mehdixlabetix/Weather-Time-Series

A Time Series model that predicts the level of water in multiple dams around the Tunisian country # Tunisian Water Level Prediction This project is a Jupyter Notebook that implements a Time Series model for predicting the water level in multiple dams across Tunisia. The goal of the project is to provide accurate and timely predictions to help monitor and manage water resources in the country. ## Table of Contents - Introduction - Data - Methodology - Installation - Usage - Results - Contributing - License ## Introduction Water scarcity and effective management of water resources are critical issues for Tunisia. This project aims to address these challenges by developing a predictive model that can forecast the water level in multiple dams across the country. The model is based on time series analysis and provides valuable insights for decision-makers and water resource managers. ## Data The dataset used for this project includes historical water level data from various dams in Tunisia. The data is collected at regular intervals, and each observation consists of the dam's location, date, and the corresponding water level,it was given for free by the Tunisian government. The dataset is preprocessed and cleaned to remove any missing or erroneous values before being used for model training and evaluation. ## Methodology The Time Series model employed in this project is based on the xgbregressor model. Xgbregressor is a powerful tool to provide accurate predictions. The methodology used in this project can be summarized as follows: 1. Data preprocessing: Cleaning the dataset, handling missing values, and formatting the data for analysis. 2. Exploratory data analysis: Understanding the characteristics of the data, identifying patterns, and visualizing the time series. 3. Model training: Using the Prophet library to train the time series model on the historical data. 4. Model evaluation: Assessing the performance of the trained model using appropriate evaluation metrics. 5. Forecasting: Generating future predictions of water levels based on the trained model. …