Application of Wavelet Decomposition and Phase Space Reconstruction in Urban Water Consumption Forecasting: Chaotic Approach (Case Study)

The forecasting of future value of water consumption in an urban area is highly complex and nonlinear. It often exhibits a high degree of spatial and temporal variability. It is a crucial factor for long-term sustainable management and improvement of the operation of urban water allocation system. This chapter will study the application of two pre-processing phase space reconstruction (PSR) and wavelet decomposition transform (WDT) methods to investigate the behavior of time series to forecast short-term water demand value of Kelowna City (BC, Canada). The research proposes two pre-process technique to improve the accuracy of the models. Artificial neural networks (ANNs), gene expression programming (GEP) and multilinear regression (MLR) methods are the tools that considered for forecasting the demand values. Evaluation of the tools is based on two steps with and without applying the pre-processing methods. Moreover, autocorrelation function (ACF) is used to calculate the lag time. Correlation dimension is used to study the chaotic behavior of the dataset. The models’ relative performance is compared using three different fitness indexes; coefficient of determination (CD), root mean square error (RMSE) and mean absolute error (MAE). The results showed how pre-processing combination of WDT and PSR improved the performance of the models in forecasting short-term demand values

Situation Modeling of Regional Development in the Republic of Kazakhstan

The methodology of situation modeling was based on the application of fuzzy cognitive maps, indistinct regional data and indistinct time horizon. Based on indistinct number of involved concepts, the model enables users to create their own situations with fuzzy quantity of available concepts including both the existing and the added ones. The added concepts are characterized by the set properties and database related to no less than three fuzzy time horizons. The number of set impulses is fuzzy as well. Cognitive map training was based on the artificial intelligence element – the active Hebb learning rule. The impact of concepts was defined in the course of training. Fine adjustment of the fuzzy cognitive map was achieved by changing the training order using a rank scale and Saati’s sorting algorithm. The developed computer software was used in simulation modeling of regional socio-economic processes related to the project aiming at tourism development of the Alacol Lake in Almaty region. Research results are shown in the form of a fuzzy cognitive map reflecting internal and external relations within the region, graphs reflecting socio-economic development and the Bossel criterion. Simulation of allocations had a positive effect: GRP (Gross Regional Product) growth along with increase in employment and environmental improvement. The proposed approach provides a tool for forecasting of regional development and solution of different regional problems. This approach can be used with regard to any administrative-territorial entity, provided relevant statistical data

Situation Modeling of Regional Development in the Republic of Kazakhstan

The methodology of situation modeling was based on the application of fuzzy cognitive maps, indistinct regional data and indistinct time horizon. Based on indistinct number of involved concepts, the model enables users to create their own situations with fuzzy quantity of available concepts including both the existing and the added ones. The added concepts are characterized by the set properties and database related to no less than three fuzzy time horizons. The number of set impulses is fuzzy as well. Cognitive map training was based on the artificial intelligence element – the active Hebb learning rule. The impact of concepts was defined in the course of training. Fine adjustment of the fuzzy cognitive map was achieved by changing the training order using a rank scale and Saati’s sorting algorithm. The developed computer software was used in simulation modeling of regional socio-economic processes related to the project aiming at tourism development of the Alacol Lake in Almaty region. Research results are shown in the form of a fuzzy cognitive map reflecting internal and external relations within the region, graphs reflecting socio-economic development and the Bossel criterion. Simulation of allocations had a positive effect: GRP (Gross Regional Product) growth along with increase in employment and environmental improvement. The proposed approach provides a tool for forecasting of regional development and solution of different regional problems. This approach can be used with regard to any administrative-territorial entity, provided relevant statistical data

FORECASTING CLIMATE AND LAND USE CHANGE IMPACTS ON ECOSYSTEM SERVICES IN HAWAIʻI THROUGH INTEGRATION OF HYDROLOGICAL AND PARTICIPATORY MODELS

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2018

Interactive Problem Structuring with ICZM Stakeholders

Integrated Coastal Zone Management (ICZM) is struggling with a lack of science-management integration. Many computer systems, usually known as “decision support systems”, have been developed with the intention to make scientific knowledge about complex systems more accessible for coastal managers. These tools, allowing a multi-disciplinary approach with multi-criteria analyses, are designed for well-defined, structured problems. However, in practice stakeholder consensus on the problem structure is usually lacking. Aim of this paper is to explore the practical opportunities for the new so-called Quasta approach to structure complex problems in a group setting. This approach is based on a combination of Cognitive Mapping and Qualitative Probabilistic Networks. It comprehends a new type of computer system which is quite simple and flexible as well. The tool is tested in two workshops in which various coastal management issues were discussed. Evaluations of these workshops show that (1) this system helps stakeholders to make them aware of causal relationships, (2) it is useful for a qualitative exploration of scenarios, (3) it identifies the quantitative knowledge gaps of the problem being discussed and (4) the threshold for non technicians to use this tool is quite low.Integrated Coastal Zone Management, Problem Structuring, Stakeholder Participation, Cognitive Mapping, Interactive Policy Making

The potential impact of Brexit on the energy, water and food nexus in the UK: A fuzzy cognitive mapping approach

