Environmental Simulation Models Research Articles

Evaluating future flood risk mitigation capacities in major cities across China

Rapid urbanization and climate change have significantly altered urban hydrological processes. Previous research has highlighted their adverse effects on escalating risk of urban flood. Nevertheless, few assessments have quantified how future urban flood risk mitigation capacities evolve under various urban expansion and climate change scenarios. This study simulates urban expansion of major cities across China using future urban-regional environment simulation model at 30-m resolution under shared socioeconomic pathways scenarios (SSP). Concurrently, future precipitation intensity changes are assessed based on climate projection data. Finally, the urban flood risk mitigation capacity impacted by urban expansion and climate change is evaluated using the soil conservation service model. The results reveal: (1) The sizes of major cities are projected to expand 30% by 2050, and the north and east China are primary urban growth poles. (2) Major cities' precipitation intensity increase 0.4% in SSP126, but decline 5.4% and 2.3% in SSP245 and SSP585. (3) The flood risk mitigation capacities of major cities decrease 2.7% in SSP126 and 2.1% in SSP585, but increase 0.2% in SSP245. Most cities experiencing capacity decrease are located in the north and east China. These results provide valuable insights for the formulation of future urban flood adaptation and mitigation strategies.

Potential thermal constraints for sustainable industrial city: refined simulation and prediction of extreme urban heat island effect

Extreme urban heat islands (E-UHIs) have caused radical changes in urban climates. The lack of refined extreme thermal environment simulation models (ETESM) hinders research on the spatial evolution of E-UHI, which increases the uncertainty for managers to make sustainable decisions. Here, considering both the heating of industrial land and the heat transfer of surrounding facilities, a refined ETESM is constructed. Assuming four scenarios: maintain current trend (MtS), economically oriented scenario (EcS), environmentally oriented scenario (EnS), green transformation scenario (GtS), the future potential constraints of the E-UHI are simulated and predicted, thus determining the sustainable development decision in industrial city. The results indicate that there is significant spatial consistency between simulated temperature and actual temperature at both city scale (Pearson'r = 0.924, P<0.001) and industrial land scale (Pearson'r = 0.948, P<0.001). The average temperature under four scenarios shows: EcS>MtS>GtS>EnS. The average SST of EnS and GtS was 1.36℃ and 1.06℃ lower than that of MtS, respectively. Considering the dual constraints of environmental livability and economic sustainability, GtS is the best decision to achieve the sustainable development in industrial city. This simulation-driven study provides support for E-UHI mitigation and sustainable development of industrial city.

Использование метода дискретных элементов для имитационного моделирования выемки угля очистным комбайном

Timulation modeling as a method of studying complex technological processes is successfully used in mining. Due to the creation of digital models, it is possible to estimate and improve the efficiency of technical and organizational solutions used in difficult mining and geological conditions. The article describes the development of a simulation model of coal extraction by a shearer and its transportation to an armored conveyer. Fragmentation of a high coal seam was simulated under the action of the cutting planes of the shearer. The created model in the Rocky DEM simulation environment takes into account the physical and mechanical properties of materials and 3D structures of mining equipment. The method of discrete elements was used as a mathematical tool for computing fragmentation of the coalbed, which helped to perform the computing without loss of the initial mass and volume of the materials. During the validation of the simulation model, a series of tests were carried out to determine the angle of natural slope of non-spherical coal particles close in granulometric composition to the mass formed during the fragmentation of the coalbed. Based on the test results, an array of values of coal particle adhesion parameters was obtained, which allows conducting simulation studies in conditions of existing and projected coal mines.

MUSE-RASA captures human dimension in climate-energy-economic models via global geoAI-ML agent datasets

This article provides a combined geospatial artificial intelligence-machine learning, geoAI-ML, agent-based, data-driven, technology-rich, bottom-up approach and datasets for capturing the human dimension in climate-energy-economy models. Seven stages were required to conduct this study and build thirteen datasets to characterise and parametrise geospatial agents in 28 regions, globally. Fundamentally, the methodology starts collecting and handling data, ending with the application of the ModUlar energy system Simulation Environment (MUSE), ResidentiAl Spatially-resolved and temporal-explicit Agents (RASA) model. MUSE-RASA uses AI-ML-based geospatial big data analytics to define eight scenarios to explore long-term transition pathways towards net-zero emission targets by mid-century. The framework and datasets are key for climate-energy-economy models considering consumer behaviour and bounded rationality in more realistic decision-making processes beyond traditional approaches. This approach defines energy economic agents as heterogeneous and diverse entities that evolve in space and time, making decisions under exogenous constraints. This framework is based on the Theory of Bounded Rationality, the Theory of Real Competition, the theoretical foundations of agent-based modelling and the progress on the combination of GIS-ABM.

