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Drought modeling using classic time series and hybrid wavelet-gene expression programming models
(ELSEVIER, 2020)
The standardized precipitation evapotranspiration index (SPEI) at three different time scales (i.e., SPEI-3, SPEI-6, and SPEI-12) from six meteorology stations located in Turkey are modeled in this study. To this end, two ...
Estimating the short-term and long-term wind speeds: implementing hybrid models through coupling machine learning and linear time series models
(SPRINGER INTERNATIONAL PUBLISHING AG, 2020)
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 ...
Developing novel hybrid models for estimation of daily soil temperature at various depths
(ELSEVIER, 2020)
Estimation of soil temperature (ST) as one of the vital parameters of soil, which has an impact on many chemical and physical characteristics of soil, is of great importance in soil science. This study applies a time ...
Hybrid models to improve the monthly river flow prediction: Integrating artificial intelligence and non-linear time series models
(ELSEVIER, 2019)
Prediction of river flow as a fundamental source of hydrological information plays a crucial role in various fields of water projects. In this study, at first, the capabilities of two time series analysis approaches, namely ...
Drought modeling using classic time series and hybrid wavelet-gene expression programming models
(Journal of Hydrology, 2020)
The standardized precipitation evapotranspiration index (SPEI) at three different time scales (i.e., SPEI-3, SPEI-6, and SPEI-12) from six meteorology stations located in Turkey are modeled in this study. To this end, two ...
Developing novel hybrid models for estimation of daily soil temperature at various depths
(Soil and Tillage Research, 2020)
Estimation of soil temperature (ST) as one of the vital parameters of soil, which has an impact on many chemical and physical characteristics of soil, is of great importance in soil science. This study applies a time ...
Comparative assessment of time series and artificial intelligence models to estimate monthly streamflow: A local and external data analysis approach
(Journal of Hydrology, 2019)
River flow rates are important for water resources projects. Given this, the current study explored the use of autoregressive (AR) and moving average (MA) techniques as individual time series models and compared them to ...
Hybrid models to improve the monthly river flow prediction: Integrating artificial intelligence and non-linear time series models
(Journal of Hydrology, 2019)
Prediction of river flow as a fundamental source of hydrological information plays a crucial role in various fields of water projects. In this study, at first, the capabilities of two time series analysis approaches, namely ...
Comparative assessment of time series and artificial intelligence models to estimate monthly streamflow: A local and external data analysis approach
(ELSEVIER, 2019)
River flow rates are important for water resources projects. Given this, the current study explored the use of autoregressive (AR) and moving average (MA) techniques as individual time series models and compared them to ...
Closure to the discussion of Ebtehaj et al. on "Comparative assessment of time series and artificial intelligence models to estimate monthly streamflow: A local and external data analysis approach"
(Elsevier, 2021)
In this closure, we respond to the comments of Ebtehaj et al. (2020), and also provide additional details regarding several features of our study.
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