Agent-based model development of a complex socio-ecological system: Methods for overcoming data and domain limitations
- James, C. L, Bradshaw, Karen L
- Authors: James, C. L , Bradshaw, Karen L
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/440189 , vital:73755 , xlink:href="https://doi.org/10.1016/j.ecoinf.2023.102224"
- Description: Agent-based models (ABMs) are appropriate tools for modelling socio-ecological systems due to their ability to handle complexity. However, development of such models is often an intensive process. There are many tutorials on the general methods and steps in ABM development but there are not necessarily practical details on how to overcome certain challenges. Honeybush (Cyclopia spp.), a kind of fynbos vegetation found in the Western and Eastern Cape mountains, is an important ecological and agricultural product in South Africa. It is considered a complex system due to its variability and unpredictability. The honeybush tea industry faces the challenge of meeting emerging market demands while maintaining sustainable harvesting practices, in the midst of an uncertain future due to climate change. We created a prototype model, HoneybushModel, using the MARS framework, a C# multi-agent simulation toolkit. The model was validated using historic data. Whilst outlining the development processes used to create the Honeybush Model, this paper provides a methodology for the development of such models and demonstrates techniques for addressing data, domain and framework limitations. The implemented model also acts as a case study for similar systems that could be modelled using an ABM.
- Full Text:
- Date Issued: 2023
- Authors: James, C. L , Bradshaw, Karen L
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/440189 , vital:73755 , xlink:href="https://doi.org/10.1016/j.ecoinf.2023.102224"
- Description: Agent-based models (ABMs) are appropriate tools for modelling socio-ecological systems due to their ability to handle complexity. However, development of such models is often an intensive process. There are many tutorials on the general methods and steps in ABM development but there are not necessarily practical details on how to overcome certain challenges. Honeybush (Cyclopia spp.), a kind of fynbos vegetation found in the Western and Eastern Cape mountains, is an important ecological and agricultural product in South Africa. It is considered a complex system due to its variability and unpredictability. The honeybush tea industry faces the challenge of meeting emerging market demands while maintaining sustainable harvesting practices, in the midst of an uncertain future due to climate change. We created a prototype model, HoneybushModel, using the MARS framework, a C# multi-agent simulation toolkit. The model was validated using historic data. Whilst outlining the development processes used to create the Honeybush Model, this paper provides a methodology for the development of such models and demonstrates techniques for addressing data, domain and framework limitations. The implemented model also acts as a case study for similar systems that could be modelled using an ABM.
- Full Text:
- Date Issued: 2023
Agent-Based Modeling and Simulation for Transmission Dynamics and Surveillance of Dengue: Conceptual and Design Model
- Pascoe, Luba, Nyambo, Devotha G, Bradshaw, Karen L, Clemen, Thomas
- Authors: Pascoe, Luba , Nyambo, Devotha G , Bradshaw, Karen L , Clemen, Thomas
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/440200 , vital:73756 , xlink:href="https://doi.org/10.1109/AFRICON55910.2023.10293299"
- Description: African countries need to strengthen surveillance and control of arboviral diseases such as dengue due to increased outbreaks and spread of arboviruses. Climatic, socio-environment, and ecological variables influence the spread of dengue fever in Sub-Saharan Africa. This paper presents an Agent-Based conceptual and design model for dengue fever developed using the Multi-Agent Research and Simulation (MARS) framework. The study analyzes dengue fever's spatial distribution and identifies the causal relationship between the disease and its climatic and environmental variables. Agent-based modeling (ABM) was used to comprehend the spatial patterns of variation to determine the ecological association between the observed spatio-temporal variations in dengue fever. The domain and design model of an ABM for the surveillance of dengue fever is presented based on the Overview, Design Concepts, and Details (ODD) protocol. Model input parameters and input data for the study area are also presented. The dengue ABM can be adopted and reused for modeling other diseases and other complex problems from different domains while ensuring that their unique characteristics and appropriate modifications are considered to ensure the model's validity and relevance to the new context.