Research on Scenario Modeling for V-Tail Fixed-Wing UAV Dynamic Obstacle Avoidance

With the advantages of long-range flight and high payload capacity, large fixed-wing UAVs are often used in anti-terrorism missions, disaster surveillance, and emergency supply delivery. In the existing research, there is little research on the 3D model design of the V-tail fixed-wing UAV and 3D flight environment modeling. The study focuses on designing a comprehensive simulation environment using Gazebo and ROS, referencing existing large fixed-wing UAVs, to design a V-tail aircraft, incorporating realistic aircraft dynamics, aerodynamics, and flight controls. Additionally, we present a simulation environment modeling approach tailored for obstacle avoidance in no-fly zones, and have created a 3D flight environment in Gazebo, generating a large-scale terrain map based on the original grayscale heightmap. This terrain map is used to simulate potential mountainous terrain threats that a fixed-wing UAV might encounter during mission execution. We have also introduced wind disturbances and other specific no-fly zones. We integrated the V-tail fixed-wing aircraft model into the 3D flight environment in Gazebo and designed PID controllers to stabilize the aircraft’s flight attitude.

Technical note: The CREDIBLE Uncertainty Estimation (CURE) toolbox: facilitating the communication of epistemic uncertainty

Abstract. There is a general trend toward the increasing inclusion of uncertainty estimation in the environmental modelling domain. We present the Consortium on Risk in the Environment: Diagnostics, Integration, Benchmarking, Learning and Elicitation (CREDIBLE) Uncertainty Estimation (CURE) toolbox, an open-source MATLABTM toolbox for uncertainty estimation aimed at scientists and practitioners who are not necessarily experts in uncertainty estimation. The toolbox focusses on environmental simulation models and, hence, employs a range of different Monte Carlo methods for forward and conditioned uncertainty estimation. The methods included span both formal statistical and informal approaches, which are demonstrated using a range of modelling applications set up as workflow scripts. The workflow scripts provide examples of how to utilize toolbox functions for a variety of modelling applications and, hence, aid the user in defining their own workflow; additional help is provided by extensively commented code. The toolbox implementation aims to increase the uptake of uncertainty estimation methods within a framework designed to be open and explicit in a way that tries to represent best practice with respect to applying the methods included. Best practice with respect to the evaluation of modelling assumptions and choices, specifically including epistemic uncertainties, is also included by the incorporation of a condition tree that allows users to record assumptions and choices made as an audit trail log.

Development of the concept of simulation modeling of the ecological situation based on the theory of self-organization

The article is devoted to the development of a concept of simulation of the environmental situation based on the theory of self-organization. The trend of increasing temperature and sea level due to an increase in the level of greenhouse gases in the Earth’s atmosphere is global. This indicates the need to predict the development of the environmental situation in order to inform the population about possible disasters for preparing people's responses, resettlement planning, psychological adaptation. To predict the environmental situation as a result of accidents and disasters, including in rail transport, an analysis apparatus for possible situations is proposed with the aim of developing recommendations for their prevention. An environmental simulation model based on the theory of self-organization was chosen as the apparatus of research. It is based on the processes of mutually agreed relations, as a result of changes in two species of wildlife (object and environment), leading to the formation of a certain orderliness of these relations. Interaction of this kind occurs in the direction of achieving the existing equilibrium state or the formation of a new balance.

