Stochastic Simulation Model Research Articles

Three‐dimensional non‐stationary geometry‐based stochastic model for UAV‐MIMO Ricean fading channels

In this study, the authors propose a novel three-dimensional (3D) non-stationary geometry-based stochastic model (GBSM) and the corresponding deterministic and stochastic simulation models for non-isotropic unmanned aerial vehicle (UAV) multiple-input multiple-output (MIMO) Ricean fading channels. The proposed GBSM employs a two-cylinder model, where the signal is a superposition of line-of-sight component, single-bounced rays around the UAV, single-bounced rays around the ground station (GS), single-bounced rays on the ground, and double-bounced rays. The non-stationarity is reflected on the time-varying angles of departure and angles of arrival. Since the authors consider the GS to be mobile, the proposed 3D GBSM is general and suitable for UAV-aided vehicular communication scenarios. Based on the proposed model, some important channel statistical properties are derived and thoroughly investigated, including the space-time correlation function, Doppler power spectral density, envelope level crossing rate and average fade duration. Moreover, the proposed model has the ability to investigate the influence of some unique UAV-related parameters on channel characteristics and non-stationarity. Finally, excellent agreement is achieved between the reference model and simulation models, demonstrating the feasibility of simulation models as well as the correctness of theoretical derivations.

A stochastic schedule-following simulation model of bus routes

Microsimulation models of bus routes allow transit operators to both better understand the dynamics of bus routes and facilitate better policy making. Several simulation models of bus routes have been proposed in the literature, including cellular-automata, bus-following and traffic-following models. The majority of these approaches aim to simulate the interactions of a bus with other buses (the bus-following model), with passengers or the surrounding traffic (the traffic-following model), but they all fail to consider the important interactions between buses and their schedules. In a conventional schedule-based public transport system, bus drivers aim to arrive at each stop on time. This means that they will either speed up or slow down if their vehicles are not meeting the schedule. The research within this paper is a novel contribution to the literature of bus route simulation. We introduce the first schedule-following model where buses try to adhere to their schedule in a conventional schedule-based public transport system. A simulated numerical analysis shows the characteristics of the proposed schedule-following model and compares it to existing models. Finally, the model is calibrated using Automatic Vehicle Location and Smart Card data from Brisbane, Australia. The results show good model performance against the observed data. The model is relatively simple, yet the fundamental mechanisms that drive the model are novel and it has the potential to be applied in any city with well-defined bus schedules.

A Stochastic Model for Residential User Activity Simulation

User activities is an important input to energy modelling, simulation and performance studies of residential buildings. However, it is often difficult to obtain detailed data on user activities and related energy consumption data. This paper presents a stochastic model based on Markov chain to simulate user activities of the households with one or more family members, and formalizes the simulation processes under different conditions. A data generator is implemented to create fine-grained activity sequences that require only a small sample of time-use survey data as a seed. This paper evaluates the data generator by comparing the generated synthetic data with real data, and comparing other related work. The results show the effectiveness of the proposed modelling approach and the efficiency of generating realistic residential user activities.

A Long-Term Lens: Cumulative Impacts of Free-Roaming Cat Management Strategy and Intensity on Preventable Cat Mortalities.

This study used a previously developed stochastic simulation model (1) to estimate the impact of different management actions on free-roaming kitten and cat mortality over a 10-year period. These longer-term cumulative impacts have not been systematically examined to date. We examined seven management scenarios, including: (1) taking no action, (2) low-intensity removal, (3) high-intensity removal, (4) low-intensity episodic culling, (5) high-intensity episodic culling, (6) low-intensity trap-neuter-return (TNR), and (7) high-intensity TNR. For each scenario we tracked within the model the number of kittens born, the number of kittens surviving to adulthood, and the number of adults removed using lethal control over the entire 10-year simulation. We further defined all kitten deaths and lethal removal of adults as “preventable” deaths because they could potentially be reduced by certain management actions. Our simulation results suggested that the cumulative number of preventable deaths over 10 years for an initial population of 50 cats is highest for a “no-action” scenario, estimated at 1,000 deaths. It is lowest for a high-intensity TNR scenario, estimated at 32 deaths, a 31-fold difference. For all management scenarios tested, including removal and culling, the model predicted fewer preventable deaths than for a no-action scenario. For all management scenarios, the model predicted that the higher-intensity option (defined in terms of the proportion of animals sterilized or removed within a given time period) would result in fewer preventable deaths over time than the lower-intensity option. Based on these findings, we conclude that management intensity is important not only to reduce populations more quickly, but also to minimize the number of preventable deaths that occur over time. Accordingly, the lessons for the animal welfare community are both encouraging and cautionary. With sufficient intensity, management by TNR offers significant advantages in terms of combined lifesaving and population size reduction. At lower intensity levels, these advantages are greatly reduced or eliminated. We recommend that those who seek to minimize suffering and maximize lifesaving for free-roaming cats attempt to balance prospective goals (i.e., saving lives tomorrow) with proximate goals (i.e., saving lives today), and recognize that thoughtful choice of management strategies can ensure that both of these complementary goals are achieved.

