Study of Potential Integrated Management of Water Resources in Las Vegas Valley

Water resource management under short term system perturbations such as storms and longer-term systemic changes caused by climate change such as droughts is a challenge when multiple agencies are involved. To address this challenge this research focuses on water management under changing climate conditions and population growth through understanding the agency water jurisdictions, management strategies, and modes of operation in Las Vegas Valley. A framework for integrated management through sharing data and models is presented that combines drinking water supply, flood control, and waste water treatment. This framework can be adopted to improve coordination among different water management agencies

Modeling water resources management at the basin level: review and future directions

Water quality / Water resources development / Agricultural production / River basin development / Mathematical models / Simulation models / Water allocation / Policy / Economic aspects / Hydrology / Reservoir operation / Groundwater management / Drainage / Conjunctive use / Surface water / GIS / Decision support systems / Optimization methods / Water supply

Development of Integrated Water Resources Planning Model for Dublin using WEAP21

Population growth, urbanisation, and climate change are predicted to impose huge pressure on water resource systems of many cities around the world including Dublin. Integrated water resources management is seen as a viable approach to address these challenges. This approach examines the water resources system in a more interconnected manner, focusing on reducing water demands, reducing reliance on fresh water supplies, reducing discharges into receiving water bodies, and creating water supply assets from storm water and wastewater. The role of mathematical modelling in designing an integrated water resources management plan is paramount as it provides a tool whereby performances of alternative water management plans can be predicted and evaluated under future scenarios of population growth, urban development and climate. There is a lack of an integrated water resources management model for Dublin that integrates the main components of the water resources system including water supply sources, sectoral water uses, wastewater disposal, urban runoff and associated infrastructure. Previous models also did not consider water management options such as rainwater harvesting, greywater reuse, and groundwater recharge - which are important for the implementation of an integrated water resources management approach. Moreover, integration of uncertainty analysis into water resources modelling helps understand associated uncertainties and hence reduce the

Stakeholder engagement does not guarantee impact: A co-productionist perspective on model-based drought research

Stakeholder engagement has become a watchword for environmental scientists to assert the societal relevance of their projects to funding agencies. In water research based on computer simulation modelling, stakeholder engagement has attracted interest as a means to overcome low uptake of new tools for water management. An increasingly accepted view is that more and better stakeholder involvement in research projects will lead to increased adoption of the modelling tools created by scientists in water management. However, we cast doubt on this view by drawing attention to how the freedom of stakeholder organizations to adopt new scientific modelling tools in their regular practices is circumscribed by the societal context. We use a modified concept of co-production in an analysis of a case of scientific research on drought in the UK to show how relationships between actors in the drought governance space influence the uptake of scientific modelling tools. The analysis suggests an explanation of why stakeholder engagement with one scientific project led to one output (data) getting adopted by stakeholders while another output (modelling tools) attracted no discernible interest. Our main objective is to improve the understanding of the limitations to stakeholder engagement as a means of increasing societal uptake of scientific research outputs

System Dynamics Modeling for Supporting Drought-Oriented Management of the Jucar River System, Spain

