基于随机森林算法与农户视角的村落级农业面源污染风险分析

当前农业面源污染问题依然严峻,村落级尺度下各地块的污染风险状态精细识别有待进一步研究.本文针对三峡库区典型村落重庆市涪陵区南沱镇睦和村进行研究,结合无人机多光谱技术、农户行为调研、随机森林算法等进行地物识别及泛地块网格划分,通过测算总氮(total nitrogen,TN)、总磷(total phosphorus,TP...     

基于O/E模型的浅水湖泊生态系统健康评价

以长江中下游阻隔型浅水湖泊为研究对象,分析了1998—2018年116湖次底栖动物野外调查数据,构建了基于预测模型的观测物种组成/期望物种组成(O/E)指数健康评价指标体系,评价了长江中下游浅水湖泊的底栖动物物种组成完整性.O/E指数模型评价结果显示,在物种采集概率(Pc)>0阈值条件下的O/E0模型预测得到的监测湖泊...     

GIS与数模支持下的油气田环境风险预警系统研究

简要介绍了油气田在建设与生产过程中对环境的影响,构建并开发了基于GIS与数模的油气田环境风险预警系统。系统包含大气污染模拟模型、地表水污染模拟模型和地下水污染模拟模型,实现了环境监测数据的管理、实时监测预警和污染模拟、快速应急响应三大功能。介绍了油气田环境风险预警系统的设计及基本功能,探索了将地理信息系统与数学模型相结合运用于油气田环境风险预警的方法。实践表明,以GIS技术为平台,结合数学模型为辅助进行环境风险预警是可行的。     

系统安全态势的马尔科夫预测模型建立及应用

基于马尔科夫链预测理论,分析研究了系统安全态势的定性与定量预测问题,建立了系统安全态势预测模型.定性预测侧重安全态势的升、降趋势分析,定量预测以系统万人死亡率为依据,划分四个安全状态,从而实现系统安全状态定量预测.定性与定量预测相互检验、相互补充.实例研究表明,马尔科夫定性与定量相结合的预测模型结构简单,计算方便,符合系统安全态势预测特征要求,是系统安全宏观管理的重要参考依据之一.     

基于Monte-Carlo方法的长输管道气体扩散模拟研究

目前气体扩散模拟研究多采用流体力学的计算方法,分析气体扩散过程中的动力学特性.有限体积、有限元等方法都需要对事故区域整体进行网格划分,计算过程效率无法满足长输管道事故应急跨区域、多气象以及复杂地形的要求.Monte-Carlo方法利用RAMS预测的平均风场,模拟有限气体粒子在风场中的随机行走特性,有效地弥补了计算效率与网格精度冲突所导致的模拟性能下降的缺点.通过HAVEGE方法收集计算的硬件信息熵形成随机源,修正了以往伪随机数问题,增强了Monte-Carlo方法的计算精度.结果表明Monte-Carlo气体扩散模拟研究方法满足了长输管道事故灾害应急决策的需要.     

考虑逆向物流的应急物资配置流程SPN建模分析

应急物资的高效快速配置是降低灾害损失和顺利实施应急救援的有力保障。应急逆向物流包括废旧物资的回收利用以及可重复利用物资的回收再利用,能起到缓解应急物资匮乏,减少环境污染的作用。本文根据随机Petri网理论,构建考虑逆向物流的应急物资配置模型,通过对同构于该模型的马尔可夫链进行仿真,求得各种状态的稳态概率,结合马尔可夫链性质对关键因素进行静态分析和动态分析;通过“雅安地震”的案例应用表明,当地震灾害发生时,此模型可以反映各因素对应急物资配置整体流程的影响,并通过数值变化趋势反映不同条件下应急物资配置的关键环节,可以为灾后救援和应急物资的利用提供理论支持。     

