Flood Prediction using Machine Learning
Flooding is one of the most catastrophic natural disasters, causing significant damage to property, infrastructure, and human life. Accurate flood prediction models can assist in disaster preparedness and early warning systems. In this report, we implement and evaluate several machine learning models to predict flood occurrences based on historical weather and environmental data. The models evaluated include Support Vector Regression (SVR), Decision Tree, Multilayer Perceptron (MLP), and Linear Regression.
Dataset
Flood detection refers to the process of identifying, monitoring, and alerting authorities or individuals about the presence or likelihood of flooding in a particular area. It involves the use of various technologies and methods to detect, predict, and mitigate the impacts of floods.
Results
: Model Comparison Best Performance: The MLP model exhibited the best overall performance on the flood prediction task, with an R² score of 0.9043 and the lowest RMSE of 0.0155. This suggests that it effectively captures the relationships in the data and makes accurate predictions. Linear Regression: Although the Linear Regression model has an R² score of 1.0, this may be due to overfitting on the training data. Further analysis should be conducted on unseen data to confirm its validity. SVR: The SVR model also performed well, with an R² score of 0.7079, indicating reasonable predictive power, though it is not as strong as the MLP. Decision Tree: The Decision Tree model had the lowest performance, with an R² score of 0.1249, suggesting that it struggles to predict flood occurrences accurately with this dataset. :Limitations Data Imbalance: If the dataset is imbalanced (more "No Flood" events than "Flood" events), the models may struggle with accurate flood prediction. Overfitting: The near-perfect performance of the Linear Regression model may indicate overfitting. Cross-validation and testing on separate datasets could help verify its generalization ability. Non-linear Relationships: MLP and SVR performed better than linear models, suggesting that non-linear relationships exist in the dataset. These complex relationships may require deeper models for accurate predictions.
