Metaheuristic Algorithms for Spatial Multi-Objective Decision Making

Spatial decision making is an everyday activity, common to individuals and organizations. However, recently there is an increasing interest in the importance of spatial decision-making systems, as more decision-makers with concerns about sustainability, social, economic, environmental, land use planning, and transportation issues discover the benefits of geographical information. Many spatial decision problems are regarded as optimization problems, which involve a large set of feasible alternatives, multiple conflicting objectives that are difficult and complex to solve. Hence, Multi-Objective Optimization methods (MOO)—metaheuristic algorithms integrated with Geographical Information Systems (GIS) are appealing to be powerful tools in these regards, yet their implementation in spatial context is still challenging. In this thesis, various metaheuristic algorithms are adopted and improved to solve complex spatial problems. Disaster management and urban planning are used as case studies of this thesis.These case studies are explored in the four papers that are part of this thesis. In paper I, four metaheuristic algorithms have been implemented on the same spatial multi-objective problem—evacuation planning, to investigate their performance and potential. The findings show that all tested algorithms were effective in solving the problem, although in general, some had higher performance, while others showed the potential of being flexible to be modified to fit better to the problem. In the same context, paper II identified the effectiveness of the Multi-objective Artificial Bee Colony (MOABC) algorithm when improved to solve the evacuation problem. In paper III, we proposed a multi-objective optimization approach for urban evacuation planning that considered three spatial objectives which were optimized using an improved Multi-Objective Cuckoo Search algorithm (MOCS). Both improved algorithms (MOABC and MOCS) proved to be efficient in solving evacuation planning when compared to their standard version and other algorithms. Moreover, Paper IV proposed an urban land-use allocation model that involved three spatial objectives and proposed an improved Non-dominated Sorting Biogeography-based Optimization algorithm (NSBBO) to solve the problem efficiently and effectively.Overall, the work in this thesis demonstrates that different metaheuristic algorithms have the potential to change the way spatial decision problems are structured and can improve the transparency and facilitate decision-makers to map solutions and interactively modify decision preferences through trade-offs between multiple objectives. Moreover, the obtained results can be used in a systematic way to develop policy recommendations. From the perspective of GIS - Multi-Criteria Decision Making (MCDM) research, the thesis contributes to spatial optimization modelling and extended knowledge on the application of metaheuristic algorithms. The insights from this thesis could also benefit the development and practical implementation of other Artificial Intelligence (AI) techniques to enhance the capabilities of GIS for tackling complex spatial multi-objective decision problems in the future

Spatial energetics:a thermodynamically-consistent methodology for modelling resource acquisition, distribution, and end-use networks in nature and society

Resource acquisition, distribution, and end-use (RADE) networks are ubiquitous in natural and human-engineered systems, connecting spatially-distributed points of supply and demand, to provide energy and material resources required by these systems for growth and maintenance. A clear understanding of the dynamics of these networks is crucial to protect those supported and impacted by them, but past modelling efforts are limited in their explicit consideration of spatial size and topology, which are necessary to the thermodynamically-realistic representation of the energetics of these networks. This thesis attempts to address these limitations by developing a spatially-explicit modelling framework for generalised energetic resource flows, as occurring in ecological and coupled socio-ecological systems. The methodology utilises equations from electrical engineering to operationalise the first and second laws of thermodynamics in flow calculations, and places these within an optimisation algorithm to replicate the selective pressure to maximise resource transfer and consumption and minimise energetic transport costs. The framework is applied to the nectar collection networks of A. mellifera as a proof-of-concept. The promising performance of the methodology in calculating the energetics of these networks in a flow-conserving manner, replicating attributes of foraging networks, and generating network structures consistent with those of known RADE networks, demonstrate the validity of the methodology, and suggests several potential avenues for future refinement and application

Enhancing community resilience in arid regions: A smart framework for flash flood risk assessment

