A Stacking Ensemble Machine Learning Model for Emergency Call Forecasting

One of the greatest challenges of Emergency medical services providers is to handle the large number of Emergency Medical Service (EMS) calls coming from the population. An accurate forecast of EMS calls is involved in ambulance fleet dispatching and routing to minimize response times to emergency calls and enhance the efficacy of assistance. Yet, the demand for emergency services exhibits significant variability, posing a challenge in accurately predicting the future occurrence of emergency calls and their spatial-temporal distribution. Here, we propose a stacking ensemble machine learning model to forecast EMS calls, combining different base learners to enhance the overall performance of generalization. Additionally, we conducted experiments using Boruta, Lasso, RFFI and SHAP feature selection methods to identify the most informative attributes from the EMS dataset. The proposed ensemble model integrates a base layer and a meta layer. In the base layer, we applied four base learners: Decision Tree, Gradient Boosting Regression Tree, Light Gradient Boosting Machine and Random Forest. In the meta layer, we used an optimized Random Forest model to integrate the outputs of base learners. We evaluate the performance of our proposed model using the R2 -score and four different error metrics. Based on a real data set including spatial, temporal and weather features, the findings of this study demonstrated that the proposed stacking-based ensemble model showed a better score and the minimum errors compared to the traditional single algorithms, online machine learning methods and voting ensemble methods. We achieved a higher score of 0.9954, mse of 0.8938, rmse of 0.9454, mae of 0.2923 and mape of 0.0724 compared to state-of-the-art models. This work is an aid for emergency managers in making well-informed decisions, improving outcomes for ambulance dispatch and routing, and enhancing ambulance response time.