Abstract Our study uses regional-scale maps to quantify carbon storage and sequestration from different land use types evaluate the effects of future scenarios. We developed an integrated modeling approach assess spatiotemporal impacts use/cover change (LUCC) on provision value during historical period (2000–2019) predicted scenarios (2019–2046) in Jiroft plain, Iran. several analytic tools for...

Accurately estimating land-use demand is essential for urban models to predict the evolution of spatial morphology. Due uncertainties inherent in socioeconomic development, accurate forecasting remains a daunting challenge. The present study proposes modeling framework determine scaling relationship between population and area simulates spatiotemporal dynamics land use cover (LULC). An allometr...

Article history: Received 7 July 2015 Accepted 12 January 2016 Available online 19 January 2016

Investors use different approaches to select optimal portfolio. so, Optimal investment choices according to return can be interpreted in different models. The traditional approach to allocate portfolio selection called a mean - variance explains. Another approach is Markov chain. Markov chain is a random process without memory. This means that the conditional probability distribution of the nex...

Despite the rapid economic and population growth, risks related to current dynamics of land use cover (LULC) have attracted a lot attention in Ethiopia. Therefore, complete investigation past future LULC changes is essential for sustainable water resources land-use planning management. Since 1980s, change has been detected upper stream Awash River basin. The main purpose this research was inves...

OpenStreetMap (OSM) represents one of the most well-known examples of a collaborative mapping project. Major research efforts have so far dealt with data quality analysis but the modality of OSM’s evolution across space and time has barely been noted. This study aims to analyze spatio-temporal patterns of contributions in OSM by proposing a contribution index (CI) in order to investigate the dy...

We study the entropy rate of a hidden Markov process, defined by observing the output of a symmetric channel whose input is a first order Markov process. Although this definition is very simple, obtaining the exact amount of entropy rate in calculation is an open problem. We introduce some probability matrices based on Markov chain's and channel's parameters. Then, we try to obtain an estimate ...

We present here a new method to solve Markov chains based on a Stochastic Automata Network. This method merges the reduction technique and the nonsymmetric permutations on the rows and columns of the generator associated with the Markov chain. This method is developed to compute some rewards in a military application, but it happens that the method may have a larger field of interest.

A simple result is presented that links the learning of hidden Markov models to results in complexity theory about nonlearnability of finite automata under certain cryptographic assumptions. Rather than considering all probability distributions, or even just certain specific ones, the learning of a hidden Markov model takes place under a distribution induced by the model itself.