. Climate drivers on Earth. Amsterdam: Climate Change and Life; The Complex Co-evolution of Climate and Life on Earth, and Beyond.

2. Marie-Noëlle, W. (2019). Revue de littérature sur le changement climatique au Maroc : observations, projections et impacts. Papiers de recherche, pages 1-33.

3. Evaluation of future temperature and precipitation projections in Morocco using the ANN-based multi-model ensemble from CMIP6

4. Predicting indoor air temperature and thermal comfort in occupational settings using weather forecasts, indoor sensors, and artificial neural networks

5. Air Temperature Forecasting Using Machine Learning Techniques: A Review

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