A Stochastic Simulation Model for the Optimization of the Taxi Management System

The paper presents a simplified simulation model of the operation of a taxi system. The model retains the main features of a real taxi transportation system and despite its simplicity examines the system behavior in different conditions. It was shown that for every request generation rate a critical number of taxis in disposal could be determined. If the real number of taxis is lower than the critical number, the queue of pending requests grows in an unlimited way. On the other hand, if the real number of taxis is significantly higher, the service level is clearly not better and leads to the waste of taxi drivers’ time and fuel. The presented model can be regarded as a queue system; therefore, the well-known queue theory is used to describe its nature. However, this approach has some practical limitations coming from incomplete knowledge on real transportation demands, which additionally undergo significant fluctuations. A method, which optimizes the assignment of vacant taxis to the pending requests was also introduced. It was proven that this method mitigated the influence of the above-mentioned limitations.

Influence of Tissue Anisotropy on Molecular Communication.

Many biological tissues inside the human body exhibit highly anisotropic diffusion properties; for example, tissues of the nervous system and white matter in the brain. Here, we present an improved stochastic molecular communication framework to model interaction between bionanomachines in three-dimensional (3D) anisotropic brain micro-environment. The results obtained using stochastic particle-based simulation model are validated with analytical expressions. We also derive expressions for peak amplitude and peak time for the received molecular signal. The results demonstrate that the channel impulse response in anisotropic biological media depends significantly on the diffusion tensor as well as on the locations of the nanomachines.

Strengthening the scale-up and uptake of effective interventions for sex workers for population impact in Zimbabwe.

Introduction UNAIDS’ goal of ending AIDS by 2030 is unreachable without better targeting of testing, prevention and care. Female sex workers (FSW) in Zimbabwe are at high risk of HIV acquisition and transmission. Here, we report on collated programme and research data from Zimbabwe's national sex work programme. We also assess the potential for wider population impact of FSW programmes by modelling the impact on HIV incidence of eliminating transmission through FSW (i.e. calculate the population attributable fraction of incidence attributable to sex work).MethodsDescriptive analyses of individual‐level programme data collected from FSW between 2009 and June 2018 are triangulated with data collected through 37 respondent driven sampling surveys from 19 sites in Zimbabwe 2011 to 2017. We describe programme coverage, uptake, retention and patterns of sex work behaviour and gaps in service provision. An individual‐level stochastic simulation model is used to reconstruct the epidemic and then the incidence compared with the counter‐factual trend in incidence from 2010 had transmission through sex work been eliminated from that date.ResultsSisters has reached >67,000 FSW since 2009, increasing attendance as number of sites, programme staff and peer educators were increased. Over 57% of all FSW estimated to be working in Zimbabwe in 2017 (n = 40,000) attended the programme at least once. The proportion of young FSW reached has increased with introduction of the “Young Sisters programme.” There are no clear differences in pattern of sex work across settings. Almost all women report condom use with clients at last sex (95%); however, consistent condom use with clients in the last month varies from 52% to 95% by site. Knowledge of HIV‐positive status has increased from 48 to 78% between 2011 and 2016, as has prevalence of ART use among diagnosed women (29 to 67%). Although subject to uncertainty, modelling suggests that 70% (90% range: 32%, 93%) of all new infections in Zimbabwe from 2010 are directly or indirectly attributable to transmission via sex work.ConclusionsIt is feasible to increase coverage and impact of sex work programming through community‐led scale‐up of evidence‐based interventions. Eliminating transmission through commercial sex would likely have a substantial impact on new infections occurring more widely across Zimbabwe.

