改进灰色马尔科夫模型在基坑预测中的研究

基坑预测问题关系到工程施工的安全,在施工过程中对基坑进行周密的监测和变性预测分析显得尤为重要。针对传统预测模型存在固有偏差和可靠性低的缺点,采用新陈代谢的原理对无偏灰色加权马尔科夫模型进行改进。该模型先用无偏灰色模型拟合系统的总体变化趋势,然后,对拟合残差进行马尔可夫状态划分,并根据各阶权重对不同步长的转移矩阵进行加权处理,用加权后的无偏灰色马尔科夫模型进行预测。在每一步的预测中,利用新陈代谢的原理不断更新建模所使用的数据。将该模型用于基坑沉降预测,并通过实例进行验证。实验表明:基于新陈代谢的无偏灰色加权马尔科夫模型提高了基坑沉降预测的精度和可靠性,预测精度与未改进模型相比提高了8.54%。     

Bayesian Spatial Modelling of Gamma Ray Count Data

Gamma ray logging is a method routinely employed by geophysicists and environmental engineers in site geology evaluations. Modelling of gamma ray data from individual boreholes assists in the local identification of major lithological changes; modelling these data from a network of boreholes assists with lithological mapping and spatial stratigraphic correlation. In this paper we employ Bayesian spatial partition models to analyse gamma ray data spatially. In particular, a spatial partition is defined via a Voronoi tessellation and the mean intensity is assumed constant in each cell of the partition. The number of vertices generating the tessellation as well as the locations of vertices are assumed unknown, and uncertainty about these quantities is described via a hierarchical prior distribution. We describe the advantages of the spatial partition modelling approach in the context of smoothing gamma ray count data and describe an implementation that may be extended to the fitting of a more general model than a constant mean within each cell of the partition. As an illustration of the methodology we consider a data set collected from a network of eight boreholes, which is part of a geophysical study to assist in mapping the lithology of a site. Gamma ray logs are linked with geological information from cores and the spatial analysis of log data assists with predicting the lithology at unsampled locations.     

On the identification of ship coupled heave–pitch motions using neural networks

This paper outlines a procedure for the derivation of the differential equations describing the free response of a heaving and pitching ship from its stationary response to random waves. The coupled heave–pitch motion of a ship in random seas is modelled as a multi-dimensional Markov process. The partial differential equation describing the transition probability density function, known as the Fokker-Planck equation, for this process is derived. The Fokker-Planck equation is used to derive the random decrement equations for the coupled heave–pitch motion. The parameters in these equations are then identified using a neural network approach. The method is validated using numerical simulations and experimental results. The experimental data was obtained using an icebreaker ship model heaving and pitching in random waves. It is shown that the method produces good results when the system is lightly damped. An extension for using this method to identify couple heave–pitch motion in realistic seas is suggested.     

Combined analysis of precipitation and water deficit for drought hazard assessment

Abstract

The combined analysis of precipitation and water scarcity was done with the use of the Standardized Precipitation Index (SPI) and the Standardized Runoff Index (SRI), developed as a monthly, two-variable SPI-SRI indicator to identify different classes of hydrometeorological conditions. Stochastic analysis of a long-term time series (1966–2005) of monthly SPI-SRI indicator values was performed using a first-order Markov chain model. This provided characteristics of regional features of drought formation, evolution and persistence, as well as tools for statistical long-term drought hazard prediction. The study was carried out on two subbasins of the Odra River (Poland) of different orography and land use: the mountainous Nysa K?odzka basin and the lowland, agricultural Prosna basin. Classification obtained with the SPI-SRI indicator was compared with the output from the NIZOWKA model that provided identification of hydrological drought events including drought duration and deficit volume. Severe and long-duration droughts corresponded to SPI-SRI Class 3 (dry meteorological and dry hydrological), while severe but short-term droughts (lasting less than 30 days) corresponded to SPI-SRI Class 4 (wet meteorological and dry hydrological). The results confirm that, in Poland, meteorologically dry conditions often shift to hydrologically dry conditions within the same month, droughts rarely last longer than 2 months and two separate drought events can be observed within the same year.     

