Bridge deterioration models for different superstructure types using Markov chains and two-step cluster analysis

Moscoso, Yina F. M.; Rincón, Luis F.; Leiva-Maldonado, Stefan L.; Matos, José C.

doi:10.1080/15732479.2022.2119583

Cited by 7 publications

(1 citation statement)

References 36 publications

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“…We have mentioned that the use of Markov chains is common in various fields. Among the many applications, we find applications of Markov chains in geology [4] , in bridge deterioration models for different types of superstructures [5] , in random environments (the state of Marcovic environments) [6] , to model the state of underground pipelines [7] . In the field of crime science, analysis of the Markov series was used to predict specialization in criminal professions [8] .…”

Section: Introductionmentioning

confidence: 99%

Predicting the Value of Agricultural GDP in Iraq for the Period 2019—2030 by Applying the Markov Transition Matrix

AL-Hiyali,

Blaw,

Madlul

2024

Res. World Agric. Econ.

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This research aims to predict the values of agricultural GDP in Iraq for the period 2019–2030 using the Markov Transition Matrix, whereby the fifth state was chosen due to the convergence of the predicted values with the most recent real values in the original time series. In addition, the predictive error or predictive accuracy of the selected state was better compared to other states. The reason for choosing this methodology in forecasting is that it is based on probabilities derived from old historical data, but this methodology does not need old historical data for the purpose of extracting predictive values, even if the time series includes long-time series data. The predicted values follow the same path as the original time series and are not affected by the general trend of the original data, which gives us an indication that there is a problem of stagnation of agricultural GDP. Therefore, a recommendation that can be given to economic policymakers, in particular the agricultural ones, is the existent need to address the problems that the agricultural sector has always suffered from in order to break this stagnation.

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