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In this paper, a recursive training procedure with forgetting factor is proposed for on-line calibration of temporal neural networks. The forgetting factor discounts old measurements through an on-line model calibration. The forgetting factor approach enables the recursive algorithm to reduce the effect of the older error data by multiplying the error data by a discounting factor. The proposed procedure is used to calibrate a temporal neural network for reservoir inflow modeling. The mean monthly inflow of the Karoon-III reservoir dam in the south-western part of Iran is used to test the performance of the proposed approach. An autoregressive moving average (ARMA) model is also applied to the same data. The temporal neural network, which is trained with the proposed approach, has shown a significant improvement in the forecast accuracy in comparison with the network trained by the conventional method. It is also demonstrated that the neural network trained with forgetting factor results in better forecasts compared to the statistical ARMA model, which has been calibrated through this approach. [ABSTRACT FROM AUTHOR]

Highlights: [•] This paper addresses a new clustering-classification method for classifying the severity of road accidents in Iran. [•] k-Means and some classification methods are used for running numerical experiments. [•] Some activation functions have been used for training the neural networks. The results of them are compared together. [•] Also, the accuracy of classification is compared by using different approaches in k-mean. [•] Silhouette technique was used to confirm the validity of the clusters. [Copyright &y& Elsevier]