Weather prediction performance evaluation on selected machine learning algorithms
In one sentence. Compares decision tree, k-NN and logistic regression classifiers for weather prediction on a Kaggle dataset, with a Flask demo application; small-scale evaluation.
Abstract
Prediction of weather has been proved useful in the early warning on the impacts of weather on several areas of human livelihood. For example, the provision of decisions for autonomous transportation to reduce traffic congestion and accidents during the rainy season. However, providing the most accurate and effective forecasting model for weather forecasts has been a challenge. Hence, machine learning (ML) techniques and factors influencing weather prediction need to be investigated. Data scientists are yet to discover the best models for weather prediction. Therefore, this study compares three ML classification techniques for weather prediction. A web-based software application was developed using Flask App to demonstrate weather modeling using three ML models, and the data used for the study was obtained from Kaggle. For the weather prediction; a decision tree (DT), k-nearest neighbor (k-NN), and logistic regression (LR) classifier method were suggested, and comparisons were made between the three classifications techniques. The accuracy results show that with a 100% accuracy rate, the DL classifier outperforms the k-NN with a 78% accuracy rate and LR with a 93% accuracy rate. The results show that the application of ML models gives accurate results on weather prediction.
Labels
- Topic
- Local & station forecasting
- Type
- Evaluation or intercomparison of existing models
- Method
- Classical ML (trees, GP, linear, kernels)
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.11591/ijai.v11.i4.pp1535-1544 OpenAlex:W4293093027
- Added
- 2026-10-22
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