ACCESSIBLE WEATHER FORECASTING APP WITH ML AND SPEECH RECOGNITION FOR THE VISUALLY IMPAIRED
DOI:
https://doi.org/10.29121/granthaalayah.v11.i11.2023.6109Keywords:
Weather, Ml, Speech, Agriculture, Transportation, Event Planning, Personal SafetyAbstract [English]
Weather forecasting plays a crucial role in our daily lives, influencing decisions in agriculture, transportation, event planning, and personal safety. This project presents an intelligent weather prediction system powered by advanced machine learning techniques to forecast weather parameters such as temperature, humidity, rainfall, wind speed, and atmospheric pressure. The system collects real-time weather data using an API and leverages historical weather datasets for model training and evaluation.
To achieve high prediction accuracy, this system incorporates robust machine learning algorithms, including XGBoost and Random Forest Regression, known for their effectiveness in handling non-linear relationships and feature interactions. For time-series forecasting tasks, particularly in predicting future rainfall and temperature trends, the project employs Long Short-Term Memory (LSTM) networks due to their capacity to learn temporal dependencies over time.
An innovative feature of this application is its speech recognition interface, enabling voice-based interaction for querying weather reports. This makes the system especially accessible to visually impaired users, allowing them to receive current weather updates hands-free. Additionally, users can access a graphical dashboard that visualizes real-time weather insights and predictions after entering the city name.
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Copyright (c) 2023 Prince, Nikhil Jha, Pushpendra Singh, Amita Kumari
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