Machine Learning-Based Flood Probability Prediction System: A Complete Practice from Data Exploration to Model Optimization

The original project was published on GitHub by s26-redi-ml-ai (Project title: Machine-Learning-for-flood-Probability-Prediction, Link: https://github.com/s26-redi-ml-ai/Machine-Learning-for-flood-Probability-Prediction, Release date: June 14, 2026). This project uses environmental factors and infrastructure data, based on a Kaggle competition dataset. Through exploratory data analysis, feature engineering, and comparison of models such as Ridge regression, Random Forest, XGBoost, and Multi-Layer Perceptron (MLP), it finally builds a high-precision flood probability prediction system to provide data support for risk management in the insurance industry.

Floods are one of the natural disasters causing the most severe economic losses globally. Traditional risk assessment methods struggle to capture the complex non-linear relationships between environmental factors and infrastructure, and machine learning technology can make up for this deficiency. Starting from the actual needs of the insurance industry, this project builds a complete machine learning regression model system based on the Kaggle Playground Series Season 4 Episode 5 flood prediction dataset to predict flood occurrence probability in specific areas, facilitating data science applications in the field of natural disaster risk management.

The dataset includes multi-dimensional numerical features such as environmental, geographical, and infrastructure data. The task is to predict continuous flood probability values between 0 and 1 (a regression task). Fine-grained probability predictions (e.g., 0.12 for low risk, 0.54 for medium risk, and 0.89 for high risk) provide insurance companies with richer risk information, supporting precise risk stratification and differentiated pricing.

1. Exploratory Data Analysis: Analyze the distribution of target variables (skewness, abnormal patterns) and variable outliers; perform correlation analysis using heatmaps to identify variable correlations and multicollinearity, guiding feature engineering;
2. Feature Engineering: Construct composite risk scores, interaction features, and comprehensive risk scores to enhance the model's predictive ability;
3. Model Selection and Training: Compare Ridge regression (baseline, strong interpretability), Random Forest (captures non-linear relationships), XGBoost (core optimized model, with 50 rounds of hyperparameter optimization via Optuna), and Multi-Layer Perceptron (MLP, did not outperform tree models); XGBoost performed best on structured data.

Root Mean Squared Error (RMSE, measures prediction error) and R² score (measures variance explanation ability) are used to evaluate model performance, and five-fold cross-validation is implemented to ensure reliable results. Evaluation results: XGBoost had the most accurate predictions, followed by Random Forest; Ridge regression had strong interpretability, and neural networks did not outperform tree models.

Value to the insurance industry:

• Risk Assessment: Identify high-risk areas and understand the geographical risk distribution of underwriting portfolios;
• Underwriting Support: Provide data-driven decision-making basis for underwriters to improve underwriting quality;
• Pricing Strategy: Support differentiated premium pricing based on flood probability stratification;
• Portfolio Management: Monitor risk exposure in flood-prone areas and proactively mitigate risks before disasters.

This project fully demonstrates the entire machine learning workflow (data exploration → feature engineering → model training → evaluation), and builds a high-performance model through systematic method comparison and rigorous validation. The success of the project lies in the combination of technology and insurance business needs, providing a reference methodological framework for the development of insurance technology and risk assessment of other natural disasters.