Study on Electromagnetic Radiation Characteristics Based on HTS Maglev Levitation Test Line

As a new type of magnetic levitation train with the characteristics of self-stabilization and self-suspension, high-temperature superconducting magnetic levitation has developed to the test line research stage. In order to promote the rapid development of high-temperature superconducting magnetic levitation train engineering, and the main electromagnetic radiation sources are clarified by analyzing their working principles and structures. Then Ansoft Maxwell EM was used to build a 3D magnetic levitation train electromagnetic environment simulation model to simulate and predict the electromagnetic radiation characteristics of the magnetic levitation train system. Finally, a field EMF test was carried out to verify and assess the impact on the EM environment in the system. The results show that the permanent magnet track on the ground and the synchronous linear motor are the primary electromagnetic radiation sources, and the generated fields are mainly low-frequency fields and static magnetic fields. The low-frequency magnetic field inside the train decreases with the increase of frequency and is partially shielded by the carriage; The static magnetic field cannot be weakened by the carriage, and the static magnetic field inside the car decreases with the increase of height. All types of electromagnetic fields are far below the requirements of the relevant electromagnetic environmental standard limits, and have no effect on the electromagnetic radiation safety of the personnel inside the train and the surrounding environment; In considering of the special people who have pacemakers, static magnetic field suppression measures are studied, and the results show that the trains with high magnetic permeability permalloy as the shielding layer at the bottom of the vehicle greatly lower the static magnetic field within the train.

Simulation Modeling of Mobile Robotic Complexes Tool Analysis According to Physical Laws (a Review)

This article is about the technologies of simulation modeling of mobile robotic complexes. The basic demands for a simulation environment are defined such as realistic results, open-source code, extensibility, performance of system, possibility of usage of low-level code for simulation. Moreover, the most significant characteristics for physics engines are described in the article, specifically solid-state physics, torque setup, stability of "axle" connection, stability of the simulated many-body system. The frequently used physics engines such as PhysX, ODE, MuJoCo, Bullet, Havok are analyzed and the search of modeling services is made in order to make the comparative table. The platform Unity is in focus of this work to show its abilities in modeling of mechanical and electronic parts of mobile robots. The aim of this work is minimizing mobile robots development outgoings. Programming product is given as result of simulation modeling of robotic complexes. The Unity platform is used as the engine for development of simulation tools for mechanical and electronic parts of robotic complex. The set of tools based on Unity engine is developed in order to create virtual models of mobile robots. The architecture of the project was developed in order to determine aspects of creating mobile robots in a simulation modeling environment. The logic of scripts for modeling the solid-state physics, kinematic chains and joints with different degrees of freedom is presented as a result of developing architecture of the project. The package was tested, a model of a mobile four-wheeled robotic platform was built as a result of the test.

Acausal equation-based and object-oriented modeling of heating systems: The College Thermal library

With the growing demand for adequate thermal comfort and increasingly sophisticated and interconnected energy systems, software tools able to easily and effectively study complex thermal systems need to be developed. Here, a novel acausal equation-based and object-oriented modeling (OOM) approach is used to model the seasonal performance of a residential heating system taken as a case study. To this end, a purpose-built library named College Thermal was developed using Modelica language in the Wolfram System Modeler simulation environment. In detail, dynamic simulations under varying operating conditions were performed to assess the ability of the system to ensure the design conditions, and to estimate the resulting energy consumption during the heating season. Then, the effect of key parameters of the thermal components on the overall energy performance of the system is analyzed. The developed OOM library, which is characterized by interoperability, modularity, and scalability features and thus could easily incorporate extensions/evolutions of the studied system, would be prospectively suitable for optimizing system layouts and testing control strategies in various thermal engineering applications.

Lake volume and potential hazards of moraine-dammed glacial lakes – a case study of Bienong Co, southeastern Tibetan Plateau

