Modelling Land Use/Land Cover Change of River Rwizi Catchment, South-Western Uganda Using GIS and Markov Chain Model

Analysis of catchment Land use/Land cover (LULC) change is a vital tool in ensuring sustainable catchment management. The study analyzed land use/land cover changes in the Rwizi catchment, south western Uganda from 1989-2019 and projected the trend by 2040. Landsat images, field observations, key informant interviews and focus group discussions were used to collect data. Changes in cropland, forestland, built up area, grazing land, wetland and open water bodies were analyzed in ArcGIS version 10.2.2 and ERDAS IMAGINE 14 software and a Markov chain model. All the LULC classes increased in area except grazing land. Forest land and builtup area between 2009-2019 increased by 370.03% and 229.53% respectively. Projections revealed an increase in forest land and builtup area by 2030 and only built up area by 2040. LULCC in the catchment results from population pressure, reduced soil fertility and high value of agricultural products.

Application of Cellular Automata and Markov Chain model for urban green infrastructure in Kuala Lumpur,Malaysia

Kuala Lumpur of Malaysia,as a tropical city,has experienced a notable decline in its critical urban green infrastructure(UGI)due to rapid urbanization and haphazard development.The decrease of UGI,especially natural forest and artificial forest,may reduce the diversity of ecosystem services and the ability of Kuala Lumpur to build resilience in the future.This study analyzed land use and land cover(LULC)and UGI changes in Kuala Lumpur based on Landsat satellite images in 1990,2005,and 2021and employed the overall accuracy and Kappa coefficient to assess classification accuracy.LULC was categorized into six main types:natural forest,artificial forest,grassland,water body,bare ground,and built-up area.Satellite images in 1990,2005,and 2021 showed the remarkable overall accuracy values of 91.06%,96.67%,and 98.28%,respectively,along with the significant Kappa coefficient values of 0.8997,0.9626,and 0.9512,respectively.Then,this study utilized Cellular Automata and Markov Chain model to analyze the transition of different LULC types during 1990-2005 and 1990-2021 and predict LULC types in 2050.The results showed that natural forest decreased from 15.22%to 8.20%and artificial forest reduced from 18.51%to 15.16%during 1990-2021.Reductions in natural forest and artificial forest led to alterations in urban surface water dynamics,increasing the risk of urban floods.However,grassland showed a significant increase from 7.80%to 24.30%during 1990-2021.Meanwhile,bare ground increased from 27.16%to 31.56%and built-up area increased from 30.45%to 39.90%during 1990-2005.In 2021,built-up area decreased to 35.10%and bare ground decreased to 13.08%,indicating a consistent dominance of built-up area in the central parts of Kuala Lumpur.This study highlights the importance of integrating past,current,and future LULC changes to improve urban ecosystem services in the city.

Emoti-Shing: Detecting Vishing Attacks by Learning Emotion Dynamics through Hidden Markov Models

This study examines vishing, a form of social engineering scam using voice communication to deceive individuals into revealing sensitive information or losing money. With the rise of smartphone usage, people are more susceptible to vishing attacks. The proposed Emoti-Shing model analyzes potential victims’ emotions using Hidden Markov Models to track vishing scams by examining the emotional content of phone call audio conversations. This approach aims to detect vishing scams using biological features of humans, specifically emotions, which cannot be easily masked or spoofed. Experimental results on 30 generated emotions indicate the potential for increased vishing scam detection through this approach.

Persistence of a Stochastic HPV Epidemic Model with Markov Switching

In order to study the influence of stochastic disturbance and environment switching on the HPV infection and provide a theoretical basis for the development of effective HPV disease prevention measures,in this paper we establish a kind of two-sex stochastic HPV epidemic model with white noise and Markov switching.We show that the model has a unique local positive solution and a unique global positive solution.Then we identify the threshold conditions for the persistence of the HPV epidemic,and verify the persistence of the disease using the Lyapunov method and the Ito^formula.At last,the numerical simulation is carried out to illustrate the rationality of the theoretical results.

基于PLSR-FLUS-MarKov模型的生态系统服务价值多情景预测--以漓江流域为例

以漓江流域为研究单元,运用修正后的PLSR-FLUS-MarKov模型和当量因子法,对流域自然发展、生态优先、旅游优先情景下2030年生态系统服务价值做出预测,探究多情景土地利用变化对生态系统服务价值的影响。结果表明,PLSR-FLUS-MarKov模型能更准确刻画流域土地利用动态变化,模拟精度高达83.57%。流域土地利用变化集中区位于桂林市辖区、兴安县和阳朔县,多表现为耕地、林地向建设用地的转入以及耕地和林地间的相互转化。2030年,漓江流域生态系统服务价值均呈现下降趋势,自然发展情景、生态优先情景、旅游优先情景分别为218.07亿、225.61亿、222.91亿元,较2020年下降了9.02亿、1.48亿和4.18亿元,并呈现“东北高,西南低”的格局特征,实施生态优先策略后其下降速度放缓。林地是漓江流域生态系统服务价值的关键来源,占比超过80%,耕地和水域也贡献了一定比例的服务价值。生态优先情景下的生态系统服务价值最高,旅游优先情景次之,自然发展情景最低。研究结果可为桂林世界级旅游城市建设及区域生态系统健康管理提供科学指导。

