Bio-inspired optimization in integrated river basin management

Water resources worldwide are facing severe challenges in terms of quality and quantity. It is essential to conserve, manage, and optimize water resources and their quality through integrated water resources management (IWRM). IWRM is an interdisciplinary field that works on multiple levels to maximize the socio-economic and ecological benefits of water resources. Since this is directly influenced by the river’s ecological health, the point of interest should start at the basin-level. The main objective of this study is to evaluate the application of bio-inspired optimization techniques in integrated river basin management (IRBM). This study demonstrates the application of versatile, flexible and yet simple metaheuristic bio-inspired algorithms in IRBM. In a novel approach, bio-inspired optimization algorithms Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are used to spatially distribute mitigation measures within a basin to reduce long-term annual mean total nitrogen (TN) concentration at the outlet of the basin. The Upper Fuhse river basin developed in the hydrological model, Hydrological Predictions for the Environment (HYPE), is used as a case study. ACO and PSO are coupled with the HYPE model to distribute a set of measures and compute the resulting TN reduction. The algorithms spatially distribute nine crop and subbasin-level mitigation measures under four categories. Both algorithms can successfully yield a discrete combination of measures to reduce long-term annual mean TN concentration. They achieved an 18.65% reduction, and their performance was on par with each other. This study has established the applicability of these bio-inspired optimization algorithms in successfully distributing the TN mitigation measures within the river basin. Stakeholder involvement is a crucial aspect of IRBM. It ensures that researchers and policymakers are aware of the ground reality through large amounts of information collected from the stakeholder. Including stakeholders in policy planning and decision-making legitimizes the decisions and eases their implementation. Therefore, a socio-hydrological framework is developed and tested in the Larqui river basin, Chile, based on a field survey to explore the conditions under which the farmers would implement or extend the width of vegetative filter strips (VFS) to prevent soil erosion. The framework consists of a behavioral, social model (extended Theory of Planned Behavior, TPB) and an agent-based model (developed in NetLogo) coupled with the results from the vegetative filter model (Vegetative Filter Strip Modeling System, VFSMOD-W). The results showed that the ABM corroborates with the survey results and the farmers are willing to extend the width of VFS as long as their utility stays positive. This framework can be used to develop tailor-made policies for river basins based on the conditions of the river basins and the stakeholders' requirements to motivate them to adopt sustainable practices. It is vital to assess whether the proposed management plans achieve the expected results for the river basin and if the stakeholders will accept and implement them. The assessment via simulation tools ensures effective implementation and realization of the target stipulated by the decision-makers. In this regard, this dissertation introduces the application of bio-inspired optimization techniques in the field of IRBM. The successful discrete combinatorial optimization in terms of the spatial distribution of mitigation measures by ACO and PSO and the novel socio-hydrological framework using ABM prove the forte and diverse applicability of bio-inspired optimization algorithms

The enhanced best performance algorithm for global optimization with applications.

Doctor of Philosophy in Computer Science. University of KwaZulu-Natal, Durban, 2016.Abstract available in PDF file

OPERATIONAL RESEARCH TOOLS IN IRRIGATION - A REVIEW

Operational research optimization is an old method for allocating scarce resources with maximum benefits and efficiency. With increasing global water scarcity, earliness and tiredness in demand base water supply, economical issues, maximizing crop per drop of water, OR is getting popular in irrigation and agriculture sector as well. This paper is intended to review different optimization techniques used so far in the field of irrigation.Key Words: Operation research, optimization, irrigation, water delivery, genetic algorithm, simulated annealing, fuzzy sets, swarm optimization

Portfolio Selection Problem Using Generalized Differential Evolution 3

This Portfolio selection Problem (PSP) remains an intractable research problem in finance and economics and often regarded as NP-hard problem in optimization and computational intelligence. This paper solved the extended Markowitz mean- variance portfolio selection model with an efficient Metaheuristics method of Generalized Differential Evolution 3 (GDE3). The extended Markowitz mean- variance portfolio selection model consists of four constraints: bounds on holdings, cardinality, minimum transaction lots, and expert opinion. There is no research in literature that had ever engaged the set of four constraints with GDE3 to solve PSP. This paper is the first to conduct the study in this direction. The first three sets of constraints have been presented in other researches in literatures. This paper introduced expert opinion constraint to existing portfolio selection models and solved with GDE3. The computational results obtained in this research study show improved performance when compared with other Metaheuristics methods of Genetic algorithm (GA), Simulated Annealing (SA), Tabu Search (TS) and Particle Swarm Optimization (PSO)

