Efficiency of the Combination of Machine Learning Models in the Evaluation of Weather Parameters
In one sentence. Boosting ensemble of machine learning models predicts multiple meteorological parameters from measurements; no quantitative results given.
Abstract
In this article we exploit the potential presented by the combination of machine learning models (Ensemble Learning) as one of the essential points of the Soft aspect, i.e. observation tools, monitoring, sampling and study of meteorological parameters in order to provide effective support and monitoring of measures taken at different levels in the fight against climate change and sustainable management of the environment by creating a learning model automatic composed of the measurements of the various meteorological parameters (Temperature, Rainfall, Humidity rate, Wind speed, etc.) by training this model using the Ensemble Learning technique called "BOOSTING" on the various measurements taken from each indicator so as to continuously train on past data and be able to predict the next weather forecast with high precision or even make annual or multi-year projections of the evolution of our climatic situation and present this to the various players in our environment and thus enable them to better anticipate possible extreme situations that could negatively affect our environmental situation.
Labels
- Topic
- Local & station forecasting
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 6 h–3 days
- Domain
- Local / station
- Data
- Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.24014/coreit.v9i1.21713 OpenAlex:W4395100190
- Added
- 2026-10-22
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