Akajiaku11/Using-GIS-and-machine-learning-to-monitor-sandbars-along-the-Niger-River-in-the-Niger-Delta-Nigeria.

This project uses Geographic Information Systems (GIS) and machine learning to monitor sandbars along the Niger River in Nigeria's Niger Delta. By analyzing satellite imagery and spatial data, the study identifies and tracks the formation, movement, and erosion of sandbars. # Using-GIS-and-machine-learning-to-monitor-sandbars-along-the-Niger-River-in-the-Niger-Delta-Nigeria. This repository contains the workflow and analysis for detecting changes in sandbar areas over time using a combination of Geographic Information Systems (GIS) and statistical methods, augmented by machine learning for deeper insights. --- ## **Table of Contents** 1. Introduction 2. Objectives 3. Data Description 4. Methodology 5. Results 6. Future Work 7. How to Use 8. References --- ## **Introduction** Sandbars are dynamic landforms that change due to natural processes like sedimentation and erosion. Monitoring their changes is essential for understanding ecological dynamics and planning coastal management strategies. This project applies change detection analysis using GIS datasets and machine learning techniques. --- ## **Objectives** - Analyze changes in sandbar areas over different years. - Identify correlations and trends between time periods. - Apply statistical tests and machine learning to quantify and predict changes. - Visualize findings for enhanced interpretability. --- ## **Data Description** The dataset consists of sandbar area measurements (in arbitrary units) for the years: - 1974 - 1984 - 1994 - 2004 - 2014 - 2024 **Sample Data Representation:** | Year | Values (Sample) | |--------|----------------------------------| | 1974 | 2.22, 24.15, 4.39, ... | | 1984 | 544.63, 2.07, 240.42, ... | | ... | ... | | 2024 | 19.31, 10.51, 4.75, ... | The data has been preprocessed for statistical analysis and visualization. --- ## **Methodology** The analysis workflow comprises several steps: ### 1. **Descriptive Statistics** - **Purpose**: Summarize key statistics such as mean, median, and variability. - **Code**: ```python desc_stats = df.describe() print(desc_stats) ``` --- ### 2. **Change Detection Analysis** - **Purpose**: Compute differences in sandbar …