Nelvinebi/Shoreline-Erosion-and-Accretion-Analysis-in-the-Niger-Delta-Using-Satellite-Imagery-and-ML
Domaine:
environment and energygeospatialType de record:
softwareproject
Créateur:
Nel
Hôte:
This project applies machine learning to analyze shoreline erosion and accretion in the Niger Delta using synthetic satellite data, demonstrating how remote sensing and AI can support coastal monitoring, environmental management, and climate change impact assessment.
# Shoreline Erosion and Accretion Analysis in the Niger Delta Using Satellite Imagery and Machine Learning
## 📌 Project Overview
Coastal erosion and accretion are major environmental challenges in the **Niger Delta region of Nigeria**, driven by sea-level rise, wave action, sediment transport, and human activities such as oil and gas exploration.
This project demonstrates a **Machine Learning–based approach** for analyzing shoreline erosion and accretion using **synthetic satellite-derived data**. The workflow is designed to closely mimic real-world remote sensing applications and can be easily extended to real datasets such as **Landsat** or **Sentinel imagery**.
---
## 🎯 Objectives
- Simulate multi-temporal shoreline positions for the Niger Delta
- Identify shoreline **erosion**, **stability**, and **accretion** zones
- Train a Machine Learning model to classify shoreline change patterns
- Visualize shoreline dynamics spatially
- Provide a reproducible framework adaptable to real satellite data
---
## 🧠 Methodology
1. **Synthetic Data Generation**
- Shoreline positions are simulated on a 2D grid representing satellite imagery.
- Temporal shoreline movement is modeled to reflect erosion and accretion trends.
2. **Shoreline Change Detection**
- Shoreline displacement between years (2005–2025) is computed.
- Changes are classified as:
- `-1` → Erosion
- `0` → Stable
- `1` → Accretion
3. **Machine Learning Modeling**
- A **Random Forest Classifier** is trained using shoreline position data.
- The model predicts erosion and accretion zones.
4. **Visualization**
- Shoreline positions
- Observed erosion/accretion
- ML-predicted shoreline change maps
---
## 📂 Project Structure
Shoreline-Erosion-Niger-Delta-ML/
│
├── shoreline_erosion_ml.py # Main Python script
├── Niger_Delta_Synthetic_Shoreline_Data.xlsx
│ ├── Shoreline_2005
│ ├── Shoreline_2025
│ └── Erosion_Accretion_Labels
├── README.md
yaml
Copy code
---
## 📊 Dataset Description
The project uses **synth …
Similaires
Nelvinebi/Land-Use-Land-Cover-Change-Analysis-in-the-Niger-Delta-Using-Multi-Temporal-Satellite-Data-and-MLNelvinebi/Sediment-Transport-and-Coastal-Morphodynamics-Modeling-in-the-Niger-Delta-Using-MLNelvinebi/Mangrove-Degradation-and-Health-Assessment-in-the-Niger-Delta-Using-NDVI-NDWI-and-MLNelvinebi/Water-Quality-Index-Prediction-for-Niger-Delta-Rivers-Using-ML-and-XGBoostNelvinebi/Harmful-Algal-Bloom-Prediction-in-Niger-Delta-Coastal-Waters-Using-Remote-Sensing-and-MLNelvinebi/Climate-Change-Impact-on-Coastal-Ecosystems-in-the-Niger-Delta-Using-ML-Based-Models