© 2017 The Authors. Energy is one of the cornerstones essential for human life, along with other services such as water and food. Understanding how the different services in the energy-water-food (EWF) nexus interact and are perceived by different actors is key to achieving sustainability. In this paper, we derive a model of the EWF nexus using fuzzy cognitive mapping (FCM). Data were collected in a two-step approach from workshops with researchers and stakeholders involved in the three focal sectors. Four FCMs were developed; one for each of the EWF sectors, and one for the interactions that create the nexus between EWF. The FCM represents the combined views of the groups who participated in the workshops, the importance and limitations of which is discussed. To demonstrate its effectiveness, the aggregated FCM was applied to predict the impacts on the EWF nexus of four scenarios under which the United Kingdom would depart from the European Union (i.e. Brexit). The FCM indicated that energy-related concepts had the largest influence on the EWF nexus and that EWF demand will decrease most under a 'hard-Brexit' scenario. The demand for energy was shown to decline relatively less than other services and was strongly associated with gross domestic product (GDP), whereas UK population size had a stronger effect on water and food demand. Overall, we found a threefold change across all concepts in scenarios without freedom of movement, contribution to the EU budget, and increased policy devolution to the UK

Кибербезопасность в образовательных сетях

The paper discusses the possible impact of digital space on a human, as well as human-related directions in cyber-security analysis in the education: levels of cyber-security, social engineering role in cyber-security of education, “cognitive vaccination”. “A Human” is considered in general meaning, mainly as a learner. The analysis is provided on the basis of experience of hybrid war in Ukraine that have demonstrated the change of the target of military operations from military personnel and critical infrastructure to a human in general. Young people are the vulnerable group that can be the main goal of cognitive operations in long-term perspective, and they are the weakest link of the System.У статті обговорюється можливий вплив цифрового простору на людину, а також пов'язані з людиною напрямки кібербезпеки в освіті: рівні кібербезпеки, роль соціального інжинірингу в кібербезпеці освіти, «когнітивна вакцинація». «Людина» розглядається в загальному значенні, головним чином як та, що навчається. Аналіз надається на основі досвіду гібридної війни в Україні, яка продемонструвала зміну цілей військових операцій з військовослужбовців та критичної інфраструктури на людину загалом. Молодь - це вразлива група, яка може бути основною метою таких операцій в довгостроковій перспективі, і вони є найслабшою ланкою системи.В документе обсуждается возможное влияние цифрового пространства на человека, а также связанные с ним направления в анализе кибербезопасности в образовании: уровни кибербезопасности, роль социальной инженерии в кибербезопасности образования, «когнитивная вакцинация». «Человек» рассматривается в общем смысле, в основном как ученик. Анализ представлен на основе опыта гибридной войны в Украине, которая продемонстрировала изменение цели военных действий с военного персонала и критической инфраструктуры на человека в целом. Молодые люди являются уязвимой группой, которая может быть главной целью когнитивных операций в долгосрочной перспективе, и они являются самым слабым звеном Систем

A review of the use of artificial intelligence methods in infrastructure systems

The artificial intelligence (AI) revolution offers significant opportunities to capitalise on the growth of digitalisation and has the potential to enable the ‘system of systems’ approach required in increasingly complex infrastructure systems. This paper reviews the extent to which research in economic infrastructure sectors has engaged with fields of AI, to investigate the specific AI methods chosen and the purposes to which they have been applied both within and across sectors. Machine learning is found to dominate the research in this field, with methods such as artificial neural networks, support vector machines, and random forests among the most popular. The automated reasoning technique of fuzzy logic has also seen widespread use, due to its ability to incorporate uncertainties in input variables. Across the infrastructure sectors of energy, water and wastewater, transport, and telecommunications, the main purposes to which AI has been applied are network provision, forecasting, routing, maintenance and security, and network quality management. The data-driven nature of AI offers significant flexibility, and work has been conducted across a range of network sizes and at different temporal and geographic scales. However, there remains a lack of integration of planning and policy concerns, such as stakeholder engagement and quantitative feasibility assessment, and the majority of research focuses on a specific type of infrastructure, with an absence of work beyond individual economic sectors. To enable solutions to be implemented into real-world infrastructure systems, research will need to move away from a siloed perspective and adopt a more interdisciplinary perspective that considers the increasing interconnectedness of these systems

Using fuzzy cognitive maps for predicting river management responses: A case study of the Esla River basin, Spain

The planning and management of river ecosystems affects a variety of social groups (i.e., managers, stakeholders, professionals and users) who have different interests about water uses. To avoid conflicts and reach an environmentally sustainable management, various methods have been devised to enable the participation of these actors. Mathematical modelling of river systems is highly recommended to forecast, but we do not always have enough information to do it. In these cases, the soft and meta-models can be valid alternatives to simulate these complex systems. The Fuzzy Cognitive Maps (FCMs) are presented as a tool that facilitates the modelling of ecological systems, functions and services. FCM networking concepts are intertwined through causal relationships. The FCM concept spatial arrangement and the use of fuzzy logic facilitate the integration of different expert opinions. In our study, from a panel of seven experts from representatives of different social sectors, an aggregated FCM was obtained. The most central concept in the aggregated map was cross barriers, dams and weirs. Using our FCM expert model, we performed a number of simulations from different possible scenarios, such as the continuous degradation of natural conditions and the improvement of river natural conditions. A regular increment in the natural conditions generates a substantial enhance in variables as natural water flow and sediment transport. Conversely, the increment in human activities as agro-forestry production addresses to a deterioration of river banks among other variables. In the Esla River, the FCM indicators showed an ecosystem that was greatly influenced by human activity, especially by the presence of barriers, in which the economic variables presented high network influence even though their centrality indices were relatively low. Meanwhile, the essential elements for the proper functioning of this ecosystem, as a natural flow regime, showed very low values that were visibly affected by anthropogenic variables. FCM methodology enabled us not only to understand the perception of current fluvial ecosystems but also to generate plausible management scenarios based on expert knowledge in this field