- Full Text:
- Date Issued: 2023
- Authors: Pascoe, Luba , Nyambo, Devotha G , Bradshaw, Karen L , Clemen, Thomas
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/440200 , vital:73756 , xlink:href="https://doi.org/10.1109/AFRICON55910.2023.10293299"
- Description: African countries need to strengthen surveillance and control of arboviral diseases such as dengue due to increased outbreaks and spread of arboviruses. Climatic, socio-environment, and ecological variables influence the spread of dengue fever in Sub-Saharan Africa. This paper presents an Agent-Based conceptual and design model for dengue fever developed using the Multi-Agent Research and Simulation (MARS) framework. The study analyzes dengue fever's spatial distribution and identifies the causal relationship between the disease and its climatic and environmental variables. Agent-based modeling (ABM) was used to comprehend the spatial patterns of variation to determine the ecological association between the observed spatio-temporal variations in dengue fever. The domain and design model of an ABM for the surveillance of dengue fever is presented based on the Overview, Design Concepts, and Details (ODD) protocol. Model input parameters and input data for the study area are also presented. The dengue ABM can be adopted and reused for modeling other diseases and other complex problems from different domains while ensuring that their unique characteristics and appropriate modifications are considered to ensure the model's validity and relevance to the new context.
- Full Text:
- Date Issued: 2023
An Exploration of Flow Control Using Machine Learning and Computational Fluid Dynamics
- Bradshaw, Karen L, Cornfield Matthew
- Authors: Bradshaw, Karen L , Cornfield Matthew
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/440211 , vital:73757 , xlink:href="https://doi.org/10.59200/ICARTI.2023.017"
- Description: Although numerous studies relating to computational fluid dynamics and machine learning have been conducted in relation to automotive development, the majority focus on either early development using completed 3D models, or the final testing stages of development, or machine learning accelerated computational fluid dynamic simulations. While this approach is helpful in software development and simulation, it is not easily adaptable to automotive design where the final model is constantly changing and being modified. Consequently, the aim of this study is to propose a method for conducting computational fluid dynamics and machine learning concurrently to accelerate the development process. The proposed method is used to design and improve the aerodynamic efficiency of an object. The approach focuses on developing, implementing, and comparing machine learning models capable of generating optimised three-dimensional objects with the required geometry to direct airflow paths required in applications such as pressure generation, as needed for both active and passive flow control. The study concludes that both decision tree regression and long short-term memory (LSTM) autoencoder models could be used to optimise the aerodynamic efficiency of solid bodies, but that the LSTM autoencoder performs better overall. An undesirable effect of the shape optimisation is an overall reduction in shape size as optimization increases.
- Full Text:
- Date Issued: 2023
- Authors: Bradshaw, Karen L , Cornfield Matthew
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/440211 , vital:73757 , xlink:href="https://doi.org/10.59200/ICARTI.2023.017"
- Description: Although numerous studies relating to computational fluid dynamics and machine learning have been conducted in relation to automotive development, the majority focus on either early development using completed 3D models, or the final testing stages of development, or machine learning accelerated computational fluid dynamic simulations. While this approach is helpful in software development and simulation, it is not easily adaptable to automotive design where the final model is constantly changing and being modified. Consequently, the aim of this study is to propose a method for conducting computational fluid dynamics and machine learning concurrently to accelerate the development process. The proposed method is used to design and improve the aerodynamic efficiency of an object. The approach focuses on developing, implementing, and comparing machine learning models capable of generating optimised three-dimensional objects with the required geometry to direct airflow paths required in applications such as pressure generation, as needed for both active and passive flow control. The study concludes that both decision tree regression and long short-term memory (LSTM) autoencoder models could be used to optimise the aerodynamic efficiency of solid bodies, but that the LSTM autoencoder performs better overall. An undesirable effect of the shape optimisation is an overall reduction in shape size as optimization increases.
- Full Text:
- Date Issued: 2023
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