[EN] The management of water in systems where the balance between resources and demands is already precarious can pose a challenge and it can be easily disrupted by drought episodes. Anticipated drought management has proved to be one of the main strategies to reduce their impact. Drought economic, environmental, and social impacts affect different sectors that are often interconnected. There is a need for water management models able to acknowledge the complex interactions between multiple sectors, activities, and variables to study the response of water resource systems to drought management strategies. System dynamics (SD) is a modeling methodology that facilitates the analysis of interactions and feedbacks within and between sectors. Although SD has been applied for water resource management, there is a lack of SD models able to regulate complex water resource systems on a monthly time scale and considering multiple reservoir operating rules, demands, and policies. In this paper, we present an SD model for the strategic planning of drought management in the Jucar River system, incorporating dynamic reservoir operating rules, policies, and drought management strategies triggered by a system state index. The DSS combines features from early warning and information systems, allowing for the simulation of drought strategies, evaluating their economic impact, and exploring new management options in the same environment. The results for the historical period show that drought early management can be beneficial for the performance of the system, monitoring the current state of the system, and activating drought management measures results in a substantial reduction of the economic impact of droughts.The data used in this study was obtained from the references included. We acknowledge the European Research Area for Climate Services consortium (ER4CS) and the Agencia Estatal de Investigacion for their financial support to this research under the INNOVA project (Grant Agreement: 690462; PCIN-2017-066). This study has also been partially funded by the ADAPTAMED project (RTI2018-101483-B-I00) from the Ministerio de Ciencia, Innovacion y Universidades (MICIU) of Spain.Rubio-Martin, A.; Pulido-Velazquez, M.; Macian-Sorribes, H.; Garcia-Prats, A. (2020). System Dynamics Modeling for Supporting Drought-Oriented Management of the Jucar River System, Spain. Water. 12(5):1-19. https://doi.org/10.3390/w12051407S119125Mishra, A. K., & Singh, V. P. (2010). A review of drought concepts. Journal of Hydrology, 391(1-2), 202-216. doi:10.1016/j.jhydrol.2010.07.012Momblanch, A., Paredes-Arquiola, J., Munné, A., Manzano, A., Arnau, J., & Andreu, J. (2015). Managing water quality under drought conditions in the Llobregat River Basin. 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Agricultural Water Management, 155, 113-124. doi:10.1016/j.agwat.2015.03.014Lewandowski, J., Meinikmann, K., & Krause, S. (2020). Groundwater–Surface Water Interactions: Recent Advances and Interdisciplinary Challenges. Water, 12(1), 296. doi:10.3390/w12010296Forrester, J. W. (1968). Industrial Dynamics—After the First Decade. Management Science, 14(7), 398-415. doi:10.1287/mnsc.14.7.398Sušnik, J., Molina, J.-L., Vamvakeridou-Lyroudia, L. S., Savić, D. A., & Kapelan, Z. (2012). Comparative Analysis of System Dynamics and Object-Oriented Bayesian Networks Modelling for Water Systems Management. Water Resources Management, 27(3), 819-841. doi:10.1007/s11269-012-0217-8Mirchi, A., Madani, K., Watkins, D., & Ahmad, S. (2012). Synthesis of System Dynamics Tools for Holistic Conceptualization of Water Resources Problems. Water Resources Management, 26(9), 2421-2442. doi:10.1007/s11269-012-0024-2Simonovic, S. (2002). World water dynamics: global modeling of water resources. 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A Multi-Agent System framework to support the decision-making in complex real-world domains

The aim of this work was to develop a framework capable of supporting the decision-making process in complex real-world domains, such as environmental, industrial or medical domains using a Multi-Agent approach with Rule-based Reasoning. The validation of the framework was done in the environmental domain, particularly in the area of river basins

Colorado water, January/February 2011

The newsletter is devoted to enhancing communication between Colorado water users and managers and faculty at the state's research universities.Newsletter of the Water Center of Colorado State University. Theme: Decision support systems

An Integrated Assessment Framework for Water Resources Management: A DSS Tool and a Pilot Study Application

Decision making for the management of water resources is a complex and difficult task. This is due to the complex socio-economic system that involves a large number of interest groups pursuing multiple and conflicting objectives, within an often intricate legislative framework. Several Decision Support Systems have been developed but very few have indeed proved to be effective and truly operational. MULINO (Multisectoral, Integrated and Operational Decision Support System for Sustainable Use of Water Resources at the Catchment Scale) is a project funded under the Fifth Framework Programme of the European Research and the key action line dedicated to operational management schemes and decision support system for sustainable use of water resources. The MULINO DSS (mDSS) integrates hydrological models with multi-criteria decision methods and adopts the DPSIR (Driving Force – Pressure – State – Impact – Response) framework developed by the European Environment Agency. The DPSIR was converted from a static reporting scheme into a dynamic framework for integrated assessment modelling (IAM) and multi-criteria evaluation procedures. This paper presents the methodological framework and the intermediate results of the mDSS tool through its application in a pilot study area located in the Watershed of the Lagoon of Venice.Integrated water resources management, Spatial decision-making, Decision support system, Catchment, Environmental modelling

