Dogiye12/Integrating-Morphometric-Controls-for-Runoff-Dynamics-in-Bayelsa-State-Nigeria-Enhancing-Flood-Sus

Develop predictive models that integrate rainfall, DEM, and land-use data to map areas at risk of flooding and support early-warning systems. 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 …