Classification of Weather Conditions Based on Supervised Learning for Swedish Cities
In one sentence. Compares ANN, SVM, random forest and KNN for weather-condition classification in Swedish cities; the ANN performs best and beats Weatherstack's own forecasts.
Abstract
Weather forecasting has always been challenging due to the atmosphere’s complex and dynamic nature. Weather conditions such as rain, clouds, clear skies, and sunniness are influenced by several factors, including temperature, pressure, humidity, wind speed, and direction. Physical and complex models are currently used to determine weather conditions, but they have their limitations, particularly in terms of computing time. In recent years, supervised machine learning methods have shown great potential in predicting weather events accurately. These methods use historical weather data to train a model, which can then be used to predict future weather conditions. This study enhances weather forecasting by employing four supervised machine learning techniques—artificial neural networks (ANNs), support vector machines (SVMs), random forest (RF), and k-nearest neighbors (KNN)—on three distinct datasets obtained from the Weatherstack database. These datasets, with varying temporal spans and uncertainty levels in their input features, are used to train and evaluate the methods. The results show that the ANN has superior performance across all datasets. Furthermore, when compared to Weatherstack’s weather prediction model, all methods demonstrate significant improvements. Interestingly, our models show variance in performance across different datasets, particularly those with predicted rather than observed input features, underscoring the complexities of handling data uncertainty. The study provides valuable insights into the use of supervised machine learning techniques for weather forecasting and contributes to the development of more precise prediction models.
Labels
- Topic
- Local & station forecasting
- Type
- Evaluation or intercomparison of existing models
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 6 h–3 days
- Domain
- Local / station
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.3390/atmos14071174 OpenAlex:W4385069115
- Added
- 2026-10-22
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