基于GPR-TOPSIS方法的充填配比多目标多属性优化

为获得某金矿尾砂胶结充填材料最优配比,基于试验结果,以海水比例、灰砂比和料浆质量浓度为输入参数,以充填体强度、塌落度及泌水率为输出参数,建立了充填配比与其响应量的高斯过程回归模型,分析了不同因素对充填性能的影响程度;采用遗传算法对高斯过程回归模型进行多目标参数优化,获得了Pareto非劣解,在此基础上,引入多属性决策的TOPSIS法对Pareto非劣解进行方案优选,确定了充填最优配比。研究结果表明:高斯过程回归模型相对误差值均小于6%,可靠性高;灰砂比及料浆质量浓度对充填性能影响较为显著,采用海水作为充填水源将降低充填体的强度;经优化后的充填配比与试验结果相符。     

Modeling Diapause Termination of Rhipicephalus Appendiculatus using Statistical Tools to Detect Sudden Behavioral Changes and Time Dependencies

This paper presents statistical methodology to analyze longitudinal binary responses for which a sudden change in the response occurs in time. Probability plots, transition matrices, and change-point models and more advanced techniques such as generalized auto-regression models and hidden Markov chains are presented and applied on a study on the activity of Rhipicephalus appendiculatus, the major vector of Theileria parva, a fatal disease in cattle. This study presents individual measurements on female R. appendiculatus, which are terminating their diapause (resting status) and become active. Comprehending activity patterns is very important to better understand the ecology of R. appendiculatus. The model indicates that activity and non-activity act in an absorbing way meaning that once a tick becomes active it shows a tendency to remain active. The change-point model estimates that the sudden change in activity happens on December 10. The reaction of ticks on acceleration and changes in rainfall and temperature indicates that ticks can sense climatic changes. The study revealed the underlying not visually observable states during diapause development of the adult tick of R. appendiculatus. These states could be related to phases during the dynamic event of diapause development and post-diapause activity in R. appendiculatus.     

A simulation model of rhizome networks for Fallopia japonica (Japanese knotweed) in the United Kingdom

Fallopia japonica (Japanese knotweed) is an aggressively invasive herbaceous perennial that causes substantial economic and environmental damage in the United Kingdom (UK). As such, it is of considerable concern to councils, environmental groups, private landowners and property developers. We construct a 3D correlated random walk model of the development of the subterranean rhizome network for a single stand of F. japonica. The formulation of this model uses detailed knowledge of the morphology and physiology of the plant, both of which differ in the UK to that of its native habitat due to factors including a lack of predation and competition, longer growth seasons and favourable environmental conditions in the UK. Field data obtained as a part of this study are discussed and used in the model for parameterisation and validation. The simulation captures the field data well and predicts, for example, quadratic growth in time for the stand area. Furthermore, the role of a selection of parameters on long-term stand development are discussed, highlighting some key factors affecting vegetative spread rates.     

On the latent state estimation of nonlinear population dynamics using Bayesian and non-Bayesian state-space models

Nonlinear state-space models have been increasingly applied to study population dynamics and data assimilation in environmental sciences. State-space models can account for process error and measurement error simultaneously to correct for the bias in the estimates of system state and model parameters. However, few studies have compared the performance of different nonlinear state-space models for reconstructing the state of population dynamics from noisy time series. This study compared the performance of the extended Kalman filter (EKF), unscented Kalman filter (UKF) and Bayesian nonlinear state-space models (BNSSM) through simulations. Synthetic population time series were generated using the theta logistic model with known parameters, and normally distributed process and measurement errors were introduced using the Monte Carlo simulations. At higher levels of nonlinearity, the UKF and BNSSM had lower root mean square error (RMSE) than the EKF. The BNSSM performed reliably across all levels of nonlinearity, whereas increased levels of nonlinearity resulted in higher RMSE of the EKF. The Metropolis–Hastings algorithm within the Gibbs algorithm was used to fit the theta logistic model to synthetic time series to estimate model parameters. The estimated posterior distribution of the parameter θ indicated that the 95% credible intervals included the true values of θ (=0.5 and 1.5), but did not include 1.0 and 0.0. Future studies need to incorporate the adaptive Metropolis algorithm to estimate unknown model parameters for broad applications of Bayesian nonlinear state-space models in ecological studies.