Integrating CNN-BiLSTM Architecture for Predicting Precipitation and Meteorological Patterns

Accurate forecasting of precipitation is essential for various sectors, including agriculture, disaster management, and water resource planning. This paper presents a deep learning architecture that combines Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) layers to predict precipitation using a set of weather parameters, including temperature, station level pressure, dew point, and calculated relative humidity. The proposed architecture leverages CNN for feature extraction and BiLSTM for capturing temporal dependencies, offering insights into the model's efficiency and the challenges associated with predicting precipitation using calculated inputs.