An examination of the effects of labor efficiency on the profitability of grass-based, seasonal-calving dairy farms

The seasonality of grass-based, seasonal-calving dairy systems results in disproportionately higher labor demands during the spring, when cows are calving, than in the remaining seasons. This study aimed to (1) examine the relationship between labor efficiency and profitability; (2) investigate strategies to reduce the hours worked per day by the farmer, family, and farm staff in the spring by having certain tasks outsourced; and (3) quantify the economic implications of those strategies. Data from an existing labor efficiency study on Irish dairy farms were used in conjunction with economic performance data from the farms. Tasks that required the highest level of farm labor per day in the spring were identified and hypothetical strategies to reduce the farm hours worked per day were examined. A stochastic budgetary simulation model was then used to examine the economic implications of employing these strategies and the effects of their use in conjunction with a proportionate increase in cow numbers that would leave the hours worked per day unchanged. The strategies were to use contractors to perform calf rearing, machinery work, or milking. Contracting out milking resulted in the greatest reduction in hours worked per day (5.6 h/d) followed by calf rearing (2.7 h/d) and machinery work (2 h/d). Reducing the hours worked per day by removing those tasks had slight (i.e., <5%) negative effects on profitability; however, maintaining the farm hours worked per day while utilizing the same strategies and increasing herd sizes resulted in profitable options. The most profitable scenario was for farms to increase herd size while contracting out milking.

Embedding optimization with deterministic discrete event simulation for assignment of cross-trained operators: An assembly line case study

In manufacturing systems where productivity is constrained by operators’ availability, cross-training strategies can be used to enable dynamic assignment of operators to workstations. However, finding an assignment approach that efficiently works under various system conditions is not trivial as, among other factors, the level of cross-training, the production duration and the initial conditions of the system can influence the assignment approach’s performance. To overcome this issue, in the case study presented in this paper, operator assignments have been modeled using a simulation-based optimization approach, with an “outer” optimizer that selects assignment-related parameters to simulate based on the system conditions, and an “inner” optimizer integrated with a simulation model that generates optimal assignments. For the case described here, which is modeled using a deterministic simulation model, the “outer” optimizer is an Ant Colony Optimizer (ACO) and the “inner” optimizer is a Binary Integer Programming (BIP) model. The ACO will select weights for the assignment objectives of the BIP multi-objective function so that throughput is maximized. The BIP is called by the system simulation model at fixed intervals or when a system status changes to assign operators to workstations. Results show that the simulation-based optimization approach generates higher throughput performance than static WIP-base assignment, especially when longer production duration are considered. The effects of cross-training and production duration on production throughput are also investigated. The simulation-optimization approach used can be abstracted to a framework where the “outer” and “inner” optimizers may be applied to different domains than the case study addressed here and can also be applied to stochastic simulation models.

Symbiotic simulation for the operational management of inpatient beds: model development and validation using \u0394-method

In many modern hospitals, resources are shared between patients who require immediate care, and must be dealt with as they arrive (emergency patients), and those whose care requirements are partly known to the hospital some time in advance (elective patients). Catering for these two types of patients is a challenging short-term operational decision-making problem, since some portion of each resource must be set aside for emergency patients when planning for the number and type of elective patients to admit. This paper shows how symbiotic simulation can help hospitals with important short-term operational decision making. We demonstrate how a symbiotic simulation model can be developed from an existing simulation model by adding the ability to load the state of the physical system at run-time and by making use of conditional length-of-stay distributions. The model is parameterised using 18 months of patient administrative data from an Anonymised General Hospital. Further, we propose a new Δ-Method that is suitable for validating a stochastic symbiotic simulation model. We demonstrate the benefit of our symbiotic simulation by showing how it can be used as an early warning system, and how additional patient-level information which might only become available after admission, can affect the predicted bed census.

Multivariate Nonstationary Oscillation Simulation of Climate Indices With Empirical Mode Decomposition

