WEATHER FORECASTING USING MACHINE LEARNING ALGORITHM
In one sentence. Compares k-NN, decision tree, random forest and linear regression for weather prediction, reporting accuracy with no new method.
Abstract
The weather is disordered in nature and hence has always been difficult for meteorologists in forecasting the weather accurately. Different methods and new models are being updated to keep pace with the everchanging nature of weather. Weather is measured from days to months where it includes the following components such as: Precipitation, Temperature, Sunshine, Wind, Lightning, Direction of Wind, Humidity, Cloud Cover, Speed of Wind, Snow, Pressure etc. The nature of the Weather tends to change every second on earth and has a great influence even if there are some small changes that occurs at any point of time on the surface. Meteorologists have done researches in weather prediction using various mathematical models. This project aims to obtain the highest accuracy to predict weather using Four different algorithm. We are using four different machine learning algorithms for predicting weather. Algorithms – K-Nearest Neighbor, Decision Tree, Random Forest Algorithm, Linear Regression.
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
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.61841/37nnyn17 OpenAlex:W7215697320
- Added
- 2026-10-22
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