Akajiaku11/Flood-Susceptibility-Mapping-Using-Machine-Learning-and-Morphometric-Analysis

This project integrates Machine Learning (ML) techniques and morphometric analysis to assess flood susceptibility across four major catchments in Bayelsa State, Nigeria. The study leverages Shuttle Radar Topographic Mission (SRTM) data to extract hydrological features and applies machine Here is a README template you can add to your repository for this project: --- # Flood Susceptibility Mapping Using Machine Learning and Morphometric Analysis ## Overview This project integrates **Machine Learning (ML)** techniques and **morphometric analysis** to assess flood susceptibility across four major catchments in Bayelsa State, Nigeria. The study leverages **Shuttle Radar Topographic Mission (SRTM)** data to extract hydrological features and applies machine learning classifiers (Random Forest, Support Vector Machine, XGBoost) to classify flood-prone zones. The goal is to enhance flood prediction accuracy and granularity beyond traditional morphometric methods. ## Dataset The dataset used for this analysis includes key morphometric features, such as: - Drainage Density (Dd) - Stream Frequency (Fs) - Relief Ratio (Rh) - Bifurcation Ratio (Rbm) - Infiltration Number (If) The flood-prone labels are generated from historical flood records, with each catchment marked as either **flood-prone (1)** or **not flood-prone (0)**. You can access the dataset template for training in `morphometric_ml_dataset.csv`. ## Requirements Ensure you have the following Python packages installed: - `pandas` - `numpy` - `sklearn` - `matplotlib` - `seaborn` - `xgboost` You can install them via `pip`: ```bash pip install pandas numpy scikit-learn matplotlib seaborn xgboost ``` ## File Structure ``` ├── flood_susceptibility_model.py # Python script for model training and evaluation ├── morphometric_ml_dataset.csv # Dataset template for morphometric features ├── ROC_Comparison.png # ROC Curve comparison figure ├── XGBoost_Confusion_Matrix.png # Confusion matrix for XGBoost model ├── XGBoost_Feature_Importance.png # Feature importance plot for XGBoost model └── README.md # Project overview and instructions ``` ## Usage ### 1. Prepare the Data Load your dataset in `morphometric_ml_dataset.csv`, ensuring the dataset includes morphom …