Abstract. The existence of glacial lakes in the southeastern Tibetan Plateau (SETP) is a potential hazard to downstream regions, as the outburst of such lakes has the potential to result in disastrous glacial lake outburst floods (GLOFs). In the present study, we conducted a comprehensive investigation of Bienong Co, a moraine-dammed glacial lake in the SETP. First, the lake basin morphology was determined, and the lake volume was estimated, showing that the maximum lake depth is ∼181 m and the lake volume is ∼102.3×106 m3. These scenarios included the possibility of GLOFs being triggered by ice avalanches (Scenarios A1–3) from the mother glacier or by landslides from the lateral moraines (Scenarios B1–3 and C1–3). Avalanche volumes of the nine trigger scenarios were obtained from the Rapid Mass Movement Simulation (RAMMS) modeling results. Next, the Basic Simulation Environment for Computation of Environmental Flow and Natural Hazard Simulation (BASEMENT) model was used to simulate the generation and propagation of the avalanche-induced displacement waves in the lake. With the model, the overtopping flows and erosion on the moraine dam and the subsequent downstream floods were also simulated. The results indicate that the ice avalanche scenario may cause the largest mass volume entering the lake, resulting in a displacement wave up to 25.2 m in amplitude (Scenario A3) near the moraine dam. Landslide scenarios with smaller volumes entering the lake result in smaller displacement waves. Scenarios A1, A2, and A3 result in released water volumes from the lake of 24.1×106, 25.3×106, and 26.4×106 m3, respectively. Corresponding peak discharges at the moraine dam are 4996, 7817, and 13 078 m3 s−1, respectively. These high discharges cause erosion of the moraine dam, resulting in breach widths of 295, 339, and 368 m, respectively, with the generally similar breach depth of approximately 19 m. In landslide scenarios, only overtopping flows generated by Scenarios B3 and C3 cause erosion on the moraine dam, with breach depths of 6.5 and 7.9 m and breach widths of 153 and 169 m, respectively. According to our simulations, GLOFs generated by Scenarios A1–3 all flow through 18 settlements downstream in 20 h, threatening more than half of them. Both Scenarios B3 and C3 produce GLOFs that flow through the first eight settlements downstream in 20 h and have a relatively small impact on them. Comparisons of the area, depth, and volume of glacial lakes for which the bathymetry data are available show that Bienong Co is the deepest known glacial lake with the same surface area on the Tibetan Plateau. This study could provide a new insight into moraine-dammed glacial lakes in the SETP and be a valuable reference for GLOF disaster prevention for the local government.

Deep Reinforcement Learning-Based Joint Optimization Control of Indoor Temperature and Relative Humidity in Office Buildings

Indoor temperature and relative humidity control in office buildings is crucial, which can affect thermal comfort, work efficiency, and even health of the occupants. In China, fan coil units (FCUs) are widely used as air-conditioning equipment in office buildings. Currently, conventional FCU control methods often ignore the impact of indoor relative humidity on building occupants by focusing only on indoor temperature as a single control object. This study used FCUs with a fresh-air system in an office building in Beijing as the research object and proposed a deep reinforcement learning (RL) control algorithm to adjust the air supply volume for the FCUs. To improve the joint control satisfaction rate of indoor temperature and relative humidity, the proposed RL algorithm adopted the deep Q-network algorithm. To train the RL algorithm, a detailed simulation environment model was established in the Transient System Simulation Tool (TRNSYS), including a building model and FCUs with a fresh-air system model. The simulation environment model can interact with the RL agent in real time through a self-developed TRNSYS–Python co-simulation platform. The RL algorithm was trained, tested, and evaluated based on the simulation environment model. The results indicate that compared with the traditional on/off and rule-based controllers, the RL algorithm proposed in this study can increase the joint control satisfaction rate of indoor temperature and relative humidity by 12.66% and 9.5%, respectively. This study provides preliminary direction for a deep reinforcement learning control strategy for indoor temperature and relative humidity in office building heating, ventilation, and air-conditioning (HVAC) systems.

Maximum Entropy Exploration in Contextual Bandits with Neural Networks and Energy Based Models.

Contextual bandits can solve a huge range of real-world problems. However, current popular algorithms to solve them either rely on linear models or unreliable uncertainty estimation in non-linear models, which are required to deal with the exploration-exploitation trade-off. Inspired by theories of human cognition, we introduce novel techniques that use maximum entropy exploration, relying on neural networks to find optimal policies in settings with both continuous and discrete action spaces. We present two classes of models, one with neural networks as reward estimators, and the other with energy based models, which model the probability of obtaining an optimal reward given an action. We evaluate the performance of these models in static and dynamic contextual bandit simulation environments. We show that both techniques outperform standard baseline algorithms, such as NN HMC, NN Discrete, Upper Confidence Bound, and Thompson Sampling, where energy based models have the best overall performance. This provides practitioners with new techniques that perform well in static and dynamic settings, and are particularly well suited to non-linear scenarios with continuous action spaces.