An IoT architecture for decision support system in precision livestock

Sustainable animal production is a primary goal of technological development in the livestock industry. However, it is crucial to master the livestock environment due to the susceptibility of animals to variables such as temperature and humidity, which can cause illness, production losses, and discomfort. Thus, livestock production systems require monitoring, reasoning, and mitigating unwanted conditions with automated actions. The principal contribution of this study is the introduction of a self-adaptive architecture named e-Livestock to handle animal production decisions. Two case studies were conducted involving a system derived from the e-Livestock architecture, encompassing a Compost Barn production system - an environment and technology where bovine milk production occurs. The outcomes demonstrate the effectiveness of e-Livestock in three key aspects: (i) abstraction of disruptive technologies based on the Internet of Things (IoT) and Artificial Intelligence and their incorporation into a single architecture specific to the livestock domain, (ii) support for the reuse and derivation of an adaptive self-architecture to support the engineering of a decision support system for the livestock subdomain, and (iii) support for empirical studies in a real smart farm to facilitate future technology transfer to the industry. Therefore, our research’s main contribution is developing an architecture combining machine learning techniques and ontology to support more complex decisions when considering a large volume of data generated on farms. The results revealed that the e-Livestock architecture could support monitoring, reasoning, forecasting, and automated actions in a milk production/Compost Barn environment.Na indústria pecuária, a produção animal sustentável é o principal objetivo do desenvolvimento tecnológico. Porém, é fundamental manter boas condições no ambiente devido à suscetibilidade dos animais a variáveis como temperatura e umidade, que podem causar doenças, perdas de produção e desconforto. Assim, os sistemas de produção pecuária requerem monitoramento, controle e mitigação das condições indesejadas através de ações automatizadas. A principal contribuição deste estudo é a introdução de uma arquitetura auto-adaptativa denominada e-Livestock para apoiar as decisões relacionadas à produção animal. Foram conduzidos dois estudos de caso, envolvendo a arquitetura e-Livestock, que foi utilizada no sistema de produção Compost Barn - ambiente e tecnologia onde ocorre a produção de gado leiteiro. Os resultados demonstraram a utilidade do e-Livestock para avaliar três aspectos principais: (i) abstração de tecnologias disruptivas baseadas em Internet das Coisas (IoT) e Inteligência Artificial, e sua incorporação em uma arquitetura única, específica para o domínio da pecuária, (ii) suporte para a reutilização e derivação de uma arquitetura auto-adaptativa para apoiar o desenvolvimento de uma aplicação de apoio à decisão para o subdomínio da pecuária e (iii) suporte para estudos empíricos em uma fazenda inteligente real para facilitar a transferência de tecnologia para a indústria. Portanto, a principal contribuição dessa pesquisa é o desenvolvimento de uma arquitetura combinando técnicas de machine learning e ontologia para apoiar decisões mais complexas ao considerar um grande volume de dados gerados nas fazendas. Os resultados revelaram que a arquitetura e-Livestock pode apoiar monitoramento, controle, previsão e ações automatizadas em um ambiente de produção de leite/Compost Barn.CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superio

Modern approaches to sepsis - evolving definitions, clinician roles, and AI-based diagnostic aids

Sepsis is an ongoing concern in critical care. It is hard to quickly detect, and rapid deterioration of a patient into septic shock causes death in around 30% - 50% of patients, while survivors may live with organ damage and shorter lifespans. Traditional methods of detection require long laboratory tests and clinician vigilance, which put a strain on hospital resources. New advances in machine learning offer an alternative – using algorithmic analysis in real-time to watch for a deteriorating patient state. The use of readily available data – heart rate, respiratory rate – combined with electronic medical records and fast laboratory tests presents an opportunity for early detection of sepsis, which can potentially make great strides in minimizing damage to patients. A variety of algorithmic methods have been proposed by researchers, and research so far has been promising. Algorithms inretrospective studies have performed equal or better to standard protocols such as SIRS or SOFA. Some promising research even presents the opportunity to approach sepsis diagnosis and treatment in an entirely new manner. At the present stage, however, the field is at too early a stage for use in a clinical environment. This review intends to review some prominent types of machine learning algorithms, as well as discuss current concerns regarding machine learning-based detection support systems (ML-DSS)