## 5 Results
### Performance Comparison of Random Forest and LSTM
As an ensemble of decision trees, the RF model effectively captures interactions between variables and handles data variability. Decision trees excel at processing heterogeneous data and managing nonlinearity; characteristics often present in flood risk data. Moreover, RF is less sensitive to overfitting issues than LSTM, thanks to its ensemble nature, which averages predictions from many trees. Another significant advantage of the RF model is its robustness and stability. Decision trees, the foundation of the RF model, are adept at handling noise and outliers in data. By  
Figure 15: Comparative Metrics RF vs. LSTM. combining predictions from multiple trees, the RF model further mitigates the impact of anomalies, making it particularly suitable for complex and noisy datasets like those used in flood risk forecasting. RF's versatility in handling different variables, be they categorical or numeric, without the need for complex transformations simplifies data preprocessing. This flexibility allows for a broader range of variables to be included in the model, enhancing its interpretability. The decisions made by the trees in the RF model are easily understandable, facilitating the comprehension of the relationships between variables and their impacts on predictions, thereby increasing the model's transparency. From a computational standpoint, the RF model is generally more efficient than the LSTM. Despite its complexity, implementing RF typically requires less computational resources and training time than LSTM, which demands larger datasets and more resources to train effectively. This makes RF a more practical and cost-effective choice for numerous applications. On the other hand, while the LSTM model is robust in capturing time dependencies in data, its limitations can hinder its performance. LSTM may be more prone to overfitting, particularly with smaller datasets or highly correlated variables. Furthermore, the complexity of LSTM can complicate hyperparameter optimization, adversely affecting its ability to generalize well on test data. In conclusion, the RF model is the superior choice for flood risk forecasting. Its ability to handle variability, robustness, ease of interpretation, and computational efficiency contribute to its superior performance over the LSTM model. This underscores the importance of selecting the right model based on the data's characteristics and the analysis's objectives. A combined model could leverage Random Forest's robustness and interpretability while utilizing LSTM's ability to manage long-term dependencies, leading to more accurate predictions. This combined approach could improve forecast accuracy and provide a more comprehensive understanding of flood risk dynamics. The integration of the two models could be achieved through ensemble learning techniques, where the predictions of both models are combined for a more robust final result. Additionally, employing stacking methods could enable predictions from one model to serve as input for the other, enhancing overall performance. This perspective represents an exciting opportunity for research and development in flood risk forecasting, with the potential to deliver more effective and reliable tools for risk management and mitigation.