A hybrid neural network and ARIMA model for water quality time series prediction

By Durdu Ömer Faruk. Engineering applications of artificial intelligence, 2010

677 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Environmental Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/802fa023451bdf9a753bbe309b176bd457246f46

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In Electrical and Electronic Engineering

In Environmental Engineering

In Water Science and Technology

Citations in the corpus

Deep Learning for Time-Series Analysis (2017, methodology): “[31] presents a model for Time-Series forecasting using ANN and ARIMA models, and [15] applies the same kinds of models to water quality time series prediction.”
A Rainfall‐Runoff Model With LSTM‐Based Sequence‐to‐Sequence Learning (2020, result): “In the last few decades, several studies (Chang et al., 2015; Ömer Faruk, 2010; Sajikumar & Thandaveswara, 1999) have reported that the performances of ANN models are comparable to those of physical models, and they work well in areas with limited data (Ghumman et al., 2011).”
Forecasting monthly inflow with extreme seasonal variation using the hybrid SARIMA-ANN model (2017, methodology): “Various hybrid models have been used by other researchers in different cases (Koutroumanidis et al. 2009; Faruk 2010; Aladag et al. 2012; Martins and Werner 2012; Jeong et al. 2014; Ruiz-Aquilar et al. 2015).”
Comparison of Forecasting Energy Consumption in Shandong, China Using the ARIMA Model, GM Model, and ARIMA-GM Model (2017, background): “Its specific form can be expressed as ARIMA (p, d, q), where ‘p’ represents the order of autoregressive processes, ‘d’ represents the order of difference, and ‘q’ represents the order of the moving average process [45,46].”
Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling (2014, background): “Gardner and Dorling (1998) reviewed the limits and problems associated with the training of multi-layer perceptron (MLP) ANNs and emphasized that fundamental understanding of the basic theory is a key in developing ANNs.”
Application of Artificial Neural Networks, Support Vector Machine and Multiple Model-ANN to Sediment Yield Prediction (2020, background): “…Meshram et al. 2019a), rainfall forecasting (Chiang et al. 2004; Nasseri et al. 2008a, 2008b; Tao et al. 2018;Mirabbasi et al. 2019), suspended sediment prediction (Alp and Cigizoglu 2007; Kisi and Shiri 2012; Meshram et al. 2019b), andwater quality management (Palani et al. 2008; Faruk 2010).”