Abstract
This study examined the applicability of artificial neural network models in modelling univariate time series ambulance demand for short-term forecasting horizons in Zimbabwe. Bulawayo City Councils’ ambulance services department was used as a case study. Two models, feed-forward neural network (FFNN) and seasonal autoregressive integrated moving average, (SARIMA) were developed using monthly historical data from 2010 to 2017 and compared against observed data for 2018. The mean absolute error (MAE), root mean square error (RMSE), and paired sample t-test were used as performance measures. Calculated performance measures for FFNN were MAE (94.0), RMSE (137.19), and the test statistic value p=0.493(>0.05) whilst corresponding values for SARIMA were 105.71, 125.28, and p=0.005(<0.05), respectively. Findings of this study suggest that the FFNN model is inclined to value estimation whilst the SARIMA model is directional with a linear pattern over time. Based on the performance measures, the parsimonious FFNN model was selected to predict short-term annual ambulance demand. Demand forecasts with FFNN for 2019 reflected the expected general trends in Bulawayo. The forecasts indicate high demand during the months of January, March, September, and December. Key ambulance logistic activities such as vehicle servicing, replenishment of essential equipment and drugs, staff training, leave days scheduling, and mock drills need to be planned for April, June, and July when low demand is anticipated. This deliberate planning strategy would avoid a dire situation whereby ambulances are available but without adequate staff, essential drugs, and equipment to respond to public emergency calls.
Highlights
Artificial neural networks are currently receiving a huge amount of interest and used in areas of pattern recognition, classification, clustering, and forecasting applications [1]
The root mean square error (RMSE) and mean absolute error (MAE) were used as final performance measures for selecting a suitable model for the neural network and the results are summarised in Tables 2 and 3, respectively
Performance measures, MAE and the paired sample t-test, indicate that the feed-forward neural network (FFNN) models are superior to traditional SARIMA models in time series prediction of ambulance demand in the city of Bulawayo, over a short-term forecasting horizon
Summary
Artificial neural networks are currently receiving a huge amount of interest and used in areas of pattern recognition, classification, clustering, and forecasting applications [1]. Predictions for ambulance demand can be done for the few days in a week, a month, or a full calendar year based on time-ordered historical data for planning purposes. Such forecast will help in the mobilisation of both human and equipment resources. [4] alluded that response time, which is the time taken to reach the patient after an emergency call has been received, is a critical component in EMS as it might mean the difference between life and death of a patient This calls for robust and smart planning in ensuring that a skilled manpower and well-serviced equipment, including ambulances, are available to respond. The prediction of future demand using the Journal of Applied Mathematics historical time series by applying neural networks will play an integral role in the strategic and planning process
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