Smart Whether Prediction using Machine Learning Algorithm
In one sentence. Compares regression-style machine learning models on historical station data to predict minimum, maximum and average temperature and precipitation for different locations.
Abstract
Weather forecasting with traditional technique is mainly done by physical model, still in many parts of the world. Though not neglecting the importance of the model there is an alternative method where recorded data of past can be used for predicting the future weather data. The predicted data may not be exact, but less time consuming and more efficient. Minimum temperature, maximum temperature, average temperature, precipitation percentage these are the common parameters and predicting these with another less resource-based method with some precision will help us going. Machine learning can be used for processing the data based on models like linear regression, functional regression, circular, statistical which processes the data and reduces the error. On comparing the result of model based on different location one can used a model based on requirement. The result obtained then can be analyzed and further improved upon input variables and data size.
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:10.48175/ijarsct-3511 OpenAlex:W4229019135
- Added
- 2026-10-22
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