ABSTRATC
This project presents a comprehensive weather prediction system that utilizes a Long Short-Term Memory (LSTM) model to forecast temperature for the next 1 hour, 1 day, and 1 week. Leveraging deep learning techniques, the system is designed to predict temperature variations based on historical weather data, providing accurate short-term and medium-term forecasts. The dataset used for training consists of historical weather data, including essential features such as maximum temperature, minimum temperature, humidity, precipitation, wind speed, and wind direction. The data is preprocessed using MinMaxScaler for normalization, ensuring effective model training. Missing values are handled using mean imputation to maintain data integrity. The LSTM model is built using TensorFlow's Keras library. It consists of three LSTM layers with dropout regularization to prevent overfitting. The model is compiled using the Adam optimizer with Mean Squared Error (MSE) as the loss function. It employs ReduceLROnPlateau and EarlyStopping callbacks to optimize the training process by dynamically adjusting the learning rate and preventing unnecessary epochs. For real-time predictions, the project integrates with WeatherAPI to fetch live weather data. The data is processed and scaled before being passed to the trained LSTM model for prediction. Flask is used to develop a RESTful API that handles incoming requests, performs model inference, and returns predictions in JSON format. This enables seamless interaction with the model from any front-end interface or external application. The system is evaluated using key performance metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and R2 Score. Visual comparisons of actual vs. predicted temperatures further validate the model’s effectiveness. Additionally, GPU support is enabled for faster model training and prediction. This project finds practical applications in agriculture, logistics, tourism, and urban planning by providing reliable weather forecasts. By extending its capabilities, the system can be enhanced to predict other weather parameters like humidity, rainfall, and wind speed
In conclusion, the LSTM-based weather prediction system offers an efficient and accurate solution for short-term and medium-term temperature forecasting. The combination of deep learning, real-time API integration, and Flask deployment makes it a valuable tool for decision-making across various sectors.
INTRODUCTION
Weather prediction plays a critical role in various sectors, including agriculture, transportation, disaster management, and energy production. Accurate forecasts allow individuals, businesses, and governments to make informed decisions, minimizing risks and optimizing operations. With advancements in artificial intelligence and deep learning, weather prediction systems have become significantly more reliable and efficient. This project focuses on developing a weather prediction system using a Long Short-Term Memory (LSTM) neural network, integrated with a Flask API for real-time forecasting.
LSTMs are a specialized type of recurrent neural network (RNN) designed to process sequential data. Unlike traditional feedforward neural networks, LSTMs are capable of retaining long-term dependencies, making them ideal for time series prediction tasks. By analyzing historical weather data, the LSTM model learns patterns and correlations to predict future temperatures. This capability is especially valuable for short-term and medium-term forecasts, such as predicting the temperature for the next hour, day, or week.
The project leverages a dataset containing detailed weather information, including maximum temperature, minimum temperature, humidity, precipitation, wind speed, and wind direction. This comprehensive feature set ensures that the model can capture both seasonal and short-term weather patterns. Data preprocessing steps such as normalization and missing value imputation are applied to ensure the quality and consistency of the input data.
The architecture of the LSTM model consists of multiple stacked layers, with dropout regularization added to reduce overfitting. The Adam optimizer and Mean Squared Error (MSE) loss function are used to optimize model performance. Additionally, callbacks like ReduceLROnPlateau and EarlyStopping are implemented to adjust the learning rate and halt training when the validation loss stops improving.
For real-time predictions, the project integrates with WeatherAPI to fetch live weather data. This data is then preprocessed using the same scaling techniques applied during training. The trained LSTM model takes the processed input and generates temperature forecasts for the next 1 hour, 1 day, and 1 week. Flask, a lightweight web framework, is used to create a RESTful API that enables users to send prediction requests and receive responses in JSON format.
Evaluation metrics including Mean Squared Error (MSE), Mean Absolute Error (MAE), and R2 Score are used to assess the model's accuracy. Visualizations comparing actual and predicted temperatures are also generated to provide a clear understanding of the model's performance.
This system has numerous practical applications. In agriculture, accurate temperature predictions can help farmers make timely decisions regarding irrigation, planting, and harvesting. In the transportation sector, weather forecasts ensure safer travel planning. Disaster management agencies can also leverage this system to predict extreme weather events, enabling proactive measures to minimize damage.
The modular design of this project allows for easy customization and expansion. Additional weather parameters like humidity, wind speed, and precipitation can be included for enhanced prediction capabilities. Moreover, the API can be integrated with web or mobile applications, providing users with real-time weather insights on the go.
In conclusion, this weather prediction system showcases the potential of deep learning in time series forecasting. By combining LSTM-based predictions with a user-friendly API, the project offers a practical and efficient solution for accurate temperature forecasting. Future enhancements could involve expanding the dataset, incorporating ensemble models, and refining the prediction accuracy through hyperparameter tuning and feature engineering.