IMPROVING OF AN ARTIFICIAL NEURAL NETWORKS FORECASTING MODEL FOR DETERMINING OF THE NUMBER OF CALLS IN 112 EMERGENCY CALL CENTER

Forecasting studies are extremely important in the technical, social and economic research. generally, we know it is very difficult to forecast with higher accurate about a system by using recent values. in the scientific literature, the forecasting studies of energy, personnel planning, production planning, climate changes, sales and marketing and economics etc. are frequently found. in this paper, for an emergency calls center in isparta province of turkey an artificial neural network (ann) forecasting model was developed to determine the number of calls for as health, fire and security services on a pilot implementation of the emergency calls center on a single number 112. in the developed model, the gradient descent with adaptive learning and momentum (gdx) algorithm is selected as the training algorithm with feed-forward back-propagation by using 80% of input data and the 20% of input data is used for testing set data from last month. after the testing, the mean absolute percentage error (mape) rate is obtained as 4.5% and it is useful to test. in addition, the forecasting results of the next month are shown that the mape values are 2.65%, 6.40% and 5.24% with ann, trend analysis and arima (1 1 1) models respectively and, the number of calls are found separately on the types of calls in daily. consequently, the developed model by using ann to forecast the number of calls in an emergency call center is more accurate than the trend analysis and arima models.