Weather Forecasting using Application Programming Interface - S B, Manoj Kumar; A R, Jayanth Kumar; S, Koustubha; D C, Leela; K N, Gagana (2023 International Conference on Recent Advances in Science and Engineering Technology (ICRASET), 2023)
Weather forecasting is crucial for planning daily activities, making informed decisions, and ensuring the safety of individuals and communities. The project begins by selecting a weather data provider, such as openweathermap or weather stack, and obtaining the necessary API key. Python libraries, including requests and json parsing, are utilized for making api requests and extracting weather related information. Details such as temperature, humidity, wind speed, and meteorological conditions are included in the retrieved data. The user is provided with a thorough weather report containing the created weather forecasts, which provide insightful information about the current state of affairs and potential future trends. Users can interact with the application by inputting their desired locations to obtain location specific weather information. Python offers a range of machine learning and statistical modelling libraries, including Scikit-Learn, TensorFlow, and PyTorch, which are used to build weather forecasting models. These models can be as simple as linear regression or as complex as deep neural networks. They use historical weather data to learn patterns and relationships between different variables.