Peace-Aielumoh/Rainfall-Prediction-Model-for-Nigeria-

This project entails the prediction of rainfall in nigeria using machine learning models and incorporating satellite imagery for improved accuracy # Rainfall Prediction Project This project focuses on predicting rainfall using machine learning techniques and time series data. Accurate rainfall prediction is vital for various applications, including agriculture, disaster management, and water resource planning. ## Table of Contents - Project Overview - Project Structure - Data Preparation - Model Architecture - Training and Evaluation - Model Deployment - Usage - Contributing - License ## Project Overview The primary goal of this project is to develop a machine learning model for rainfall prediction. It utilizes time series data and deep learning techniques, particularly Long Short-Term Memory (LSTM) networks. ## Project Structure - `data/`: drive.google.com - `model/`: Placeholder for saving the trained model. - `rainfall_prediction.py`: Python script containing the code for data preparation, model creation, training, and evaluation. - `requirements.txt`: requirements.txt ## Data Preparation - You need to load your time series data into the appropriate variables (`trainX`, `trainY`, `valX`, `valY`). Ensure that your data is properly preprocessed and split into training and validation sets. ## Model Architecture ## Models ### Linear Regression Linear Regression is a straightforward statistical method used for predicting a target variable based on one or more independent variables. In this project, Linear Regression is employed to predict rainfall levels based on various meteorological features and satellite remote sensing data. ### LSTM (Long Short-Term Memory) Networks LSTM networks are a type of recurrent neural network (RNN) known for their ability to model sequential data effectively. They are employed here for time series prediction of rainfall, incorporating satellite remote sensing information for improved accuracy. - The project utilizes LSTM layers to model the temporal patterns in the data. - Three LSTM layers with dropou …