Forecasting accuracy: a comparative study between artificial neural network and autoregressive model for streamflow

Abstract

Estimating the reliability of potential prediction is very crucial as our life depended heavily on it. Thus, a simulation that concerned hydrological factors such as streamflow must be enhanced. In this study, Autoregressive (AR) and Artificial Neural Networks (ANN) were used. The forecasting result for each model was assessed by using various performance measurements such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Forecast Error (MFE) and Nash-Sutcliffe Model Efficiency Coefficient (CE). The experimental results showed the forecast performance of Durian Tunggal reservoir datasets by using ANN Model 7 with 7 hidden neurons has better forecast performance compared to AR (4). The ANN model has the smallest MAE (0.0116 m<sup>3</sup>/s), RMSE (0.0607 m<sup>3</sup>/s), MAPE (1.8214% m<sup>3</sup>/s), MFE (0.0058 m<sup>3</sup>/s) and largest CE (0.9957 m<sup>3</sup>/s) which show the capability of fitting to a nonlinear dataset. In conclusion, high predictive precision is an advantage as a proactive or precautionary measure that can be inferred in advance in order to avoid certain negative effects.

Deep learning approach analysis model prediction and classification poverty status

“…The same explanation also explains that ANN is a method capable of developing a classification model with a fairly good performance and level of accuracy [25]. The performance of ANN is able to present a very good performance in the prediction process [26], [27]. In addition to ANN, the SVM method is also an alternative method for conducting classification analysis.…”