Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

Nelvinebi/Climate-Change-and-Coastal-Vulnerability-Modeling-in-the-Niger-Delta-Using-Machine-Learning

Domaine:

climateenvironment and energy

Type de record:

project
Créateur:
Nel
Hôte:
Climate Change & Coastal Vulnerability Modeling in the Niger Delta Using Machine Learning: an end-to-end environmental data science project integrating temperature, rainfall, sea level, and elevation data to quantify climate trends, compute a Coastal Vulnerability Index, and forecast future temperatures across Nigeria's Niger Delta region. # 🌿 Climate Change & Coastal Vulnerability Modeling in the Niger Delta Live Demo ### Using Machine Learning · Python · Streamlit **An end-to-end environmental data science project analyzing 30 years of climate data to model and predict coastal vulnerability across the Niger Delta region of Nigeria.** --- ## 📋 Table of Contents - Overview - Study Area - Dataset Sources - Project Architecture - Visualizations - Machine Learning Models - Key Findings - Getting Started - Project Structure - Dashboard - Methodology - Limitations & Future Work - References --- ## 🔍 Overview The Niger Delta is one of the most climate-sensitive coastal regions in sub-Saharan Africa. Sitting largely below 10 m above sea level, it faces compounding threats from rising temperatures, intensifying rainfall, and accelerating sea-level rise all of which directly endanger its wetland ecosystems, fishing communities, and petroleum infrastructure. This project integrates four independent climate datasets spanning over a century of observations to: - Quantify long-term temperature and rainfall trends specific to the Niger Delta - Model global sea-level rise and project its local coastal inundation risk - Engineer a composite **Coastal Vulnerability Index (CVI)** from four climate drivers - Train and compare three machine learning models to predict future temperature - Deliver findings through a fully interactive **Streamlit dashboard** > **Study period:** 1984–2013 (30-year merged observation window) > **Forecast horizon:** 2014–2060 --- ## 🗺️ Study Area | Parameter | Value | |-----------|-------| | Region | Niger Delta, Southern Nigeria | | Latitude | 4.0° – 6.5° N | | Longitude | 4.5° – 9.0° E | | Key States | Rivers, Bayelsa, Delta, Akwa Ibom, Cross River, Edo | | Climate Zone | Tropical rainforest / Equatorial | | Mean Elevation | **23.35 m** (with 36.7% of terrain below 10 m) | | Annual Rainfall | ~2,168 mm (among the highest in West Africa) | The Niger Delta is …

Visit

github.com