ORCID Profile
0000-0003-2325-1032
Current Organisation
Bond University
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Publisher: Elsevier BV
Date: 2022
Publisher: Springer International Publishing
Date: 2015
Publisher: Springer Science and Business Media LLC
Date: 1999
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11504894_31
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: WORLD SCIENTIFIC
Date: 1998
Publisher: Springer Berlin Heidelberg
Date: 2002
Publisher: Public Library of Science (PLoS)
Date: 09-02-2022
DOI: 10.1371/JOURNAL.PONE.0262402
Abstract: In many parts of the world, conditions for small scale agriculture are worsening, creating challenges in achieving consistent yields. The use of automated decision support tools, such as Bayesian Belief Networks (BBNs), can assist producers to respond to these factors. This paper describes a decision support system developed to assist farmers on the Mekong Delta, Vietnam, who grow both rice and shrimp crops in the same pond, based on an existing BBN. The BBN was previously developed in collaboration with local farmers and extension officers to represent their collective perceptions and understanding of their farming system and the risks to production that they face. This BBN can be used to provide insight into the probable consequences of farming decisions, given prevailing environmental conditions, however, it does not provide direct guidance on the optimal decision given those decisions. In this paper, the BBN is analysed using a novel, temporally-inspired data mining approach to systematically determine the agricultural decisions that farmers perceive as optimal at distinct periods in the growing and harvesting cycle, given the prevailing agricultural conditions. Using a novel form of data mining that combines with visual analytics, the results of this analysis allow the farmer to input the environmental conditions in a given growing period. They then receive recommendations that represent the collective view of the expert knowledge encoded in the BBN allowing them to maximise the probability of successful crops. Encoding the results of the data mining/inspection approach into the mobile Decision Support System helps farmers access explicit recommendations from the collective local farming community as to the optimal farming decisions, given the prevailing environmental conditions.
Publisher: Elsevier BV
Date: 09-2002
Publisher: Springer Science and Business Media LLC
Date: 28-02-2023
DOI: 10.1007/S11269-023-03472-6
Abstract: Climate change is impacting people’s lives, with management of water resources and food security being major concerns for the future of many countries. In this paper, future water availability, crop water needs, yields, market costs and returns of current crops in a case study area in Australia are evaluated under future climatic conditions. The predictive methods on which the work is based have the advantage of being robust—they are able to simultaneously consider many climate change models—giving greater confidence in determining what the future will hold in this regard. The results indicate business as usual, in terms of the quantity and types of crops that can be grown presently, will not be sustainable in the medium and long term future. Instead, modelling indicates that changes in production and land use to maximise revenue per megalitre of water will be needed to adapt to future conditions and deliver climate-smart agriculture.
Publisher: Springer Berlin Heidelberg
Date: 2001
Publisher: Public Library of Science (PLoS)
Date: 21-10-2019
Publisher: Springer International Publishing
Date: 2019
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11779568_29
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Springer Science and Business Media LLC
Date: 19-09-2007
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: WORLD SCIENTIFIC
Date: 11-2005
Publisher: WORLD SCIENTIFIC
Date: 11-2005
Publisher: IEEE
Date: 1994
Publisher: No publisher found
Date: 2002
Publisher: IEEE
Date: 12-2010
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: IEEE
Date: 12-2010
Publisher: Elsevier BV
Date: 04-2020
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Elsevier BV
Date: 2014
Publisher: Elsevier BV
Date: 2014
Publisher: Elsevier BV
Date: 09-2008
Publisher: IEEE
Date: 12-2010
Publisher: Springer Berlin Heidelberg
Date: 2004
Publisher: IEEE
Date: 07-2018
Publisher: Elsevier BV
Date: 09-2019
Publisher: IEEE
Date: 12-2012
Publisher: IEEE
Date: 12-2006
Publisher: IEEE
Date: 06-2013
Publisher: Springer Science and Business Media LLC
Date: 2001
Publisher: IEEE
Date: 12-2006
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11504894_90
Publisher: Elsevier BV
Date: 03-2013
Publisher: Elsevier BV
Date: 11-2016
Publisher: IEEE
Date: 05-2009
Publisher: MIT Press - Journals
Date: 06-2005
Abstract: Ant colony optimization (ACO) is a constructive metaheuristic that uses an analogue of ant trail pheromones to learn about good features of solutions. Critically, the pheromone representation for a particular problem is usually chosen intuitively rather than by following any systematic process. In some representations, distinct solutions appear multiple times, increasing the effective size of the search space and potentially misleading ants as to the true learned value of those solutions. In this article, we present a novel system for automatically generating appropriate pheromone representations, based on the characteristics of the problem model that ensures unique pheromone representation of solutions. This is the first stage in the development of a generalized ACO system that could be applied to a wide range of problems with little or no modification. However, the system we propose may be used in the development of any problem-specific ACO algorithm.