Air target intent recognition method combining graphing time series and diffusion models

Air target intent recognition holds significant importance in aiding commanders to assess battlefield situations and secure a competitive edge in decision-making.Progress in this domain has been hindered by challenges posed by imbalanced battlefield data and the limited robustness of traditional recognition models.Inspired by the success of diffusion models in addressing visual domain sample imbalances,this paper introduces a new approach that utilizes the Markov Transfer Field(MTF)method for time series data visualization.This visualization,when combined with the Denoising Diffusion Probabilistic Model(DDPM),effectively enhances sample data and mitigates noise within the original dataset.Additionally,a transformer-based model tailored for time series visualization and air target intent recognition is developed.Comprehensive experimental results,encompassing comparative,ablation,and denoising validations,reveal that the proposed method achieves a notable 98.86%accuracy in air target intent recognition while demonstrating exceptional robustness and generalization capabilities.This approach represents a promising avenue for advancing air target intent recognition.

Ontology mapping based on hidden Markov model

The existing ontology mapping methods mainly consider the structure of the ontology and the mapping precision is lower to some extent. According to statistical theory, a method which is based on the hidden Markov model is presented to establish ontology mapping. This method considers concepts as models, and attributes, relations, hierarchies, siblings and rules of the concepts as the states of the HMM, respectively. The models corresponding to the concepts are built by virtue of learning many training instances. On the basis of the best state sequence that is decided by the Viterbi algorithm and corresponding to the instance, mapping between the concepts can be established by maximum likelihood estimation. Experimental results show that this method can improve the precision of heterogeneous ontology mapping effectively.

Optimal state and branch sequence based parameter estimation of continuous hidden Markov model

A parameter estimation algorithm of the continuous hidden Markov model isintroduced and the rigorous proof of its convergence is also included. The algorithm uses theViterbi algorithm instead of K-means clustering used in the segmental K-means algorithm to determineoptimal state and branch sequences. Based on the optimal sequence, parameters are estimated withmaximum-likelihood as objective functions. Comparisons with the traditional Baum-Welch and segmentalK-means algorithms on various aspects, such as optimal objectives and fundamentals, are made. Allthree algorithms are applied to face recognition. Results indicate that the proposed algorithm canreduce training time with comparable recognition rate and it is least sensitive to the training set.So its average performance exceeds the other two.

Applicability of Markov chain-based stochastic model for bubbling fluidized beds

A Markov chain-based stochastic model （MCM） is developed to simulate the movement of particles in a 2D bubbling fluidized bed （BFB）. The state spaces are determined by the discretized physical cells of the bed, and the transition probability matrix is directly calculated by the results of a discrete element method （DEM） simulation. The Markov property of the BFB is discussed by the comparison results calculated from both static and dynamic transition probability matrices. The static matrix is calculated based on the Markov chain while the dynamic matrix is calculated based on the memory property of the particle movement. Results show that the difference in the trends of particle movement between the static and dynamic matrix calculation is very small. Besides, the particle mixing curves of the MCM and DEM have the same trend and similar numerical values, and the details show the time averaged characteristic of the MCM and also expose its shortcoming in describing the instantaneous particle dynamics in the BFB.

Land use change of Kitakyushu based on landscape ecology and Markov model

Based on four phases of TM images acquired in 1990, 1995, 2000 and 2005, this paper took Kitakyushu in Japan as a case study to analyze spatial change of land use landscape and corresponding effects on environmental issues guided by landscape ecology theory in virtue of combining technology of Remote Sensing with GIS. Firstly, land use types were divided into 6 classes （farmland, mountain, forestland, water body, urban land and unused land） according to national classification standard of land use, comprehensible ability of TM image and purpose of this study. Secondly, following the theory of landscape ecology analysis, 11 typical landscape indices were abstracted to evaluate the environmental effects and spatial feature changes of land use. Research results indicated that land use has grown more and more diversified and unbalanced, human activities have disturbed the landscape more seriously. Finally, transfer matrix of Markov was applied to forecast change process of land use in the future different periods, and then potential land use changes were also simulated from 2010 to 2050. Results showed that conversion tendency for all types of land use in Kitakyushu into urban construction land were enhanced. The study was anticipated to help local authorities better understand and address a complex land use system, and develop improved land use management strategies that could better balance urban expansion and ecological conservation.