Optimal operation model of a single reservoir with drought duration explicitly concerned

In this paper, a new model for a single reservoir operation optimization is proposed. The proposed model can design the optimal operation policy of a reservoir with explicit consideration of drought duration. The authors model this problem by formulating a single-stage loss function as a function of both the reservoir release and drought duration. Thereby the expected loss per period which is calculated based on the above extended single-stage loss function is minimized in infinite time horizon on the basis of Markov decision process (MDP) theory. The reliability indices are estimated as expected loss per period for specified extended loss functions. Finally, the features of the proposed model are illustrated through numerical analysis.     

基于Markov链的金川铜镍矿床超基性岩体侵位过程模拟及找矿启示

金川铜镍矿床是世界第三大铜镍硫化物岩浆矿床,现有研究表明其成矿模式为岩浆通道成矿,但是对于金川超基性岩体的侵位过程存在较大争议。为探索岩体的侵位过程,将岩浆侵位描述为马尔可夫(Markov)过程,提出一种基于Markov链的岩体侵位模拟算法,实现对金川超基性岩体侵位过程的模拟。以金川Ⅱ矿区为例,探讨了侵位过程与矿化的关联及岩浆通道骨架,为在矿床深部寻找第二成矿空间提供方向和线索。     

Simulation of landscape spatial layout evolution in rural-urban fringe areas: a case study of Ganjingzi District

In recent years, the rapid expansion of urban spaces has accelerated the mutual evolution of landscape types. Analyzing and simulating spatio-temporal dynamic features of urban landscape can help to reveal its driving mechanisms and facilitate reasonable planning of urban land resources. The purpose of this study was to design a hybrid cellular automata model to simulate dynamic change in urban landscapes. The model consists of four parts: a geospatial partition, a Markov chain (MC), a multi-layer perceptron artificial neural network (MLP-ANN), and cellular automata (CA). This study employed multivariate land use data for the period 2000–2015 to conduct spatial clustering for the Ganjingzi District and to simulate landscape status evolution via a divisional composite cellular automaton model. During the period of 2000–2015, construction land and forest land areas in Ganjingzi District increased by 19.43% and 15.19%, respectively, whereas farmland, garden lands, and other land areas decreased by 43.42%, 52.14%, and 75.97%, respectively. Land use conversion potentials in different sub-regions show different characteristics in space. The overall land-change prediction accuracy for the subarea-composite model is 3% higher than that of the non-partitioned model, and misses are reduced by 3.1%. Therefore, by integrating geospatial zoning and the MLP-ANN hybrid method, the land type conversion rules of different zonings can be obtained, allowing for more effective simulations of future urban land use change. The hybrid cellular automata model developed here will provide a reference for urban planning and policy formulation.     

Simulating hourly rainfall occurrence within an equatorial rainforest,Borneo Island / Simulation de l'occurrence de pluie horaire au sein de la forêt équatoriale,Ile de Bornéo

Abstract

The ability to simulate characteristics of the diurnal cycle of rainfall occurrence, and its evolution over the seasons is important to the forecasting of hydrological impacts resulting from land-use and climate changes within the humid tropics. This stochastic modelling study uses a generalized linear model (GLM) solution to second-order Markov chain models, as these discrete models are better at describing binary occurrence processes on an hourly time-scale than continuous-time approaches such as stochastic state-space models. We show that transition probabilities derived by the Markov chain method need to be time-varying rather than stationary to simulate the evolution of the diurnal cycle of rainfall occurrence over a Southeast Asian monsoon sequence. The conceptual and pragmatic links between discrete diurnal processes and continuous processes occurring over seasonal periods are thereby simulated within the same model.     

Gibbs sampler by sampling-importance-resampling

Among the Markov chain Monte Carlo methods, the Gibbs sampler has the advantage that it samples from the conditional distributions for each unknown parameter, thus decomposing the sample space. In the case the conditional distributions are not tractable, the Gibbs sampler by means of sampling-importance-resampling is presented here. It uses the prior density function of a Bayesian analysis as the importance sampling distribution. This leads to a fast convergence of the Gibbs sampler as demonstrated by the smoothing with preserving the edges of 3D images of emission tomography.     

Drought characterization: a probabilistic approach

Using the alternative renewable process and run theory, this study investigates the distribution of drought interval time, mean drought interarrival time, joint probability density function and transition probabilities of drought events in the Kansabati River basin in India. The standardized precipitation index series is employed in the investigation. The time interval of SPI is found to have a significant effect of the probabilistic characteristics of drought.