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A genetic algorithm based stochastic programming model for air quality management

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Journal of Environmental Sciences 2002年第3期14卷 367-374页

作者： MaXM ZhangF CenterforEnvironmentalSciences PekingUniversityBeijing100871China.E－mail：zhangfan@*** CenterforEnvironmentalSciences PekingUniversityBeijing100871China.E－mail：zhangfan@water.pku.edu.c

This paper presents a model that can aid planners in defining the total allowable pollutant discharge in the planning region, accounting for the dynamic and stochastic character of meteorological conditions. This is accomplished by integrating Monte Carlo simulation and using genetic algorithm to solve the model. The model is demonstrated by using a realistic air urban scale SO 2 control problem in the Yuxi City of China. To evaluate effectiveness of the model, results of the approach are shown to compare with those of the linear deterministic procedures. This paper also provides a valuable insight into how air quality targets should be made when the air pollutant will not threat the residents' health. Finally, a discussion of the areas for further research are briefly delineated.

关键词： stochastic model genetic algorithms air quality management optimization

Analysis of the impact of treatments on HIV/AIDS and Tuberculosis co-infected population under random perturbations

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Infectious Disease modelling 2024年第1期9卷 27-55页

作者： Olusegun Michael Otunuga Department of Mathematics Augusta University112015th StGE 1028AugustaGA 30912USA

In this work, we study the impact of treatments at different stages of Human Immunodeficiency Virus (HIV) and Tuberculosis (TB) co-infection in a population under the influence of random perturbations. This is achieved by constructing a stochastic epidemic model describing the transmission and treatment of the diseases. The model is created with the assumption that transmission rates fluctuate rapidly compared to the evolution of the untreated diseases. The basic reproduction numbers corresponding to the population with HIV infection only (with n stages of infections and treatments), the population with tuberculosis infection only, and the overall population with co-infection (with n stages of infection/treatments) are derived in the presence and absence of noise perturbations. These are used to discuss the long term behavior of the population around a disease-free equilibrium and an endemic equilibrium, and to analyze the effect of noise and treatments on the system. We also showed conditions under which TB infected population dynamic undergoes backward bifurcation and give conditions for disease eradication in the entire population. Analysis shows that small perturbations to the disease-free equilibrium can initially grow under certain conditions, and the introduction of TB treatment is effective in eliminating the co-infection. Numerical simulations are presented for validation of our results using published parameters.

关键词： stochastic model HIV/AIDS Tuberculosis Treatment Global stability Reproduction number

Evolved differential model for sporadic graph time-series prediction

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Intelligent and Converged Networks 2024年第3期5卷 237-247页

作者： Yucheng Xing Jacqueline Wu Yingru Liu Xuewen Yang Xin Wang Department of Electrical and Computer Engineering Stony Brook UniversityStony BrookNY 11794USA New York University New YorkNY 10012USA

Sensing signals of many real-world network systems,such as traffic network or microgrid,could be sparse and irregular in both spatial and temporal domains due to reasons such as cost reduction,noise corruption,or device *** is a fundamental but challenging problem to model the continuous dynamics of a system from the sporadic observations on the network of nodes,which is generally represented as a *** this paper,we propose a deep learning model called Evolved Differential model(EDM)to model the continuous-time stochastic process from partial observations on *** model incorporates diffusion convolutional network to parameterize continuous-time system dynamics by graph Ordinary Differential Equation(ODE)and graph stochastic Differential Equation(SDE).The graph ODE is applied to accurately capture the spatial-temporal relation and extract hidden features from the *** graph SDE can efficiently capture the underlying uncertainty of the network *** the recurrent ODE-SDE scheme,EDM can serve as an accurate online predictive model that is effective for either monitoring or analyzing the real-world networked *** extensive experiments on several datasets,we demonstrate that EDM outperforms existing methods in online prediction tasks.

关键词： graph sequence prediction sporadic time series continuous model stochastic model differential equation

stochastic modelling of infectious diseases for heterogeneous populations

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Infectious Diseases of Poverty 2016年第1期5卷 982-992页

作者： Rui-Xing Ming Jiming Liu William K.W.Cheung Xiang Wan School of Statistics&Mathematics Zhejiang Gongshang UniversityHangzhouChina Department of Computer Science Hong Kong Baptist UniversityKowloon TongHong Kong Department of Computer Science&Institute of Theoretical and Computational Study Hong Kong Baptist UniversityKowloon TongHong Kong

Background:Infectious diseases such as SARS and H1N1 can significantly impact people’s lives and cause severe social and economic *** outbreaks have stressed the urgency of effective research on the dynamics of infectious disease ***,it is difficult to predict when and where outbreaks may emerge and how infectious diseases spread because many factors affect their transmission,and some of them may be ***:One feasible means to promptly detect an outbreak and track the progress of disease spread is to implement surveillance systems in regional or national health and medical *** accumulated surveillance data,including temporal,spatial,clinical,and demographic information can provide valuable information that can be exploited to better understand and model the dynamics of infectious disease *** aim of this work is to develop and empirically evaluate a stochastic model that allows the investigation of transmission patterns of infectious diseases in heterogeneous ***:We test the proposed model on simulation data and apply it to the surveillance data from the 2009 H1N1 pandemic in Hong *** the simulation experiment,our model achieves high accuracy in parameter estimation(less than 10.0%mean absolute percentage error).In terms of the forward prediction of case incidence,the mean absolute percentage errors are 17.3%for the simulation experiment and 20.0%for the experiment on the real surveillance ***:We propose a stochastic model to study the dynamics of infectious disease spread in heterogeneous populations from temporal-spatial surveillance *** proposed model is evaluated using both simulated data and the real data from the 2009 H1N1 epidemic in Hong Kong and achieves acceptable prediction *** believe that our model can provide valuable insights for public health authorities to predict the effect of disease spread and analyse its underlying factors and to guide new control efforts.