基于InVEST与CA-Markov模型的宝鸡市碳储量时空演变与预测

土地利用类型的变化是影响陆地生态系统碳储量变化的重要因素,分析区域未来土地利用格局对碳储量的影响、对维持碳平衡具有重要意义.基于宝鸡市2000—2020年土地利用数据,运用InVEST模型和CA-Markov模型,对2000—2020年宝鸡市不同土地利用类型碳储量的变化特征进行分析,并对2030年不同情景下宝鸡市土地利用变化及碳储量变化情况进行了预测.研究表明:①2000—2020年宝鸡市建设用地面积不断增加,耕地、林地和草地为主要土地利用类型.②2000—2020年,宝鸡市碳储量共损失0.42×10^(6)t,耕地和林地面积减少是其碳储量减少的主要原因.③2020—2030年自然发展情景下宝鸡市碳储量损失最多,耕地保护情景和生态保护情景下宝鸡市碳储量相差不大,控制城市扩张情景下宝鸡市碳储量损失最少、固碳能力最强.因此,未来进行国土空间规划时,可统筹考虑控制城市扩张情景、生态保护情景和耕地保护情景,以确保在粮食供给和生态安全的基础上,提高研究区的碳储量.

D-S理论和Markov链组合的桥梁性能退化预测研究

为准确预测桥梁性能退化,考虑到数据随机性和微小扰动发生状态跳跃,提出了一种D-S(Dempster-Shafer)证据理论和Markov链组合的桥梁性能退化组合预测模型和性能退化率的概念.该模型基于指数平滑(exponential smoothing,ES)方法获得新的预测数据序列,并利用Markov链和D-S理论不断进行优化,从而实现桥梁性能退化的组合预测.实际工程的应用结果表明:性能退化率可以直观地表征在梁性能退化的速度.其次,该模型的平均相对误差为1.54%,较于回归、灰色和模糊加权Markov链模型,精度分别提高了1.11%,0.88%和2.8%,而后验差比值为0.242,小于0.35;模型的标准差为9.021,相比其他模型分别减小了3.978,3.405和7.500,而变异系数为0.109,均小于其他模型,验证了组合预测模型在精度和稳定性方面的优越性,可为在役桥梁结构性能退化预测与维护提供理论基础.

Simulation of Land-use Scenarios for Beijing Using CLUE-S and Markov Composite Models

This study investigated and simulated land use patterns in Beijing for the year 2000 and the year 2005 from the actual land use data for the year 1995 and the year 2000,respectively,by combining spatial land allocation simulation using the CLUE-S model,and numerical land demand prediction using the Markov model.The simulations for 2000 and 2005 were confirmed to be generally accurate using Kappa indices.Then the land-use scenarios for Beijing in 2015 were simulated assuming two modes of development:1) urban development following existing trends;and 2) under a strict farmland control.The simulations suggested that under either mode,urbanized areas would expand at the expense of land for other uses.This expansion was predicted to dominate the land-use conversions between 2005 and 2015,and was expected to be accompanied by an extensive loss of farmland.The key susceptible to land-use changes were found to be located at the central urban Beijing and the surrounding regions including Yanqing County,Changping District and Fangshan District.Also,the simulations predicted a considerable expansion of urban/suburban areas in the mountainous regions of Beijing,suggesting a need for priority monitoring and protection.

基于动态稳定性验证和CA-Markov预测的耕地时空演变特征--以赣榆区为例

为阐明耕地变化动态稳定性验证的重要性,预测“十四五”目标年(2025年)与远景目标年(2035年)的耕地变化,本研究运用耕地面积变异系数、地理集中指数、不平衡指数及趋势分析法,从耕地数量、空间分布与空间发展趋势三个方面,对江苏省连云港市赣榆区耕地变化的动态稳定性进行验证,并运用CA-Markov模型对未来不同政策情境下的耕地变化情况进行模拟。结果表明,2009-2016年赣榆区耕地总量持续下降,未存在较大反复与波动,耕地空间分布较为均衡,耕地占用与补充的空间发展趋势较为连续,总体上具有较高的动态稳定性,符合运用CA-Markov模型的前提条件。预测在各种政策情境下,2025年之前赣榆区将经历快速的耕地非农化过程,至2035年耕地减少的速率有所放缓,具有区位优势的赣榆北部地区将是未来耕地非农化的重点区域,耕地将逐渐向南部内陆地区转移。研究表明,对耕地变化的动态稳定性进行验证可以有效保证CA-Markov模型的模拟精度,赣榆区未来的耕地保护形势不容乐观,应合理协调经济发展与耕地保护之间的关系,保障社会、生态与粮食安全。

