Prediction of Daily Climate Using Long Short-Term Memory (LSTM) Model

🤖 Plain-English Summary

Climaate prediction plays a vital role in various sectors, including agriculture, disaster management, and urban planning. Our results demonstrate the model's capability to capture temporal dependencies in climate data, achieving a satisfactory level of accuracy in temperature forecasting.

🔑 Key Findings

• Traditional methods for climate forecasting often rely on complex physical models, which require substantial computational resources and may not accurately capture local weather patterns.
• This study explores the potential of Long Short-Term Memory (LSTM) networks, a type of recurrent neural network, for predicting daily climate variables such as temperature, precipitation, and humidity.
• Utilizing historical climate data from the city of Delhi, we developed an LSTM model to forecast short-term climate trends.

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