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kennethnyangweso/Flood-Risk-Analysis-with-GIS

Domaine:

geospatialenvironment and energy

Type de record:

project
Créateur:
ken
HĂ´te:
A machine learning system that leverages GIS data to predict flood risk areas in Kenya # 🌍 Flood Risk Prediction System (Kenya) ## 📌 Business Understanding Flooding is a major environmental and socio-economic challenge in Kenya, impacting infrastructure, agriculture, and human safety. Traditional flood risk assessment methods often lack integration of diverse geospatial factors. In this project, I built a machine learning-driven system that leverages GIS data to predict flood risk, enabling stakeholders such as urban planners, disaster response teams, and policymakers to make informed decisions. --- ## 📖 Project Overview This project integrates **Geographic Information Systems (GIS)** and **Machine Learning** to assess flood risk across Kenya. I implemented two modeling approaches: - **Classification** → Predict flood risk categories (Low, Moderate, High) - **Regression** → Predict a continuous flood risk score The dataset was engineered from multiple geospatial data sources using: - `GeoPandas` - `Rasterio` - `WhiteboxTools` --- ## ❗ Problem Statement Flood risk prediction is complex due to the interaction of environmental, topographic, and human-related factors. This project aims to: - Quantify flood risk using a numerical score - Categorize regions into actionable risk levels - Leverage spatial data for predictive modeling --- ## 🎯 Objectives - Develop a **Flood Risk Score** - Classify areas into **risk categories** - Compare **classification vs regression approaches** - Improve model performance using **ensemble techniques (Voting & Stacking)** - Deploy a working prediction system --- ## 📊 Metrics of Success ### Classification - Accuracy (75%) - F1 Score (75%) - Confusion Matrix ### Regression - R² Score (70%) - Mean Absolute Error (MAE) (0.5) - Mean Squared Error (MSE) (0.5) - Residual Analysis (Residual Plots) --- ## 📂 Data Understanding ### 📌 Dataset Features 1. **county** - The administrative region in Kenya where the data point is located. This categorical feature enables geographic grouping and regional analysis of flo …