Decision Tree-Based Weather Prediction
In one sentence. C4.5 decision tree classifies next-day weather as no rain, rain, light rain or heavy rain from weather variables; a simple classification study.
Abstract
Weather is formed from a group of weather elements and can be changed fast. For example, in the morning, afternoon, or evening, the weather can be different for each place and every hour. Weather is the condition of the air that occurs in a narrow place and lasts for a short time. Weather conditions in a place can be determined by many factors, such as air pressure, humidity, wind, sunlight, and so on. Therefore, by looking at these factors it can be estimated the weather that will occur the next day. Fishermen and farmers are fields of work that are closely related to weather forecasting, accurate and fast weather predictions are needed by these fields to carry out various activities. The amount of rainfall that occurs cannot be determined exactly but can be predicted or estimated. The application of determining weather information is needed, hence, the prediction can be utilized optimally by the community. The design of a system that will classify automatically can be developed by applying machine learning methods, one of them is used in this study, i.e., C.4.5 Algorithm using weather data that a reference in determining the weather conditions whether not rainy, rainy, light rain, or heavy rain.
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
- Status
- Research
- Identifiers
- DOI:10.33558/piksel.v10i1.4418 OpenAlex:W4223932376
- Added
- 2026-10-22
Labels and summary are generated by Claude and reviewed weekly. Wrong? Edit this entry.