scholarly journals Modelling spatial changes in coastal areas of Samsun (Turkey) using a Cellular Automata-Markov Chain method

2017 ◽  
Vol 24 (Supplement 1) ◽  
2018 ◽  
Vol 23 (3) ◽  
pp. 175-191
Author(s):  
Anneke Annassia Putri Siswadi ◽  
Avinanta Tarigan

To fulfill the prospective student's information need about student admission, Gunadarma University has already many kinds of services which are time limited, such as website, book, registration place, Media Information Center, and Question Answering’s website (UG-Pedia). It needs a service that can serve them anytime and anywhere. Therefore, this research is developing the UGLeo as a web based QA intelligence chatbot application for Gunadarma University's student admission portal. UGLeo is developed by MegaHal style which implements the Markov Chain method. In this research, there are some modifications in MegaHal style, those modifications are the structure of natural language processing and the structure of database. The accuracy of UGLeo reply is 65%. However, to increase the accuracy there are some improvements to be applied in UGLeo system, both improvement in natural language processing and improvement in MegaHal style.


2020 ◽  
Vol 12 (1) ◽  
pp. 626-636
Author(s):  
Wang Song ◽  
Zhao Yunlin ◽  
Xu Zhenggang ◽  
Yang Guiyan ◽  
Huang Tian ◽  
...  

AbstractUnderstanding and modeling of land use change is of great significance to environmental protection and land use planning. The cellular automata-Markov chain (CA-Markov) model is a powerful tool to predict the change of land use, and the prediction accuracy is limited by many factors. To explore the impact of land use and socio-economic factors on the prediction of CA-Markov model on county scale, this paper uses the CA-Markov model to simulate the land use of Anren County in 2016, based on the land use of 1996 and 2006. Then, the correlation between the land use, socio-economic data and the prediction accuracy was analyzed. The results show that Shannon’s evenness index and population density having an important impact on the accuracy of model predictions, negatively correlate with kappa coefficient. The research not only provides a reference for correct use of the model but also helps us to understand the driving mechanism of landscape changes.


1987 ◽  
Vol 1 (1) ◽  
pp. 33-46 ◽  
Author(s):  
David Aldous

Uniform distributions on complicated combinatorial sets can be simulated by the Markov chain method. A condition is given for the simulations to be accurate in polynomial time. Similar analysis of the simulated annealing algorithm remains an open problem. The argument relies on a recent eigenvalue estimate of Alon [4]; the only new mathematical ingredient is a careful analysis of how the accuracy of sample averages of a Markov chain is related to the second-largest eigenvalue.


Author(s):  
Afdelia Novianti ◽  
Dina Tri Utari

Java Island is one of the areas that is very fertile and densely populated, but on the other hand, Java Island is also one of the areas that is most frequently hit by natural disasters, one of which is Klaten Regency. Natural disaster itself is an event that threatens and disrupts human life caused by nature. Some of the natural disasters that often occur simultaneously in Klaten Regency are floods, landslides, and hurricanes. These three disasters usually occur during the rainy season. This of course makes the government need to take action by seeing the large chance of a disaster occurring in order to optimize disaster management. Then research will be carried out that aims to determine the chances of natural disasters occurring in the next few years. Forecasting will be carried out using the Markov chain method, with this method the probability value of the future period can be estimated using the current period probability value based on the characteristics of the past period. So that the value of the steady state chance of floods and landslides in period 36 (December 2023) and hurricanes in period 15 (March 2022) with the chances of a disaster are 34.21%, 15.38%, and 73.53%, respectively.Received August 31, 2021Revised October 27, 2021Accepted November 11, 2021


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