Revolutionizing Weather Forecasting By Combination Of Internet Of Things (Iot) And Artificial Intelligence (AI) Especially For Tourism In Relevant Cities Of India

Abstract

The Environmental Weather Comparison Application is an innovative tool that provides users with real-time weather updates, historical weather analysis, and multi-city comparisons based on customizable "ideal" conditions. Designed with an intuitive graphical user interface (GUI) using Tkinter, the application integrates advanced data visualization techniques and machine learning for enhanced insights. Key features include multi-city weather comparisons, trend analysis through historical weather data. IoT sensors sense and receive  real-time physical parameters such as temperature, humidity, wind speed, rain and air pressure data, while AI processes this data to generate accurate weather predictions. The machine learning integration using Linear Regression to predict patterns in physical parameters of weather.  Users can define their preferred weather conditions, personalize themes, and store data for future reference. The application fetches live weather updates via Weather API and utilizes the Wikipedia API for city descriptions.  The technology stack consists of Python and essential libraries such as Tkinter for GUI development, Matplotlib for visual graphs, Requests for API communication, and scikit-learn for machine learning implementations. NumPy and Pandas further support numerical data processing. Weather trends are visually represented through graphs, enabling users to determine the most suitable city to visit based on calculated scores for temperature, wind speed, and humidity. The aim of this proposed real-time experimental research aims to deliver weather insights to tourism enthusiasts, helping them determine the most suitable weather conditions for visiting various destinations across India. By leveraging advanced technologies IoT and AI, this initiative ensures that travellers receive accurate forecasts, allowing them to plan their trips effectively and comfortably as per their suitable weather conditions for a seamless travel experience. In future enhancements include the integration of Polynomial Regression for complex trend predictions, extended language support, and improved GUI aesthetics with animations.