# Accurate Long-term Air Temperature Prediction with a Fusion of Artificial Intelligence and Data Reduction Techniques

arXiv: https://arxiv.org/abs/2209.15424 · HTML: https://mlsanity.com/paper/2209.15424/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 59 references that are in the map.
- First posted on arXiv 29 Sep 2022.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [WeatherBench: A benchmark dataset for data-driven weather forecasting](https://mlsanity.com/paper/2002.00469/) (2020)
- [Deep learning to represent sub-grid processes in climate models](https://mlsanity.com/paper/1806.04731/) (2018)
- [Analog forecasting of extreme-causing weather patterns using deep learning](https://mlsanity.com/paper/1907.11617/) (2019)
- [Machine Learning Information Fusion in Earth Observation: A Comprehensive Review of Methods, Applications and Data Sources](https://mlsanity.com/paper/2012.05795/) (2020)
- [A Deep Learning Model for Forecasting Global Monthly Mean Sea Surface Temperature Anomalies](https://mlsanity.com/paper/2202.09967/) (2022)
- [Analysis, Characterization, Prediction and Attribution of Extreme Atmospheric Events with Machine Learning: a Review](https://mlsanity.com/paper/2207.07580/) (2022)

## Built on by (most cited first, 1 of 1)

- [Spain on Fire: A novel wildfire risk assessment model based on image satellite processing and atmospheric information](https://mlsanity.com/paper/2306.05045/) (2023)

## Abstract (arXiv)

In this paper three customised Artificial Intelligence (AI) frameworks, considering Deep Learning (convolutional neural networks), Machine Learning algorithms and data reduction techniques are proposed, for a problem of long-term summer air temperature prediction. Specifically, the prediction of average air temperature in the first and second August fortnights, using input data from previous months, at two different locations, Paris (France) and C'ordoba (Spain), is considered. The target variable, mainly in the first August fortnight, can contain signals of extreme events such as heatwaves, like the mega-heatwave of 2003, which affected France and the Iberian Peninsula. Thus, an accurate prediction of long-term air temperature may be valuable also for different problems related to climate change, such as attribution of extreme events, and in other problems related to renewable energy. The analysis carried out this work is based on Reanalysis data, which are first processed by a correlation analysis among different prediction variables and the target (average air temperature in August first and second fortnights). An area with the largest correlation is located, and the variables within, after a feature selection process, are the input of different deep learning and ML algorithms. The experiments carried out show a very good prediction skill in the three proposed AI frameworks, both in Paris and C'ordoba regions.