Integrating ecosystem services conservation into the optimization of urban planning policies in eco-fragile areas: A scenario-based case study

Alleviating the contradiction between land use change and natural ecosystem protection is an urgent problem that needs to be solved in the context of rapid urbanization. Urban expansion from 2015 to 2035 is simulated under three scenarios in this study using the Future Urban-Regional Environment Simulation (FUTURES) model, with Hohhot, China as the research area. Then, the value of crucial ecosystem services and the trade-offs or synergies between them are calculated for past and future periods. We find that urban expansion is dominated by the occupation of cropland and grassland, leading to the losses of ecosystem services. Especially under the economic priority (EP) scenario, food production may be reduced by up to 2.98 % and soil conservation may be reduced by up to 1.04 %. The directionality of the relationships between ecosystem services will not be reversed under any scenario. Our results also emphasize that although ecosystem services have been effectively protected under the ecological conservation (EC) scenario, it may increase the trade-off intensity among them. By considering the assessment of ecosystem services and changes in their interactions when choosing the urban expansion pattern, managers can optimize planning policies to ensure sustainable urban and community management.

Collaborative Watershed Modeling as Stakeholder Engagement Tool for Science-Based Water Policy Assessment in São Paulo, Brazil

This study describes a collaborative modeling process deployed at the Cantareira Water Supply System (CWSS) in São Paulo City Metropolitan Area, Brazil. The CWSS faces challenges for meeting the increasing water demand, while land-use and climate change and their combined effect on its water cycle and balance have created a complex water resources management problem. Through a stakeholder engagement process—involving scientists and policymakers, the water utility company, and state administration—environmental simulation models were developed to elicit and represent multiple environmental, economic, and policy perspectives, developing a mutual language to communicate and establish common goals of water resources management. Study outputs include estimation of biophysical and economic benefits associated with prioritized native vegetation restoration activities in the source watersheds. These outputs are deployed in support of landscape planning and the decision process integrating multiple stakeholder perspectives in São Paulo state administration, the water utility company, and municipalities.

A basic macroeconomic agent-based model for analyzing monetary regime shifts.

In macroeconomics, an emerging discussion of alternative monetary systems addresses the dimensions of systemic risk in advanced financial systems. Monetary regime changes with the aim of achieving a more sustainable financial system have already been discussed in several European parliaments and were the subject of a referendum in Switzerland. However, their effectiveness and efficacy concerning macro-financial stability are not well-known. This paper defines the economic requirements for modeling the current monetary system and introduces the corresponding macroeconomic agent-based model (MABM) in a continuous-time stochastic agent-based simulation environment with a provenance model. This MABM aims to present a starting point for exploring and analyzing monetary reforms. In this context, the monetary system affects the lending potential of banks and might impact the dynamics of financial crises. MABMs are predestined to replicate emergent financial crisis dynamics, analyze institutional changes within a financial system, and thus measure macro-financial stability. The used simulation environment makes the model more accessible and facilitates exploring the impact of different hypotheses and mechanisms in a less complex way. Moreover, the model replicates a wide range of stylized economic facts, which validates it as an analysis tool to implement and compare monetary regime shifts.

Spatio-temporal analysis of the environmental ranges and phenotypic traits of Zostera muelleri subpopulations in Central Queensland