Reviewing agent-based modelling of socio-ecosystems: a methodology for the analysis of climate change adaptation and sustainability

The integrated - environmental, economic and social - analysis of climate change calls for a paradigm shift as it is fundamentally a problem of complex, bottom-up and multi-agent human behaviour. There is a growing awareness that global environmental change dynamics and the related socio-economic implications involve a degree of complexity that requires an innovative modelling of combined social and ecological systems. Climate change policy can no longer be addressed separately from a broader context of adaptation and sustainability strategies. A vast body of literature on agent-based modelling (ABM) shows its potential to couple social and environmental models, to incorporate the influence of micro-level decision making in the system dynamics and to study the emergence of collective responses to policies. However, there are few publications which concretely apply this methodology to the study of climate change related issues. The analysis of the state of the art reported in this paper supports the idea that today ABM is an appropriate methodology for the bottom-up exploration of climate policies, especially because it can take into account adaptive behaviour and heterogeneity of the system's components.Review, Agent-Based Modelling, Socio-Ecosystems, Climate Change, Adaptation, Complexity.

Probabilistic and artificial intelligence modelling of drought and agricultural crop yield in Pakistan

Pakistan is a drought-prone, agricultural nation with hydro-meteorological imbalances that increase the scarcity of water resources, thus, constraining water availability and leading major risks to the agricultural productivity sector and food security. Rainfall and drought are imperative matters of consideration, both for hydrological and agricultural applications. The aim of this doctoral thesis is to advance new knowledge in designing hybridized probabilistic and artificial intelligence forecasts models for rainfall, drought and crop yield within the agricultural hubs in Pakistan. The choice of these study regions is a strategic decision, to focus on precision agriculture given the importance of rainfall and drought events on agricultural crops in socioeconomic activities of Pakistan. The outcomes of this PhD contribute to efficient modelling of seasonal rainfall, drought and crop yield to assist farmers and other stakeholders to promote more strategic decisions for better management of climate risk for agriculturalreliant nations

Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management

The main aim of this book is to present various implementations of ML methods and metaheuristic algorithms to improve modelling and prediction hydrological and water resources phenomena having vital importance in water resource management

IoT in smart communities, technologies and applications.

Internet of Things is a system that integrates different devices and technologies, removing the necessity of human intervention. This enables the capacity of having smart (or smarter) cities around the world. By hosting different technologies and allowing interactions between them, the internet of things has spearheaded the development of smart city systems for sustainable living, increased comfort and productivity for citizens. The Internet of Things (IoT) for Smart Cities has many different domains and draws upon various underlying systems for its operation, in this work, we provide a holistic coverage of the Internet of Things in Smart Cities by discussing the fundamental components that make up the IoT Smart City landscape, the technologies that enable these domains to exist, the most prevalent practices and techniques which are used in these domains as well as the challenges that deployment of IoT systems for smart cities encounter and which need to be addressed for ubiquitous use of smart city applications. It also presents a coverage of optimization methods and applications from a smart city perspective enabled by the Internet of Things. Towards this end, a mapping is provided for the most encountered applications of computational optimization within IoT smart cities for five popular optimization methods, ant colony optimization, genetic algorithm, particle swarm optimization, artificial bee colony optimization and differential evolution. For each application identified, the algorithms used, objectives considered, the nature of the formulation and constraints taken in to account have been specified and discussed. Lastly, the data setup used by each covered work is also mentioned and directions for future work have been identified. Within the smart health domain of IoT smart cities, human activity recognition has been a key study topic in the development of cyber physical systems and assisted living applications. In particular, inertial sensor based systems have become increasingly popular because they do not restrict users’ movement and are also relatively simple to implement compared to other approaches. Fall detection is one of the most important tasks in human activity recognition. With an increasingly aging world population and an inclination by the elderly to live alone, the need to incorporate dependable fall detection schemes in smart devices such as phones, watches has gained momentum. Therefore, differentiating between falls and activities of daily living (ADLs) has been the focus of researchers in recent years with very good results. However, one aspect within fall detection that has not been investigated much is direction and severity aware fall detection. Since a fall detection system aims to detect falls in people and notify medical personnel, it could be of added value to health professionals tending to a patient suffering from a fall to know the nature of the accident. In this regard, as a case study for smart health, four different experiments have been conducted for the task of fall detection with direction and severity consideration on two publicly available datasets. These four experiments not only tackle the problem on an increasingly complicated level (the first one considers a fall only scenario and the other two a combined activity of daily living and fall scenario) but also present methodologies which outperform the state of the art techniques as discussed. Lastly, future recommendations have also been provided for researchers

