Urban land uses are in a dynamic state that varies over time, the city of Karbala in Iraq has experienced functional changes over the past 100 years, as the city is characterized by the presence of significant tourist and socio-economic activity represented by religious tourism, and it occur due to various reasons such as urbanization. The purpose of this study is to apply a Markov model to analyze and predict the behavior of transforming the use of land in Karbala city over time. This can include the conversion of agricultural land, or other areas into residential, commercial, industrial land uses. The process of urbanization is typically driven by population growth, economic development, based on a set of probabilities and transitions between different states. They can help decision-makers understand the likely outcomes of different scenarios for the future. The research question is in which direction of the functional during the next 50 years in the case study? What are the values of the prediction of functional changes for future? The research Hypothesis: Urban functions are changed in different areas; agricultural land uses have decreased and land use functions have changed in an unplanned direction in the next 50 years. The study discovered that almost one-third of the agricultural land in Karbala has reduced. Additionally, there has been a 10% alteration in the usage of residential land in slums and other sectors. However, there has been a positive growth in transport, cemeteries, trade, industry, and services, with different degrees of progress.
In this paper, estimation of system reliability of the multi-components in stress-strength model R(s,k) is considered, when the stress and strength are independent random variables and follows the Exponentiated Weibull Distribution (EWD) with known first shape parameter θ and, the second shape parameter α is unknown using different estimation methods. Comparisons among the proposed estimators through Monte Carlo simulation technique were made depend on mean squared error (MSE) criteria