Nelvinebi/Harmful-Algal-Bloom-Prediction-in-Niger-Delta-Coastal-Waters-Using-Remote-Sensing-and-ML

Domain:

environment and energygeospatial

Creator:

Nel

Host:

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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