Model-Based Decision Support for Industry-Environment Interactions

Applied systems analysis is -- or should be -- a tool in the hands of planners and decision makers who have to deal with the complex and growing problems of modern society. There is, however, an obvious gap between the ever-increasing complexity and volume of scientific and technological information and tools of analysis relevant to large socio-technical and environmental systems, and the information requirements at a strategic planning and policy level. The Advanced Computer Applications (ACA) project builds on IIASA's traditional strength in the methodological foundations of operations research and applied systems analysis, and its rich experience in numerous application areas including the environment, technology, and risk. The ACA group draws on this infrastructure and combines it with elements of AI and advanced information and computer technology. Several completely externally-funded research and development projects in the field of model-based decision support and applied Artificial Intelligence (AI) are currently under way. As an example of this approach to information and decision support systems, one of the components of an R&D project sponsored by the CEC's EURATOM Joint Research Centre (JRC) at Ispra, Italy, in the area of hazardous substances and industrial risk management, is described in this paper. The PDA (Production Distribution Area) is an interactive optimization code (based on DIDASS, one of a family of multicriteria decision support tools developed at IIASA) and a linear problem solver, for chemical industry structures, configured for the pesticide industry of a hypothetical region. The user can select optimization criteria, define allowable ranges or constraints on these criteria, define reference points for the multi-criteria trade-off, and display various levels of model output, including the waste streams generated by the different industrial structure alternatives. These waste streams can then be used to provide input conditions for the environmental impact models. With the emphasis on a directly understandable problem representation and dynamic color graphics, and the user interface as a key element of interactive decision support systems, this is a step toward increased direct practical usability of IIASA's research results

Interactive Environmental Software: Intergration, Simulation and Visualization

The research described in this report results from a series of research and development projects carried out by IIASA's Advanced Computer Applications group. The examples presented range from work done for the Commission of the European Communities' Joint Research Centre and the Dutch Ministry for Housing, Physical Planning and the Environment; the US Bureau of Reclamation and EPA; the State Science and Technology Commission of the People's Republic of China; the Mekong Secretariat in Thailand, to the City of Hanover in Germany. Simulation models are integrated with various data bases and geographical information systems, as well as computer graphics for the visualization of information, problems and solutions, and provide the basis for easy-to-use interactive software tools. The research results presented demonstrate the role and potential of advanced software tools in environmental systems analysis and modeling -- a key area of IIASA's applied research

SMART CITY MANAGEMENT USING MACHINE LEARNING TECHNIQUES

In response to the growing urban population, smart cities are designed to improve people\u27s quality of life by implementing cutting-edge technologies. The concept of a smart city refers to an effort to enhance a city\u27s residents\u27 economic and environmental well-being via implementing a centralized management system. With the use of sensors and actuators, smart cities can collect massive amounts of data, which can improve people\u27s quality of life and design cities\u27 services. Although smart cities contain vast amounts of data, only a percentage is used due to the noise and variety of the data sources. Information and communication technology (ICT) and the Internet of Things (IoT) play a far more prominent role in developing smart cities when it comes to making choices, designing policies, and executing different methods. Smart city applications have made great strides thanks to recent advances in artificial intelligence (AI), especially machine learning (ML) and deep learning (DL). The applications of ML and DL have significantly increased the accuracy aspect of decision-making in smart cities, especially in analyzing the captured data using IoT-based devices and sensors. Smart cities employ algorithms that use unlabeled and labeled data to manage resources and deliver individualized services effectively. It has instantaneous practical use in many crucial areas, including smart health, smart environment, smart transportation system, energy management, and smart water distribution system in a smart city. Hence, ML and DL have become hot research topics in AI techniques in recent years and are proving to be accurate optimization techniques in smart cities. In addition, artificial intelligence algorithms enable the processing massive datasets and identify patterns and characteristics that would otherwise go unnoticed. Despite these advantages, researchers\u27 skepticism of AI\u27s sometimes mysterious inner workings has prevented it from being widely used for smart cities. This thesis\u27s primary intent is to explore the value of employing diverse AI and ML techniques in developing smart city-centric domains and investigate the efficacy of these proposed approaches in four different aspects of the smart city such as smart energy, smart transportation system, smart water distribution system and smart environment. In addition, we use these machine learning approaches to make a data analytics and visualization unit module for the smart city testbed. Internet-of-Things-based machine learning approaches in diverse aspects have repeatedly demonstrated greater accuracy, sensitivity, cost-effectiveness, and productivity, used in the built-in Virginia Commonwealth University\u27s real-time testbed

