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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Shuhaida Ismail
Faculty of Applied Scienced and Technology, Universiti Tun Hussein Onn Malaysia
Computer Science & IT Engineering

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Forecasting accuracy: a comparative study between artificial neural network and autoregressive model for streamflow Wan Nur Hawa Fatihah Wan Zurey; Shuhaida Ismail; Aida Mustapha
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 3: September 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (694.714 KB) | DOI: 10.11591/ijai.v9.i3.pp464-472

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 m3/s), RMSE (0.0607 m3/s), MAPE (1.8214% m3/s), MFE (0.0058 m3/s) and largest CE (0.9957 m3/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.
Comparison of daily rainfall forecasting using multilayer perceptron neural network model Mazwin Arleena Masngut; Shuhaida Ismail; Aida Mustapha; Suhaila Mohd Yasin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 3: September 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (651.353 KB) | DOI: 10.11591/ijai.v9.i3.pp456-463

Abstract

Rainfall is important in predicting weather forecast particularly to the agriculture sector and also in environment which gives great contribution towards the economy of the nation. Thus, it is important for the hydrologists to forecast daily rainfall in order to help the other people in the agriculture sector to proceed with their harvesting schedules accordingly and to make sure the results of their crops would be satisfying. This study is set to forecast the daily rainfall future value using ARIMA model and Artificial Neural Network (ANN) model. Both method is evaluated by using Mean Absolute Error (MAE), Mean Forecast Error (MFE), Root Mean Squared Error (RMSE) and coefficient of determination (R ). The results showed that ANN model has outperformed results than ARIMA model. The results also showed ANN has under-forecast the daily rainfall data by 2.21% compare to ARIMA with over-forecast of -3.34%. From this study, it shows that the ANN (6,4,1) model produces better results of MAE (8.4208), MFE (2.2188), RMSE (34.6740) and R (0.9432) compared to ARIMA model. This has proved that ANN model has outperformed ARIMA model in predicting daily rainfall values.
Co-Authors Aida Mustapha Mazwin Arleena Masngut Suhaila Mohd Yasin Wan Nur Hawa Fatihah Wan Zurey