Analysis of a K-Nearest Neighbor Model for Short-Term Weather Prediction

This research paper examines a weather prediction model using the k-nearest neighbor (K-NN) algorithm, with a focus on evaluating its accuracy, simplicity, and efficiency in forecasting essential weather parameters such as temperature, humidity, precipitation, and wind speed. By analyzing historical weather data, the K-NN algorithm identifies patterns and uses proximity to predict future weather conditions. The study also looks at the model's computational efficiency and its ability to adapt to various geographic regions. Despite its straightforward nature, the K-NN model shows promising results, offering a good balance ratio between accuracy and computational cost for short-term weather forecasting.