This paper presents a novel framework for smart integrated risk management in arid regions. The framework combines flash flood modelling, statistical methods, artificial intelligence (AI), geographic evaluations, risk analysis, and decision-making modules to enhance community resilience. Flash flood is simulated by using Watershed Modelling System (WMS). Statistical methods are also used to trim outlier data from physical systems and climatic data. Furthermore, three AI methods, including Support Vector Machine (SVM), Artificial Neural Network (ANN), and Nearest Neighbours Classification (NNC), are used to predict and classify flash flood occurrences. Geographic Information System (GIS) is also utilised to assess potential risks in vulnerable regions, together with Failure Mode and Effects Analysis (FMEA) and Hazard and Operability Study (HAZOP) methods. The decision-making module employs the Classic Delphi technique to classify the appropriate solutions for flood risk control. The methodology is demonstrated by its application to the real case study of the Khosf region in Iran, which suffers from both drought and severe floods simultaneously, exacerbated by recent climate changes. The results show high Coefficient of determination (R2) scores for the three AI methods, with SVM at 0.88, ANN at 0.79, and NNC at 0.89. FMEA results indicate that over 50% of scenarios are at high flood risk, while HAZOP indicates 30% of scenarios with the same risk rate. Additionally, peak flows of over 24 m3/s are considered flood occurrences that can cause financial damage in all scenarios and risk techniques of the case study. Finally, our research findings indicate a practical decision support system that is compatible with sustainable development concepts and can enhance community resilience in arid regions

Mapping the knowledge of ecosystem service-based ecological risk assessment: scientometric analysis in CiteSpace, VOSviewer, and SciMAT

The ecosystem services approach offers a more ecologically relevant method to establish environmental conservation goals and implement ecological risk assessment (ERA). The emergence of bibliometrics has facilitated the development of new systematic review techniques. In this study, we utilised CiteSpace, VOSviewer, and SciMAT software, based on the Web of Science database, to qualitatively and quantitatively analyse the ecosystem service-based ecological risk assessment (ESRA) literature knowledge map spanning from 1994 to 2023. This article explored the field’s evolution from macro to micro perspectives, incorporating background information, current trends, and knowledge structure. The findings demonstrate that ESRA has progressed from an initial stage to a phase of global cooperation and policy applications. This transition between stages has been characterised by a shift from focusing on natural processes to understanding human impacts on ecosystems. Key themes identified include ecosystem services, landscape ERA, aquatic ERA and ecosystem health. The overall development of ERA can be observed as a progression through different periods, namely, the traditional era, regional era, and landscape era. Currently, landscape ERA methods based on changes in land use and land cover are widely employed. The study also revealed various challenges in the ESRA field, such as data availability, scale issues, and uncertainty. Future ESRA studies should consider holistic ecosystem services, interdisciplinary approaches, ecological models, and advanced technologies to address complexity. Using big data and informatisation for research offers new opportunities but requires integration and innovation. It is anticipated that ESRA holds promise for ecological sustainability and human wellbeing

Ecological Security Analysis of Land Use Changes in Lavasanat Basin Using Landscape Metrics

Continuous urbanization over the past decades has caused a large concentration of human population in these areas. Due to the rapid growth of the population and the rapid development of urban disorder in Iran, changes in land use and land cover are occurring rapidly and the sustainability of cities is decreasing day by day. Therefore, understanding the effects of urban growth on the ecosystem and determining the relationship between urban dynamics and ecological security are vital for effective urban planning and environmental protection, to support and support sustainable development.The purpose of this study was to monitor and predict land use changes over a 4 year period (2040-2000) with the Markov Chain Model (CA-Markov) in the Lavasanat Basin of Tehran Province and to evaluate the ecological security of this area over time periods. Landsat satellite imagery was used to investigate land use changes. According to the existing land use in the area, five land uses were considered, barren land, pasture land, irrigated land and agricultural and agricultural land. To quantify the landscape patterns in class metrics of NP, LSI, IJI, CA, PLAND and LPI. And NP, LSI, IJI, ED, PD and SPILT metrics were calculated on the landscape surface.Forecasting results for 2040 shows that at each floor level, the number of spots other than the Bayer floor will decrease with the current trend

SPARC 2017 retrospect & prospects : Salford postgraduate annual research conference book of abstracts