Intraspecific variation in phenotypic traits of seagrasses enables populations to tolerate varying environmental conditions and facilitates population persistence and expansion. Significant variation in seagrass population morphometrics may reflect highly plastic responses to abiotic and biotic factors, allowing populations to acclimate to changing environmental conditions. Alternatively, morphological variation may arise from developmental canalisation or genetically determined fixed traits, which may inhibit future plasticity and adaptive flexibility to changing environmental conditions. Understanding the drivers behind intraspecific variation in phenotypic traits is important for seagrass restoration practitioners when identifying populations that may provide suitable donor propagules with high adaptability to new sites or propagules that may be more resilient to future environmental changes. This study aimed to investigate whether variation in morphological traits of four subpopulations of Zostera muelleri on the Curtis Coast of the Great Barrier Reef World Heritage Area, Queensland, correlated with meadow-specific environmental conditions and whether this varied between seasons. Morphometric analysis was conducted on Z. muelleri apical shoots, sampled during the spring growing/flowering season and autumn senescent season. Meadow-specific environmental conditions were assessed using historical data dating back to 2008 and environmental simulation models developed for predicting wave, and light conditions. Results indicated that Z. muelleri subpopulations exhibited distinct morphologies throughout their seasonal growth cycle that may correlate with meadow-specific differences in sediment composition, hydrodynamic stress, and light availability. However, further investigation is required to understand if expressed morphological traits are reflective of environmentally driven phenotypic plasticity or genetically predetermined fixed traits (local adaptation or developmental canalisation). Knowledge gained from further studies into the drivers behind the observed variations in morphological traits would increase the understanding of the importance of trait-based selection of propagules for seagrass restoration meadows’ resilience and persistence.

Environmental Simulation Model for Rapid Prediction of Tea Seedling Growth

Accurate and effective monitoring of environmental parameters in tea seedling greenhouses is an important basis for regulating the seedling environment, which is crucial for improving the seedling growth quality. This study proposes a tea seedling growth simulation (TSGS) model based on deep learning. The Internet of Things system was used to measure environmental change during the whole seedling process. The correlation between the environmental parameters and the biomass growth of tea seedlings in various varieties was analyzed. A CNN-LSTM network was proposed to build the TSGS model of light, temperature, water, gas, mineral nutrition, and growth biomass. The results showed that: (1) the average correlation coefficients of air temperature, soil temperature, and soil moisture with the biomass growth of tea seedlings were 0.78, 0.84, and −0.63, respectively, which were three important parameters for establishing the TSGS model. (2) For evaluating the TSGS model of a single variety, the accuracy of ZM’s TSGS based on the CNN-LSTM network was the highest (Rp2 = 0.98, RMSEP = 0.14). (3) For evaluating the TSGS model of multiple varieties, the accuracy of TSGS based on the CNN-LSTM network was the highest (Rp2 = 0.96, RMSEP = 0.17). This study provided effective technical parameters for intelligent control of tea-cutting growth and a new method for rapid breeding.

Deep Learning in Security of Internet of Things

Internet-of-Things (IoT) technology is increasingly prominent in the current stage of social development. All walks of life have begun to implement the IoT integration technology, so as to strive to promote industrial modernization, intelligence, and digitalization. In this case, how to link high-risk network activities with entities has become the primary issue for promoting industrial development. However, at this stage, the security issues in the development of the IoT technology have contradictions that are difficult to resolve. According to this situation, how to make system defense intelligent and replace manual monitoring has become the future of the development of security architecture. This article combines existing security research to explore the possibility of deep learning (DL) in upgrading the IoT security architecture, discusses how the IoT can identify and respond to cyber attacks, and how to encrypt edge data transmission. Moreover, this article discusses security research in application fields, such as Industrial IoT, Internet of Vehicles, smart grid, smart home, and smart medical. Then, we summarized the areas that can be improved in future technological development, including sharing computing power through the edge network processing unit (NPU) central device and closely combining the environmental simulation model with the actual environment, as well as malicious code detection, intrusion detection, production safety, vulnerability detection, fault diagnosis, and blockchain technology.

Optimization of Parameters for Pedestrian Simulation Software: A Case Study of Emergency Evacuation Experiments

This research study provides a new framework to optimize pedestrian simulation parameters required to replicate emergency evacuation conditions specifically observed in high rise buildings, passenger vehicles and transport terminals. The simulation experiments are performed with generalised social force model in VISWALK simulation environment. The data required for optimization are obtained by conducting several evacuation experiments. The results indicate that pedestrian behavioural aspects, such as the relaxation time, distance from the other pedestrian and force exerted by closest pedestrians, have a significant impact on total evacuation time. The outcomes are in consistence with field observations and the calibrated parameters would help in simulating future scenarios including building level simulations and further simulating emergency evacuation of passengers from transport terminals and vehicles.