Nelvinebi/Harmful-Algal-Bloom-Prediction-in-Niger-Delta-Coastal-Waters-Using-Remote-Sensing-and-ML
Domaine:
environment and energygeospatialCréateur:
Nel
Hôte:
Machine learning–driven prediction of harmful algal blooms in Niger Delta coastal waters using synthetic remote sensing and water quality data, demonstrating an early warning framework for coastal ecosystem monitoring, environmental risk assessment, and decision support in data-scarce regions.
Harmful Algal Bloom Prediction in Niger Delta Coastal Waters
Using Remote Sensing and Machine Learning
📌 Project Overview
This project develops a machine learning–based framework for predicting Harmful Algal Bloom (HAB) events in Niger Delta coastal waters using synthetic satellite-derived and water quality data. It demonstrates how remote sensing indicators and environmental variables can support early warning systems for coastal ecosystem management.
🎯 Objectives
Simulate realistic coastal water quality and oceanographic data
Model HAB occurrence using machine learning techniques
Identify key environmental drivers of algal blooms
Demonstrate an early warning decision-support workflow
🧠 Methodology
Synthetic data generation inspired by satellite ocean-color products
Feature scaling and stratified train–test split
Random Forest classification for HAB prediction
Model evaluation using precision, recall, F1-score, and confusion matrix
Feature importance analysis for environmental interpretation
📂 Project Structure
├── harmful_algal_bloom_prediction_ml.py
├── harmful_algal_bloom_niger_delta_dataset.xlsx
├── README.md
📊 Dataset Description
The dataset includes the following variables:
Sea Surface Temperature (°C)
Chlorophyll-a concentration (mg/m³)
Turbidity (NTU)
Colored Dissolved Organic Matter (CDOM index)
Nitrate concentration (mg/L)
Phosphate concentration (mg/L)
HAB event label (0 = No Bloom, 1 = Bloom)
Note: The dataset is fully synthetic and intended for research demonstration and modeling practice.
⚙️ Requirements
Python 3.8+
NumPy
Pandas
Scikit-learn
Matplotlib
Install dependencies using:
pip install numpy pandas scikit-learn matplotlib
▶️ How to Run
python harmful_algal_bloom_prediction_ml.py
The script will:
Generate synthetic data
Train a Random Forest model
Evaluate performance
Display feature importance
Simulate an HAB early warning scenario
🌍 Applications
Coastal water quality monitoring
Fisheries and aquaculture …
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