A Comparative Study of Autoregressive, Autoregressive Moving Average, Gene Expression Programming and Bayesian Networks for Estimating Monthly Streamflow

Mehdizadeh, Saeid; Sales, Ali Kozekalani

doi:10.1007/s11269-018-1970-0

Cited by 41 publications

(11 citation statements)

References 32 publications

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“…The results achieved in the present work (i.e., a much better performance of the hybrid machine learning-time series models compared to the stand-alone machine learning models) are in agreement with the outcomes of previous works [11,12,[39][40][41][42][43][44]. The different hybrid models were developed by the authors via coupling the various machine learning and time series models to estimate the hydrological and meteorological data and reported the higher accuracies of hybrid models than the stand-alone machine learning models.…”

Section: Discussionsupporting

confidence: 92%

Estimating the short-term and long-term wind speeds: implementing hybrid models through coupling machine learning and linear time series models

Mehdizadeh

Sales²,

Safari

2020

SN Appl. Sci.

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Wind speed data are of particular importance in the design and management of wind power projects. In the current study, three types of linear time series models including autoregressive (AR), moving average (MA), and autoregressive moving average (ARMA) were employed to estimate short-term (i.e., daily) and long-term (i.e., monthly) wind speeds. The required data were gathered, respectively, from the Tabriz and Zahedan stations in the northwest and southeast of Iran. The MA models outperformed the AR and ARMA on the both daily and monthly scales. Daily and monthly wind speed values, as a function of lagged wind speed data, were then estimated using two machine learning models of random forests (RF) and multivariate adaptive regression splines (MARS). It was found that the RF and MARS provided similar results; however, RF performed slightly better than the MARS. Finally, the stand-alone time series and machine learning models were coupled to improve the accuracy of the wind speed estimation. Accordingly, the hybrid RF-AR, RF-MA, RF-ARMA, MARS-AR, MARS-MA, and MARS-ARMA models were implemented. It was concluded that, the hybrid models outperformed the stand-alone RF and MARS for both short-and long-term wind speed estimations where, the RF-AR and MARS-AR hybrid models provided the best performances. The hybrid models tested in the present study could be effective alternatives to the stand-alone machine learning-based RF and MARS models for the estimation of wind speed time series.

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Section: Discussionsupporting

confidence: 92%

Estimating the short-term and long-term wind speeds: implementing hybrid models through coupling machine learning and linear time series models

Mehdizadeh

Sales²,

Safari

2020

SN Appl. Sci.

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“…Likewise, Tabari et al (2015) reported better performance of an AI-based model including the MLP using the antecedent ST data for modelling the ST at deeper layers than the surface layers. Furthermore, the results of the current study verify the outcomes of previous studies such as Mehdizadeh (2018b), Mehdizadeh and Kozekalani Sales (2018), Mehdizadeh et al (2017cMehdizadeh et al ( , 2018bMehdizadeh et al ( , 2019, and Fathian et al (2019). The authors developed different types of the hybrid models through hybridization of the different time-series-and AI-based models for improving the modelling efficiency of classical models in modelling hydrological variables.…”

Section: Performance Evaluation Of the Classical And Hybrid Models supporting

confidence: 91%

Modelling daily soil temperature at different depths via the classical and hybrid models

Mehdizadeh

Ahmadi

Sales³

2020

Meteorological Applications

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Soil temperature (ST) is one of the crucial variables of soil and it plays a fundamental role in different research scopes such as underground soil physical and agricultural applications. The study explores the modelling performance of a time series‐based model (i.e. bi‐linear, BL), and an artificial intelligence‐based approach including adaptive neuro‐fuzzy inference system (ANFIS), for modelling the daily ST of different soil depths (5, 10, 50 and 100 cm). The study also develops and proposes two diverse types of the hybrid models through coupling the ANFIS with the BL and wavelet analysis (W) to improve the accuracy of the ST modelling. Two stations in Iran (i.e. Isfahan and Urmia) were selected as the study locations. The results demonstrated that the ANFIS generally presented better results than the BL. Furthermore, the hybrid models (i.e. W‐ANFIS and ANFIS‐BL) gave superior performances than the classical ANFIS and BL for modelling the daily ST of the studied areas at various soil depths. In addition to the local evaluation of the ANFIS (i.e. modelling the ST at a specific depth by using the original ST data at that depth), an external analysis was also conducted. In doing so, the daily ST data at a 5 cm depth were modelled via the corresponding ST data at a 10 cm depth, and vice versa. The results denoted the applicability of the ST data at another depth for modelling the ST of each specific/target depth.

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“…The ARMA model not only fits the stationary time series accurately, but also reduces the number of parameters. ARMA model and its combination models have been widely used in many different fields, such as prediction [15,16], electricity consumption [17][18][19], hydrological research [20] and so on.…”

Section: Product Seasonal Modelmentioning

confidence: 99%

The short-term prediction of the mobile communication traffic based on the product seasonal model

2020

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The short-term forecast can be used to respond to the unexpected business shocks in advance, thus guaranteeing user experience. We present a practical communication traffic forecasting technology based on autoregressive moving average model. The proposed short-term prediction method is mainly based on the product seasonal model. The orders of product seasonal ARMA equation are recognized by the Akaike information criterion, and the parameters of equation are estimated by the maximum likelihood method. The unit root test is used to judge the stability and reversibility of the model. The performance of the proposed method is evaluated. The experimental result shows that the method has high prediction accuracy. The short-term forecast can provide a basis for mobile operators to accurately expand capacity.

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A Comparative Study of Autoregressive, Autoregressive Moving Average, Gene Expression Programming and Bayesian Networks for Estimating Monthly Streamflow

Cited by 41 publications

References 32 publications

Estimating the short-term and long-term wind speeds: implementing hybrid models through coupling machine learning and linear time series models

Estimating the short-term and long-term wind speeds: implementing hybrid models through coupling machine learning and linear time series models

Modelling daily soil temperature at different depths via the classical and hybrid models

The short-term prediction of the mobile communication traffic based on the product seasonal model

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