Studies in heuristics for the annual crop planning problem.

M. Sc. University of KwaZulu-Natal, Durban 2012.Increase in the costs associated with agricultural production and the limited availability of resources have amplified the need for optimized solutions to the problem of crop planning. The increased costs have imparted negatively on both the cost of production as well as the sale prices of finished products to consumers, with the resultant effects on the socio-economic livelihoods of people around the world. This has increased the burden of poverty, malnutrition, diseases and other types of social problems. The limited availability of land, irrigated water and other resources in crop planning therefore demand optimal solutions to the problem of crop planning, in order to maintain the desired level of profitable outputs that do not strain available resources while still meeting the demands of consumers. Incidentally, the current situation is such that crop producers are required to generate more output per area of crops cultivated within the ambit of the available resources for crop production. This creates a great challenge both for farmers and researchers. Interesting, the problem is essentially an optimization problem hence a challenge to researchers in mathematical and computing science. Notably within the agricultural sector, achieving efficient use of irrigated water demands that optimized solutions be found for its usage during crop planning and production. Incidentally, increase in population growth and limited availability of fresh water has increased the demand of fresh water supply from all sectors of the economy. This has increased the pressure on the agricultural sector as being one of the primary users of fresh water supply to use irrigated water more efficiently. This is to minimize excessive water wastage. It has therefore become very important that optimized solutions be found to the allocation and use of the irrigated water, for water conservational purposes. This is also a very essential key to crop planning decisions. Therefore, in order to determine good solutions to crop planning decisions, this study dwells on a fairly new but important area of agricultural planning, namely the Annual Crop Planning (ACP) problem which essentially focuses at the level of an irrigation scheme. The study presents a model of the ACP problem that helps to determine solutions to resource allocations amongst the various competing crops that are required to be grown at an irrigation scheme within a year. Both new and existing irrigation schemes are considered. Determining solutions for an ACP problem requires that the requirements and constraints presented by crop characteristics, climatic conditions, market demand conditions and the variable costs associated with agricultural production are observed. The objective is to maximize the total gross profits that can be earned in producing the various crops within a production year. Due to the complexity involved in determining solutions for an ACP problem, exact methods are not researched in this study. Rather, to determine near-optimal solutions for this -Hard optimization problem, this research introduces three new Local Search (LS) metaheuristic algorithms. These algorithms are called the Best Performance Algorithm (BPA), the Iterative Best Performance Algorithm (IBPA) and the Largest Absolute Difference Algorithm (LADA). The motivation for implementing these algorithms is to investigate techniques that can be used to determine effective solutions to difficult optimization problems at low computational costs. This study also investigates the performances of three recently introduced swarm intelligence (SI) metaheuristic algorithms in determining solutions to the ACP problems studies. These algorithms have shown great strength in providing competitive solutions to similar optimization problems in literature, hence their use in this work. To the best of the researchers’ knowledge, this is the first work that reports comparative study of the performances of these particular SI algorithms in determining solutions to a crop planning problem. Interesting results obtained and reported herein show the viability, effectiveness and efficiency of incorporation proven metaheuristic techniques into any decision support system that will help determine solutions to the ACP problem