Technology assessment of advanced automation for space missions

Six general classes of technology requirements derived during the mission definition phase of the study were identified as having maximum importance and urgency, including autonomous world model based information systems, learning and hypothesis formation, natural language and other man-machine communication, space manufacturing, teleoperators and robot systems, and computer science and technology

A Computer-Based Approach to Environmental Impact Assessment

Environmental Impact Assessment (EIA) requires the qualitative and quantitative prediction, assessment and evaluation of the impacts of human activities on the environment in terms of appropriate indicators. Various types of models are major tools for the prediction and analysis of these impacts. They must describe environmental systems in terms of those indicators that environmental law and regulations define and prescribe to evaluate the severity of impacts. In this report, methods and procedures for EIA, the relationship between indicators, standards, and methods, and in particular the use of computer-based tools, models and expert systems, that combine traditional modelling approaches with new techniques of artificial intelligence (AI) and dynamic computer graphics, are demonstrated by a number of application examples in air, surface and groundwater modelling, as well as risk analysis. Drawing on application examples from Europe, the United States, China, India and Thailand, the paper discusses some general features and emerging trends in EIA

Proceedings of Abstracts 12th International Conference on Air Quality Science and Application

© 2020 The Author(s). This an open access work distributed under the terms of the Creative Commons Attribution Licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.Final Published versio

Advances in Modeling and Management of Urban Water Networks

The Special Issue on Advances in Modeling and Management of Urban Water Networks (UWNs) explores four important topics of research in the context of UWNs: asset management, modeling of demand and hydraulics, energy recovery, and pipe burst identification and leakage reduction. In the first topic, the multi-objective optimization of interventions on the network is presented to find trade-off solutions between costs and efficiency. In the second topic, methodologies are presented to simulate and predict demand and to simulate network behavior in emergency scenarios. In the third topic, a methodology is presented for the multi-objective optimization of pump-as-turbine (PAT) installation sites in transmission mains. In the fourth topic, methodologies for pipe burst identification and leakage reduction are presented. As for the urban drainage systems (UDSs), the two explored topics are asset management, with a system upgrade to reduce flooding, and modeling of flow and water quality, with analyses on the transition from surface to pressurized flow, impact of water use reduction on the operation of UDSs, and sediment transport in pressurized pipes. The Special Issue also includes one paper dealing with the hydraulic modeling of an urban river with a complex cross-section

Decision Support Systems

Decision support systems (DSS) have evolved over the past four decades from theoretical concepts into real world computerized applications. DSS architecture contains three key components: knowledge base, computerized model, and user interface. DSS simulate cognitive decision-making functions of humans based on artificial intelligence methodologies (including expert systems, data mining, machine learning, connectionism, logistical reasoning, etc.) in order to perform decision support functions. The applications of DSS cover many domains, ranging from aviation monitoring, transportation safety, clinical diagnosis, weather forecast, business management to internet search strategy. By combining knowledge bases with inference rules, DSS are able to provide suggestions to end users to improve decisions and outcomes. This book is written as a textbook so that it can be used in formal courses examining decision support systems. It may be used by both undergraduate and graduate students from diverse computer-related fields. It will also be of value to established professionals as a text for self-study or for reference