AbstractThe objective of the current study is to build a stochastic model to simulate climate indices that are teleconnected with the hydrologic regimes of large‐scale water resources systems such as the Great Lakes system. Climate indices generally contain nonstationary oscillations (NSOs). We adopted a stochastic simulation model based on Empirical Mode Decomposition (EMD). The procedure for the model is to decompose the observed series and then to simulate the decomposed components with the NSO resampling (NSOR) technique. Because the model has only been previously applied to single variables, a multivariate version of NSOR (M‐NSOR) is developed to consider the links between the climate indices and to reproduce the NSO process. The proposed M‐NSOR model is tested in a simulation study on the Rössler system. The simulation results indicate that the M‐NSOR model reproduces the significant oscillatory behaviors of the system and the marginal statistical characteristics. Subsequently, the M‐NSOR model is applied to three climate indices (i.e., Arctic Oscillation, El Niño‐Southern Oscillation, and Pacific Decadal Oscillation) for the annual and winter data sets. The results of the proposed model are compared to those of the Contemporaneous Shifting Mean and Contemporaneous Autoregressive Moving Average model. The results indicate that the proposed M‐NSOR model is superior to the Contemporaneous Shifting Mean and Contemporaneous Autoregressive Moving Average model for reproducing the NSO process, while the other basic statistics are comparatively well preserved in both cases. The current study concludes that the proposed M‐NSOR model can be a good alternative to simulate NSO processes and their teleconnections with climate indices.

Cross-entropy based importance sampling for stochastic simulation models

To efficiently evaluate system reliability based on Monte Carlo simulation, importance sampling is used widely. The optimal importance sampling density was derived in 1950s for the deterministic simulation model, which maps an input to an output deterministically, and is approximated in practice using various methods. For the stochastic simulation model whose output is random given an input, the optimal importance sampling density was derived only recently. In the existing literature, metamodel-based approaches have been used to approximate this optimal density. However, building a satisfactory metamodel is often difficult or time-consuming in practice. This paper proposes a cross-entropy based method, which is automatic and does not require specific domain knowledge. The proposed method uses an expectation–maximization algorithm to guide the choice of a mixture distribution model for approximating the optimal density. The method iteratively updates the approximated density to minimize its estimated discrepancy, measured by estimated cross-entropy, from the optimal density. The mixture model’s complexity is controlled using the cross-entropy information criterion. The method is empirically validated using extensive numerical studies and applied to a case study of evaluating the reliability of wind turbine using a stochastic simulation model.

Prevalence of porcine Taenia solium and Taenia hydatigena cysticercosis in Cameroon

Taenia solium, a zoonotic tapeworm species of human and pigs, has been targeted by the World Health Organisation (WHO) for elimination in endemic areas of Africa, Latin America and some parts of Asia. Unfortunately, the assessment of control measures against T. solium is complicated by the lack of specificity of current immunodiagnostic techniques for diagnosis of porcine cysticercosis. Many authors have concluded that the prevalence of T. hydatigena in pigs in Africa is low and extensive epidemiological studies on T. solium cysticercosis in pigs have been carried out using serological tests that cannot differentiate the taeniid species. To estimate and compare the prevalence of T. solium and T. hydatigena in pigs in Cameroon, we examined 416 slaughtered pigs at a pig abattoir in Yaoundé city with the animals originating from several parts of the country. At post-mortem inspection, 35 pigs (8.4%; 95% Confidence Interval (CI), 5.9–11.5 %) were found infected exclusively with T. solium and 27 (6.5%; 95% CI, 4.3–9.3 %) infected exclusively with T. hydatigena metacestodes. One animal was infected with T. solium and T. hydatigena. A stochastic simulation model which took into account the sensitivity of dissection of the tongue, external masseters and heart for diagnosis of T. solium porcine cysticercosis (TMH dissection test) and examination of mesentery, surfaces of the intestines and liver to identify T. hydatigena cysticerci in slaughtered pigs was developed to estimate the prevalence of these taeniid species. The results revealed that the actual prevalence of T. solium and T. hydatigena in Cameroon were 24.8% (95% CI, 20.1–30.5%) and 19.2% (95% CI, 15.1–24.1%), respectively. This study reports the highest porcine T. hydatigena prevalence ever in Africa and adjusted the prevalence of porcine T. solium infection obtained with genus-specific Ag-ELISA (Dorny et al., 2000) in Cameroon due to the presence of T. hydatigena.

Description, evaluation, and validation of the Teagasc Pig Production Model1.