关键词： Epidemiology stochastic model Surveillance system Spread pattern

stochastic modeling for Coliform Count Assessment in Ground Water

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Journal of Statistical Science and Application 2017年第2期5卷 64-79页

作者： A. Udaya M. Kumaran P.V.Pushpaja Department of Statistics Nehru Arts and Science College KanhangadKasaragod Kerala India

stochastic models are derived to estimate the level of coliform count in terms of MPN index, one of the most important water quality characteristic in ground water based on a set of water source location and soil characteristics. The study is based on about twenty location and soil characteristics, majority of them are observed through laboratory analysis of soil and water samples collected from nearly thee hundred locations of drinking water sources, wells and bore wells selected at random from the district of Kasaragod. The water contamination in wells are found to be relatively more as compared to bore wells. The study reveals that only 7 % of the wells and 40 o~ of the bore wells of the district are within the permissible limit of WHO standard of drinking water quality. The level of contamination is very high in the hospital premises and is very low in the forest area. Two separate multiple ordinal logistic regression models are developed to predict the level of coliform count, one for well and the other for bore well. The significant feature of this study is that in addition to scientifically proving the dependence of the water quality on the distances from waste disposal area and septic tanks etc., it highlights the dependence of two other very significant soil characteristics, the soil organic carbon and soil porosity. The models enable to predict the quality of water in a location based on the set of soil and location characteristics. One of the important uses of the model is in fixing safe locations for waste dump area, septic tank, digging well etc. in town planning, designing residential layouts, industrial layouts, hospital/hostel construction etc. This is the first ever study to describe the ground water quality in terms of the location and soil characteristics.

关键词： Generalized linear model Logistic regression model Ordinal logistic regression model Coliform count MPN index Prediction stochastic model Water quality.

stochastic modeling of BeiDou double-difference observation and impact analysis

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Stochastic modeling of BeiDou double-difference observation ...

Comparison of stochastic models in Forecasting Monthly Streamflow in Rivers: A Case Study of River Nile and Its Tributaries

第十一届中国卫星导航年会

作者： Zhongzhi Wang Weikai Miao Yunzhong Shen College of Surveying and Geo-Informatics Tongji University

Double-difference integer ambiguity resolution(IAR) is playing the vital part in the high precision GNSS positioning and navigation. The IAR success rate depends on three factors: the functional model

关键词： BDS stochastic model Cross correlation Time correlation Baseline precision Integer ambiguity resolution success rate

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Journal of Water Resource and Protection 2016年第2期8卷 143-153页

作者： Mohammed A. Elganiny Alaa Esmaeil Eldwer Irrigation Eng. and Hydraulics Department Faculty of Engineering Alexandria University Alexandria Egypt

The dynamic and accurate forecasting of monthly streamflow processes of a river are important in the management of extreme events such as floods and drought, optimal design of water storage structures and drainage network. Many Rivers are selected in this study: White Nile, Blue Nile, Atbara River and main Nile. This paper aims to recommend the best linear stochastic model in forecasting monthly streamflow in rivers. Two commonly hydrologic models: the deseasonalized autoregressive moving average (DARMA) models and seasonal autoregressive integrated moving average (SARIMA) models are selected for modeling monthly streamflow in all Rivers in the study area. Two different types of monthly streamflow data (deseasonalized data and differenced data) were used to develop time series model using previous flow conditions as predictors. The one month ahead forecasting performances of all models for predicted period were compared. The comparison of model forecasting performance was conducted based upon graphical and numerical criteria. The result indicates that deasonalized autoregressive moving average (DARMA) models perform better than seasonal autoregressive integrated moving average (SARIMA) models for monthly streamflow in Rivers.

关键词： Monthly Streamflow River Nile DARMA model SARIMA model stochastic model

stochastic and upscaled analytical modeling of fines migration in porous media induced by low-salinity water injection

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Applied Mathematics and Mechanics(English Edition) 2020年第3期41卷 491-506页

作者： Yulong YANG Weifeng YUAN Jirui HOU Zhenjiang YOU Jun LI Yang LIU Unconventional Petroleum Research Institute China University of PetroleumBeijing 102249China School of Chemical Engineering The University of QueenslandBrisbane QLD 4072Australia College of Petroleum Engineering China University of PetroleumBeijing 102249China

Fines migration induced by injection of low-salinity water(LSW) into porous media can lead to severe pore plugging and consequent permeability reduction. The deepbed filtration(DBF) theory is used to model the aforementioned phenomenon, which allows us to predict the effluent concentration history and the distribution profile of entrapped particles. However, the previous models fail to consider the movement of the waterflood front. In this study, we derive a stochastic model for fines migration during LSW flooding, in which the Rankine-Hugoniot condition is used to calculate the concentration of detached particles behind and ahead of the moving water front. A downscaling procedure is developed to determine the evolution of pore-size distribution from the exact solution of a large-scale equation system. To validate the proposed model,the obtained exact solutions are used to treat the laboratory data of LSW flooding in artificial soil-packed columns. The tuning results show that the proposed model yields a considerably higher value of the coefficient of determination, compared with the previous models, indicating that the new model can successfully capture the effect of the moving water front on fines migration and precisely match the effluent history of the detached particles.

关键词： low-salinity water(LSW)flooding fines migration stochastic model downscaling porous media waterflooding front exact solution

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机场到达时刻数量决策随机模型