Prediction framework in a distributed lag model with a target function: an application to global warming data

Özbay, Nimet; Toker, Selma

doi:10.1007/s10651-020-00477-x

Cited by 6 publications

(2 citation statements)

References 46 publications

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“…The correlation coefficient between the estimated Y t and the measured Y t was used to determine an optimal lag time. If the correlation coefficient no longer increases with the increase of k, a value of k at this time is considered to be the optimal lag time (Liu, 2019;Özbay and Toker, 2021).…”

Section: Almon Distributed Lag Modelmentioning

confidence: 99%

Time-Lag Effect: River Algal Blooms on Multiple Driving Factors

et al. 2022

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Compared to the eutrophication of lakes and reservoirs, the mechanism of river algal blooms in a flowing water body are more complicated, and often lead to serious consequence in catchment scale. Due to the simultaneous impact of a variety of environmental pressures, the water ecosystem integrity state often shows a response characteristic of accumulation, complexity and time lag, therefore it is difficult to use conventional hydrodynamic and water quality models to scientifically characterize and analyze. The lower Hanjiang River (HR) is an important influence area of the middle route of the South-to-North Water Diversion Project (SNWDP) in China, continuous river blooms issue has become a major national concern. In this study, a time-lag analysis approach was developed to identify the causes of algal blooms formation and the time-lag response law in the lower HR, including principal component analysis (PCA), grey relation analysis (GRA), and Almon Distributed Lag Model. Results found that, the hydrological regime (ΔH) contributes the most to the river bloom, especially due to the flow hindrance of the Yangtze River (YR) and the water project upstream. It is also found that the algal bloom outbreak in the lower HR is not an immediate response to the driving factors. It has a time lag of about 1 period (10 days) in the response with antecedent driving factors. Finally, we discussed the influence and its responding mechanism of ΔH on the growth of phytoplankton. The research can provide early warning for the prevention and control of algal blooms in the large river system.

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Section: Almon Distributed Lag Modelmentioning

confidence: 99%

Time-Lag Effect: River Algal Blooms on Multiple Driving Factors

et al. 2022

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“…wheres denotes the correlation between the regressors and its values are chosen as follows: 0.8, 0.9, 0.99, and 0.999. Ozbay & Toker, 2021). An observation of T-p ¼ 60 and 100 with the lag length of 8 and 16 was evaluated.…”

Section: Monte Carlo Simulation Studymentioning

confidence: 99%

Almon-KL estimator for the distributed lag model

Lukman

Kibria

2021

Arab Journal of Basic and Applied Sciences

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The Almon technique is widely used to estimate the parameters of the distributed lag model (DLM). The technique suffers a setback from the challenge of multicollinearity because the explanatory variables and their lagged values are often correlated. The Almon-Ridge estimator (A-RE) and Almon-Liu estimator (A-LE) were introduced as alternative estimators for efficient modelling. We developed a new method of estimating the coefficients of the DLM using the Almon-KL estimator (A-KLE). A-KLE dominates the other estimators considered in this study via theoretical findings, simulation design and two numerical examples. The estimators' performance was compared using the mean squared error.

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How global warming data are modeled via some novel mathematical programming scenarios in distributed lag model?

Özbay,

Toker

2024

Stoch Environ Res Risk Assess

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Prediction framework in a distributed lag model with a target function: an application to global warming data

Cited by 6 publications

References 46 publications

Time-Lag Effect: River Algal Blooms on Multiple Driving Factors

Time-Lag Effect: River Algal Blooms on Multiple Driving Factors

Almon-KL estimator for the distributed lag model

How global warming data are modeled via some novel mathematical programming scenarios in distributed lag model?

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