The Teagasc Pig Production Model (TPPM), a stochastic simulation model of a farrow-to-finish pig farm, was developed to investigate effects of changes in production systems on farm profitability. The model simulates, on a weekly basis, the annual production of a farm. Biological [e.g., herd size, number of litters/sow/year, and mortality rates (%)], physical (e.g., infrastructure), and technical (e.g., feeding practices) variables and their associated costs are included as components of the model. These inputs are used to calculate physical (e.g., feed usage and number of pigs slaughtered) and financial (e.g., annual cash flow, profit and loss account, and balance sheet) outputs. The model was validated using the Delphi method and by comparing the TPPM outputs to data recorded on 20 Irish pig farms through the Teagasc e-Profit monitor system and with complete receipts for the year 2016. Results showed that the TPPM closely simulates physical and financial performance of pig farms indicating that the TPPM can be used with confidence to study pig production systems under Irish conditions. Model applicability was demonstrated by investigating the impact of 2 changes in technical performance: 1) building of extra accommodation to increase body weight (BW) at sale by 15 kg (EXTRA ROOM) and 2) a change in feeding practices by providing finisher feed from 28 kg of BW (EARLY FINISHER) compared with over 38 kg of BW. In both scenarios, the same biological parameters were used. Mortality rates, feed ingredients costs, and price per kg of meat produced were included as stochastic variables with the input distributions derived based on historical data simulated using Monte Carlo sampling using the Microsoft Excel add-in @Risk. Annual mean net profit was €198,101 (90% confidence interval [CI]: €119,606-€275,539) for the TPPM base farm, €337,078 (90% CI: €246,320-€426,809) for the EXTRA ROOM, and €225,598 (90% CI: €146,685-€303,590) for the EARLY FINISHER. EXTRA ROOM was associated with higher costs and required higher income to cover the additional costs. The 90% CI of the EARLY FINISHER was similar to the TPPM base farm while the EXTRA ROOM scenario resulted in a wider confidence interval, suggesting that a change in feeding practices could be a better option for farmers looking to improve profit with minimum investment. Thus, the TPPM could be used to facilitate decision making in farrow-to-finish pig farms.

Long-Run Management of Private Equity Investment

The authors present a stochastic simulation model that projects cash flows (capital calls and distributions) as well as unfunded commitment levels for private equity allocations over time. Their contribution links the underlying dynamics in the targeted private equity allocation to movements in the underlying portfolio while managing liquidity and rebalancing risks. Importantly, their model allows investors to assess the impact of changing the pace of annual commitment and of varying assumptions regarding capital calls, distributions, and underlying returns. Their model also offers insights into more-efficient approaches to building up an allocation to private equity strategies over time. <b>TOPICS:</b>Private equity, simulations, portfolio construction, risk management

Challenges in modelling the proportion of undiagnosed HIV infections in Sweden.

BackgroundSweden has a low HIV prevalence. However, among new HIV diagnoses in 2016, the proportion of late presenters and migrants was high (59% and 81%, respectively). This poses challenges in estimating the proportion of undiagnosed persons living with HIV (PLHIV).AimTo estimate the proportion of undiagnosed PLHIV in Sweden comparing two models with different demands on data availability and modelling expertise.MethodsAn individual-based stochastic simulation model of HIV positive populations (SSOPHIE) and the incidence method of the European Centre for Disease Prevention and Control (ECDC) HIV Modelling Tool were applied to clinical, surveillance and migration data from Sweden 1980–2016.ResultsSSOPHIE estimated that the proportion of undiagnosed PLHIV in 2013 was 26% (n = 2,100; 90% plausibility range (PR): 900–5,000) for all PLHIV, 17% (n = 600; 90% PR: 100–2,000) for men who have sex with men (MSM), 35% in male (n = 300; 90% PR: 200–700) and 34% in female (n = 400; 90% PR: 200–800) migrants from sub-Saharan Africa (SSA). The estimates for the ECDC model in 2013 were 21% (n = 2,013; 95% confidence interval (CI): 1,831–2,189) for all PLHIV, 15% (n = 369; 95% CI: 299–434) for MSM and 21% (n = 530; 95% CI: 436–632) for migrants from SSA.ConclusionsThe proportion of undiagnosed PLHIV in Sweden is uncertain. SSOPHIE estimates had wide PR. The ECDC model estimates were unreliable because migration was not accounted for. Better migration data and estimation methods are required to obtain reliable estimates of proportions of undiagnosed PLHIV in similar settings.

