Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir

By Mohammad Valipour, M. E. Banihabib, S. M. Behbahani. 2013

829 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Environmental Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/7a00a6ac029ba9adc86034bbce8650385fc9be99

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

1. Hamid Moeeni
2. Hossein Bonakdari
3. Kwok-Wing Chau
4. Amir Mosavi
5. Pınar Öztürk
6. Hristos Tyralis
7. Demetris Koutsoyiannis
8. Georgia A. Papacharalampous

In Environmental Engineering

1. Hossein Bonakdari
2. Kwok-Wing Chau
3. Ozgur Kisi
4. Amir Mosavi
5. Pınar Öztürk
6. Hamid Moeeni
7. Anurag Malik
8. Quoc Bao Pham

In Water Science and Technology

1. Hossein Bonakdari
2. Amir Mosavi
3. Pınar Öztürk
4. Kwok-Wing Chau
5. Ozgur Kisi
6. Anurag Malik
7. Hamid Moeeni
8. Quoc Bao Pham

Citations in the corpus

• Predictability of monthly temperature and precipitation using automatic time series forecasting methods (2018, methodology): “Still, it has been frequently used in hydrology (e.g. Papacharalampous et al. 2017a, b, c; Singh et al. 2011; Tyralis and Koutsoyiannis 2014; Valipour et al. 2013).”
• Forecasting monthly inflow with extreme seasonal variation using the hybrid SARIMA-ANN model (2017, methodology): “To examine the differences between linear and nonlinear methods, some researchers compared these two methods in predicting hydrological phenomena such as inflow (Mohammadi et al. 2005; Firat 2008; Fereydooni et al. 2012; Golabi et al. 2013; Pektas and Cigizoglu 2013; Valipour et al. 2013; Khatibi et”
• Kumaraswamy autoregressive moving average models for double bounded environmental data (2017, background): “Most time series appearing in natural sciences, including hydrology, climatology and environmental applications consist of observations that are serially dependent overtime (Salas et al., 1997; Machiwal and Jha, 2012; Lohani et al., 2012; Valipour et al., 2013).”
• Development and Evaluation of the Combined Machine Learning Models for the Prediction of Dam Inflow (2020, methodology): “In particular, many typical time series models are used for hydrologic predictions, such as autoregressive–moving-average (ARMA) and autoregressive integrated moving average (ARIMA), based on regression [11].”
• Support vector regression optimized by meta-heuristic algorithms for daily streamflow prediction (2020, methodology): “The moving average (MA) model, auto regressive (AR) model, auto regressive moving average (ARMA) model, and auto regressive integrated moving average (ARIMA) model are linear models that have received wide application in hydrological time-series forecasting (Wu et al. 2009; Wu and Chau 2010; Valipou”
• Flood Prediction Using Machine Learning Models: Literature Review (2018, methodology): “Back-percolation learning algorithms are used to individually calculate the propagation error in hidden network nodes for a more advanced modeling approach.”
• Optimization of neural networks for precipitation analysis in a humid region to detect drought and wet year alarms (2016, methodology): “Banihabib et al. (2012) and Valipour et al. (2012a, 2012b, 2013) forecasted the inflow using ARMA, Auto regressive integrated moving average (ARIMA) and ANN models.”