Publisher: Springer Berlin Heidelberg
Date: 2002
Publisher: Springer US
Date: 2002
Publisher: Springer Berlin Heidelberg
Date: 2003
Publisher: Elsevier
Date: 2021
Publisher: IEEE
Date: 06-2008
Publisher: Springer Berlin Heidelberg
Date: 2004
Publisher: Springer Science and Business Media LLC
Date: 2002
Publisher: Elsevier BV
Date: 03-2021
Publisher: Elsevier BV
Date: 12-2017
DOI: 10.1016/J.JENVMAN.2017.08.044
Abstract: Water as a resource is becoming increasingly more valuable given the changes in global climate. In an agricultural sense, the role of water is vital to ensuring food security. Therefore the management of it has become a subject of increasing attention and the development of effective tools to support participative decision-making in water management will be a valuable contribution. In this paper, evolutionary computation techniques and Pareto optimisation are incorporated in a model-based system for water management. An illustrative test case modelling optimal crop selection across dry, average and wet years based on data from the Murrumbidgee Irrigation Area in Australia is presented. It is shown that sets of trade-off solutions that provide large net revenues, or minimise environmental flow deficits can be produced rapidly, easily and automatically. The system is capable of providing detailed information on optimal solutions to achieve desired outcomes, responding to a variety of factors including climate conditions and economics.
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Elsevier BV
Date: 2015
Publisher: World Scientific Pub Co Pte Lt
Date: 06-2003
DOI: 10.1142/S1469026803000938
Abstract: Ant colony optimization techniques are usually guided by pheromone and heuristic cost information when choosing the next element to add to a solution. However, while an in idual element may be attractive, usually its long term consequences are neither known nor considered. For instance, a short link in a traveling salesman problem may be incorporated into an ant's solution, yet, as a consequence of this link, the rest of the path may be longer than if another link was chosen. The Accumulated Experience Ant Colony uses the previous experiences of the colony to guide in the choice of elements. This is in addition to the normal pheromone and heuristic costs. Two versions of the algorithm are presented, the original and an improved AEAC that makes greater use of accumulated experience. The results indicate that the original algorithm finds improved solutions on problems with less than 100 cities, while the improved algorithm finds better solutions on larger problems.
Publisher: Inderscience Publishers
Date: 2011
Publisher: Public Library of Science (PLoS)
Date: 21-05-2021
DOI: 10.1371/JOURNAL.PONE.0251737
Abstract: During pandemics Agent Based Models (ABMs) can model complex, fine-grained behavioural interactions occurring in social networks, that contribute to disease transmission by novel viruses such as SARS-CoV-2. We present a new agent-based model (ABM) called the Discrete-Event, Simulated Social Agent based Network Transmission model (DESSABNeT) and demonstrate its ability to model the spread of COVID-19 in large cities like Sydney, Melbourne and Gold Coast. Our aim was to validate the model with its disease dynamics and underlying social network. DESSABNeT relies on disease transmission within simulated social networks. It employs an epidemiological SEIRD+M (Susceptible, exposed, infected, recovered, died and managed) structure. One hundred simulations were run for each city, with simulated social restrictions closely modelling real restrictions imposed in each location. The mean predicted daily incidence of COVID-19 cases were compared to real case incidence data for each city. R eff and health service utilisation outputs were compared to the literature, or for the Gold Coast with daily incidence of hospitalisation. DESSABNeT modelled multiple physical distancing restrictions and predicted epidemiological outcomes of Sydney, Melbourne and the Gold Coast, validating this model for future simulation work. DESSABNeT is a valid platform to model the spread of COVID-19 in large cities in Australia and potentially internationally. The platform is suitable to model different combinations of social restrictions, or to model contact tracing, predict, and plan for, the impact on hospital and ICU admissions, and deaths and also the rollout of COVID-19 vaccines and optimal social restrictions during vaccination.
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: IEEE
Date: 2007
Publisher: Hindawi Limited
Date: 29-06-2009
DOI: 10.1002/MMCE.20382
Publisher: IGI Global
Date: 2008
DOI: 10.4018/978-1-59904-498-9.CH007
Abstract: Problems for which many objective functions are to be simultaneously optimised are widely encountered in science and industry. These multi-objective problems have also been the subject of intensive investigation and development recently for metaheuristic search algorithms such as ant colony optimisation, particle swarm optimisation and extremal optimisation. In this chapter, a unifying framework called evolutionary programming dynamics (EPD) is examined. Using underlying concepts of self organised criticality and evolutionary programming, it can be applied to many optimisation algorithms as a controlling metaheuristic, to improve performance and results. We show this to be effective for both continuous and combinatorial problems.
Publisher: Springer International Publishing
Date: 2020
Publisher: Springer Berlin Heidelberg
Date: 2002
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: IEEE
Date: 07-2010
Publisher: IEEE
Date: 2004
Publisher: Springer International Publishing
Date: 2015
Publisher: ACM
Date: 12-07-2011
No related grants have been discovered for Marcus Randall.