NMDAR-Mediated Ca2+ Increase Shows Robust Information Transfer in Dendritic Spines

A dendritic spine is a small structure on the dendrites of a neuron that processes input timing information from other neurons. Tens of thousands of spines are present on a neuron. Why are spines so small and many? We addressed this issue by using the stochastic simulation model of N-methyl-D-aspartate receptor (NMDAR)-mediated Ca2+ increase. NMDAR-mediated Ca2+ increase codes the input timing information between prespiking and postspiking. We examined how much the input timing information is encoded by Ca2+ increase against presynaptic fluctuation. We found that the input timing information encoded in the cell volume (103μm3) largely decreases against the presynaptic fluctuation, whereas that in the spine volume (10−1μm3) slightly decreases. Therefore, the input timing information encoded in the spine volume is more robust against presynaptic fluctuation than that in the cell volume. We further examined the mechanism of the robust information transfer in the spine volume. We demonstrated that the condition for the robustness is that the stochastic NMDAR-mediated Ca2+ increase (intrinsic noise) becomes much larger than the presynaptic fluctuation (extrinsic noise). When the presynaptic fluctuation is large, the condition is satisfied in the spine volume but not in the cell volume. Moreover, we compared the input timing information encoded in many small spines with that encoded in a single large spine. We found that the input timing information encoded in many small spines are larger than that in a single large spine when presynaptic fluctuation is large because of their robustness. Thus, robustness is a functional reason why dendritic spines are so small and many.

A 3-D Geometry-Based Stochastic Model for UAV-MIMO Wideband Nonstationary Channels

In this paper, we propose a 3-D geometry-based stochastic model for multi-input multioutput wideband nonstationary channels between the unmanned aerial vehicle (UAV) and the ground user. The proposed model uses a single concentric-cylinders model, where the signal is a superposition of a line-of-sight component and single-bounced rays. In order to describe the nonstationarity of the channel, time-varying angels have been properly derived and added in the proposed model. At the same time, based on the reference model, we derive the expressions of the channel statistical properties of the space–time–frequency correlation function and space-Doppler power spectral density. In addition, corresponding deterministic and stochastic simulation models are developed based on the reference model. Moreover, we investigate the influence of some important UAV-related parameters on channel statistical properties as well as the channel nonstationarity. Some interesting observations and conclusions are obtained, which are helpful for the design of UAV communication systems. Finally, the great agreement between simulation models and the reference model demonstrates not only the utility of simulation models but also the correctness of the theoretical derivations and simulations.

Modelling the potential of genetic control of malaria mosquitoes at national scale

BackgroundThe persistence of malaria in large parts of sub-Saharan Africa has motivated the development of novel tools to complement existing control programmes, including gene-drive technologies to modify mosquito vector populations. Here, we use a stochastic simulation model to explore the potential of using a driving-Y chromosome to suppress vector populations in a 106 km2 area of West Africa including all of Burkina Faso.ResultsThe consequence of driving-Y introductions is predicted to vary across the landscape, causing elimination of the target species in some regions and suppression in others. We explore how this variation is determined by environmental conditions, mosquito behaviour, and the properties of the gene-drive. Seasonality is particularly important, and we find population elimination is more likely in regions with mild dry seasons whereas suppression is more likely in regions with strong seasonality.ConclusionsDespite the spatial heterogeneity, we suggest that repeated introductions of modified mosquitoes over a few years into a small fraction of human settlements may be sufficient to substantially reduce the overall number of mosquitoes across the entire geographic area.

Discrete &amp;lt;i&amp;gt;k&amp;lt;/i&amp;gt;-nearest neighbor resampling for simulating multisite precipitation occurrence and model adaption to climate change

Abstract. Stochastic weather simulation models are commonly employed in water resources management, agricultural applications, forest management, transportation management, and recreational activities. Stochastic simulation of multisite precipitation occurrence is a challenge because of its intermittent characteristics as well as spatial and temporal cross-correlation. This study proposes a novel simulation method for multisite precipitation occurrence employing a nonparametric technique, the discrete version of the k-nearest neighbor resampling (KNNR), and couples it with a genetic algorithm (GA). Its modification for the study of climatic change adaptation is also tested. The datasets simulated from both the discrete KNNR (DKNNR) model and an existing traditional model were evaluated using a number of statistics, such as occurrence and transition probabilities, as well as temporal and spatial cross-correlations. Results showed that the proposed DKNNR model with GA-simulated multisite precipitation occurrence preserved the lagged cross-correlation between sites, while the existing conventional model was not able to reproduce lagged cross-correlation between stations, so long stochastic simulation was required. Also, the GA mixing process provided a number of new patterns that were different from observations, which was not feasible with the sole DKNNR model. When climate change was considered, the model performed satisfactorily, but further improvement is required to more accurately simulate specific variations of the occurrence probability.