Welcome to the Book of Abstracts for the 2017 SPARC conference. This year we not only celebrate the work of our PGRs but also the 50th anniversary of Salford as a University, which makes this year’s conference extra special. Once again we have received a tremendous contribution from our postgraduate research community; with over 130 presenters, the conference truly showcases a vibrant PGR community at Salford. These abstracts provide a taster of the research strengths of their works, and provide delegates with a reference point for networking and initiating critical debate. With such wide-ranging topics being showcased, we encourage you to exploit this great opportunity to engage with researchers working in different subject areas to your own. To meet global challenges, high impact research inevitably requires interdisciplinary collaboration. This is recognised by all major research funders. Therefore engaging with the work of others and forging collaborations across subject areas is an essential skill for the next generation of researchers

Synergy between biology and systems resilience

Resilient systems have the ability to endure and successfully recover from disturbances by identifying problems and mobilizing the available resources to cope with the disturbance. Resiliency lets a system recover from disruptions, variations, and a degradation of expected working conditions. Biological systems are resilient. Immune systems are highly adaptive and scalable, with the ability to cope with multiple data sources, fuse information together, makes decisions, have multiple interacting agents, operate in a distributed manner over a multiple scales, and have a memory structure to facilitate learning. Ecosystems are resilient since they have the capacity to absorb disturbance and are able to tolerate the disturbances. Ants build colonies that are dispersed, modular, fine grained, and standardized in design, yet they manage to forage intelligently for food and also organize collective defenses by the property of resilience. Are there any rules that we can identify to explain the resilience in these systems? The answer is yes. In insect colonies, rules determine the division of labor and how individual insects act towards each other and respond to different environmental possibilities. It is possible to group these rules based on attributes. These attributes are distributability, redundancy, adaptability, flexibility, interoperability, and diversity. It is also possible to incorporate these rules into engineering systems in their design to make them resilient. It is also possible to develop a qualitative model to generate resilience heuristics for engineering system based on a given attribute. The rules seen in nature and those of an engineering system are integrated to incorporate the desired characteristics for system resilience. The qualitative model for systems resilience will be able to generate system resilience heuristics. This model is simple and it can be applied to any system by using attribute based heuristics that are domain dependent. It also provides basic foundation for building computational models for designing resilient system architectures. This model was tested on recent catastrophes like the Mumbai terror attack and hurricane Katrina. With the disturbances surrounding the current world this resilience model based on heuristics will help a system to deal with crisis and still function in the best way possible by depending mainly on internal variables within the system --Abstract, page iii

The nutritional ecology of farmland bees : behavioural and community approach

Nutritional degradation, attributed to agriculture, is a primary driver of bee declines, yet we know very little about bee larval nutrition. How larvae deal with nutritional variation in their provisions remains relatively unexplored. Additionally, the nutritional requirements of adult and larval bees differ, yet such a distinction is rarely considered when investigating bee-flower communities. Using the Geometric Framework I ask (a) whether solitary bee larvae (Osmia bicornis) regulate their nutrient intake, and (b) whether the importance of macronutrients change across development. Second, I investigate (a) how bee and host plants communities change within a flight season on organic and conventional farms, and (b) how bees’ foraging decisions shape their interaction networks. Specifically, using DNA metabarcoding of pollen, I separate larval- and adult-focussed foraging interactions. I show that larval bees prioritise carbohydrate over protein, but that the importance of individual macronutrients shifts from protein to carbohydrate throughout development. I also demonstrate that larvae regulate lipid intake, a macronutrient often overlooked in bee nutrition. I show that organic farms support higher abundances, but not higher diversity, of plants and bees, and that nutritional resources vary more with season than farming practice. Lastly, I show that bees forage differentially for their offspring, highlighting the need to consider both adult and larval nutrition when managing landscapes for bees. These findings highlight the importance of a holistic view of bee nutrition. Larval bees are able to regulate their nutritional intake, suggesting a capacity to deal with nutritional variation. However, this ability is limited, with bees perhaps being vulnerable to undetectable changes to their nutritional environment. Nutritional resources also differ phenologically across farming practices, highlighting the need to address key nutritional gaps for bees. Finally, understanding that bee parental care shapes the way bees interact with their environment is essential to providing quality floral resources that address both adult and larval needs