Grey Markov chain and its application in drift prediction model of FOGs

A novel grey Markov chain predictive model is discussed to reduce drift influence on the output of fiber optical gyroscopes (FOGs) and to improve FOGs' measurement precision. The proposed method possesses advantages of grey model and Markov chain. It makes good use of dynamic modeling idea of the grey model to predict general trend of original data. Then according to the trend, states are divided so that it can overcome the disadvantage of high computational cost of state transition probability matrix in Markov chain. Moreover, the presented approach expands the applied scope of the grey model and makes it be fit for prediction of random data with bigger fluctuation. The numerical results of real drift data from a certain type FOG verify the effectiveness of the proposed grey Markov chain model powerfully. The Markov chain is also investigated to provide a comparison with the grey Markov chain model. It is shown that the hybrid grey Markov chain prediction model has higher modeling precision than Markov chain itself, which prove this proposed method is very applicable and effective.

改进CA-Markov模型的城市土地空间结构演变预测

城市土地空间结构演变预测需要大量的数据支持,现阶段预测手段存在预测精度不佳的问题,为此提出GIS技术下城市土地空间结构演变预测方法。通过GIS技术获取城市土地空间结构数据,并对其进行结构演变分析;通过人工神经网络模型、轮盘竞争机制和惯性系数对CA模型实行优化处理;将改进CA模型与Markov结构结合,建立改进CA-Markov模型,根据城市土地空间结构演变分析结果实现城市土地空间结构演变预测。实验结果表明,所提方法的城市土地空间结构演变预测精度更高,整体应用效果更好。

A Smart English Text Zero-Watermarking Approach Based on Third-Level Order and Word Mechanism of Markov Model

Text information is principally dependent on the natural languages.Therefore,improving security and reliability of text information exchanged via internet network has become the most difficult challenge that researchers encounter.Content authentication and tampering detection of digital contents have become a major concern in the area of communication and information exchange via the Internet.In this paper,an intelligent text Zero-Watermarking approach SETZWMWMM(Smart English Text Zero-Watermarking Approach Based on Mid-Level Order and Word Mechanism of Markov Model)has been proposed for the content authentication and tampering detection of English text contents.The SETZWMWMM approach embeds and detects the watermark logically without altering the original English text document.Based on Hidden Markov Model(HMM),Third level order of word mechanism is used to analyze the interrelationship between contexts of given English texts.The extracted features are used as a watermark information and integrated with digital zero-watermarking techniques.To detect eventual tampering,SETZWMWMM has been implemented and validated with attacked English text.Experiments were performed on four datasets of varying lengths under multiple random locations of insertion,reorder and deletion attacks.The experimental results show that our method is more sensitive and efficient for all kinds of tampering attacks with high level accuracy of tampering detection than compared methods.

Cybersecurity: A Stochastic Predictive Model to Determine Overall Network Security Risk Using Markovian Process

There are several security metrics developed to protect the computer networks. In general, common security metrics focus on qualitative and subjective aspects of networks lacking formal statistical models. In the present study, we propose a stochastic model to quantify the risk associated with the overall network using Markovian process in conjunction with Common Vulnerability Scoring System (CVSS) framework. The model we developed uses host access graph to represent the network environment. Utilizing the developed model, one can filter the large amount of information available by making a priority list of vulnerable nodes existing in the network. Once a priority list is prepared, network administrators can make software patch decisions. Gaining in depth understanding of the risk and priority level of each host helps individuals to implement decisions like deployment of security products and to design network topologies.

Improved hidden Markov model for speech recognition and POS tagging

In order to overcome defects of the classical hidden Markov model (HMM), Markov family model (MFM), a new statistical model was proposed. Markov family model was applied to speech recognition and natural language processing. The speaker independently continuous speech recognition experiments and the part-of-speech tagging experiments show that Markov family model has higher performance than hidden Markov model. The precision is enhanced from 94.642% to 96.214% in the part-of-speech tagging experiments, and the work rate is reduced by 11.9% in the speech recognition experiments with respect to HMM baseline system.

On-line Fault Diagnosis in Industrial Processes Using Variable Moving Window and Hidden Markov Model

An integrated framework is presented to represent and classify process data for on-line identifying abnormal operating conditions. It is based on pattern recognition principles and consists of a feature extraction step, by which wavelet transform and principal component analysis are used to capture the inherent characteristics from process measurements, followed by a similarity assessment step using hidden Markov model (HMM) for pattern comparison. In most previous cases, a fixed-length moving window was employed to track dynamic data, and often failed to capture enough information for each fault and sometimes even deteriorated the diagnostic performance. A variable moving window, the length of which is modified with time, is introduced in this paper and case studies on the Tennessee Eastman process illustrate the potential of the proposed method.