Akajiaku11/Machine-Learning-Based-Shoreline-Change-Prediction-and-Erosion-Analysis-A-Case-Study-of-Ogu-Bolo

This study examines shoreline change and erosion dynamics along the Ogu/Bolo coastline in Nigeria’s Niger Delta from 1984 to 2024, with predictive projections to 2044. The objective was to quantify past shoreline shifts, identify key environmental and anthropogenic drivers, and develop accurate predictive models for future shoreline positions Historical-Trends-and-Forecast-Future-Shoreline-Changes-1974-2024 using Satellites with Machine Learning in the Niger Delta Overview The Niger Delta, one of the world's largest river deltas, has been experiencing significant shoreline changes over the past several decades due to various environmental and anthropogenic factors. These changes are critical to understand for sustainable coastal management and environmental conservation in this highly dynamic and ecologically sensitive region. In this project, we aim to analyze historical shoreline trends from 1974 to 2024 and forecast future changes using satellite imagery and machine learning techniques. By integrating multi-temporal satellite data with advanced machine learning algorithms, we can model the rate of shoreline change and predict future trends with high accuracy. This README.md provides a detailed overview of the objectives, methodology, and data used in this project. Project Objectives Historical Shoreline Analysis: To assess the historical changes in the Niger Delta shoreline from 1974 to 2024 using multi-temporal satellite imagery. Shoreline Change Detection: To analyze the spatial-temporal patterns of shoreline erosion and accretion using machine learning models. Future Shoreline Forecasting: To predict future shoreline changes using time series data from historical satellite imagery and predictive machine learning algorithms. Erosion and Accretion Drivers: To identify key factors driving shoreline changes, including natural processes (e.g., sediment deposition, sea-level rise) and human activities (e.g., oil exploration, deforestation). Policy Implications: To provide insights that can inform coastal management policies aimed at mitigating erosion and promoting sustainable development. Methodology 1. Data Collection Satellite Imagery The primary data source for this analysis consists of satellite imagery from various platforms, covering the period from 1974 to 2024: Landsat (1974–2023): We use …