Integration of Smart Pavement Data with Decision Support Systems: A Systematic Review

A prototype of knowledge-based decision support system for construction cost management

Distributed decision support and organizational connectivity: A case study

Water leakage is acknowledged as one of the most important issues that drinking water supply systems are facing worldwide. Non-Revenue Water is estimated to 346 million m3 per day and its cost/value is estimated to 39 billion USD per year. At the same time drinking water quality is jeopardized from the water intake points to the consumer’s tap, even during normal operating conditions.ICT support water utility operators to improve the operational capacity of their water supply system. A smart green system to control water leakage and monitor drinking water quality in the water supply system of Paramythia city will be built in the context of SMASH project. It consists of: (a) IoT system comprising three local control stations, installed in selected parts of the water supply network, monitoring water quantity&quality parameters in real time; (b) the hydraulic simulation model of the water supply system of Paramythia; (c) a virtual sensors system, which will be used for water quality prediction; (d) a Decision Support System (DSS) for leakage detection and optimal management of water supply system parameters in an automated manner.The DSS will detect and locate water leakages within the DMA zone and inform the operators for excessive values in drinking water quality parameters. The DSS will use as inputs the data from the IoT system, will interact with the hydraulic simulation model, and obtain the water quality data from the virtual sensors. All these data will be processed by the DSS logic in the backend subsystem. The IoT and the hydraulic simulation data, based on the digital twin of the water supply system, are used for the calculation of specific performance indicators related to water leakage, such as well-known IWA indicators: water losses, ILI, etc. Calculating the divergences between the PI values observed & the ones representing the optimal operation of the water network without leakages and setting appropriate thresholds, the DSS will detect the leakage, while several different scenarios will run in hydraulic simulation. The frontend subsystem of the DSS will be able to visualize the water distribution network, statistical values of water quantitative & qualitative parameters. It will provide alarms in case of leakage or exceedance of water quality parameters’ values and it will show the leakage location in a map. The architecture of the smart green system, currently under development, is depicted in Fig.1.Figure 1. The DSS for the water parameters management in the water supply systemKeywords: Drinking water; water quality; leakage; virtual sensors; smart system; decision support.Acknowledgement: This work is co-financed by EEA Grants 2014 – 2021 and Greek Public Investments Program.Liemberger, R., & Wyatt, A. (2019). Quantifying the global non-revenue water problem. Water Supply, 19(3), 831-837. Antzoulatos G., Mourtzios C., et al (2020), Making urban water smart: the SMART-WATER solution. Water Science & Technology, 82(12), 2691–2710. Alegre, H., Baptista, et al (2016). Performance indicators for water supply services. 3rd IWA publishing.

National roads are one of the main networks of a country’s transportation system. To maintain the performance level of national roads requires a well-structured pavement management system (PMS). The decision support system (DSS) is inseparable in the modern PMS, which required the development of a new approach for the DSS in support of national road network maintenance. The proposed model integrates data mining (DM) and geographical information system (GIS) to construct a simple DSS. DM is used to developed road maintenance optimization models, and then integrated with DSS with the help of GIS as an interface application. Historical data on the national road network in West Java, Indonesia is used as a case study. Examples show that the proposed model can determine a decision support solution efficiently. In addition, a userfriendly computer interface is developed so that PMS stakeholders can plan pavement maintenance simply and effectively.

Within the framework of a technical cooperation project between ACSAD and BGR a Decision Support System (DSS) for water resources management was developed and applied in two pilot areas.

Industrial dairy farms boast highly advanced health monitoring and disease diagnosis systems. But without easily accessible, user-friendly web platforms for real-time decision-making, most dairy farmers cannot proactively manage herd health management and optimize treatments based on disease prediction and prevention. To bridge this gap, we have developed a web application of a Decision support system (DSS) for dairy health management based on machine learning. The system architecture combines a Flask backend with a React frontend and scalable cloud data storage and includes preprocessing, data integration, predictive modelling, and cost analysis. DSS forecasts herd diseases with an accuracy 6.66 mean absolute error and 2.35 median absolute deviation across predictions. Its core predictive capabilities rely on long short-term memory (LSTM) neural networks to forecast disease progression from historical records and on a linear trend model to project cuts in treatment costs. The system calculates medication dosages and cost per disease, streamlines supplier selection, and simulates various treatment scenarios, thereby identifying high-cost diseases with potential savings. In other words, this DSS application processes disease and treatment data by incorporating veterinary records into advanced data analytics and neural networks, thereby predicting diseases, optimizing disease prevention and treatment strategies, and reducing costs. As such, this DSS application provides dairy farmers with a tool for strategic decision-making, veterinary treatment planning, and cost-effective disease management towards improving animal welfare and increasing milk yield.

I: General Issues.- Modeling Knowledge: Model-based Decision Support and Soft Computations.- Benefits of Decision Support Using Soft Computing.- Evolving Connectionist-based Decision Support Systems.- An Agent-based Soft Computing Society with Application Applications in Financial Investment Planning.- A Rough Sets/Neoral Networks Approach to Knowledge Discovery for the Development of Decision Support Systems.- II: Applications and Implementations.- Decision Support Systems in Healthcare: Emerging Trends and Success Factors.- A View of Public and Private Sectors for Taiwan's BOT Project Financing Using Fuzzy Multi-Criteria Methods.- Relational Structures for the Analysis of Decision Information in an Electronic Market.- A Fuzzy Evaluation Model: A Case for Intermodal Terminals in Europe.- Application of Kemeny's Median for Group Decision Support.- An Internet-based Group Decision and Consensus Reaching Support System.- Limpio: A DSS for the Urban Waste Collection Problem.- A Decision Support System for Air Quality Control Based on Soft Computing Methods.- Multicriteria Genetic Tuning for the Optimization and Control of HVAC Systems.- Intelligent Information Systems for Crime Analysis.- Application of Fuzzy Decision Trees to Reservoir Recognition.- Introducing SACRA: A Decision Support System for the Construction of Cattle Diets.- Prediction of Parthenium Weed Dispersal Using Fuzzy Logic on GIS Spatial Image.

Towards an integrated weed management decision support system: A simulation model for weed-crop competition and control

This study developed a user-friendly and simplified Spatial Decision Support System using Geographic Information System (GIS) techniques to provide essential information for road managers. This enables the application of appropriate treatments at the right time, based on the available budget. A Global Positioning System (GPS) device was used to collect coordinate data for designated routes within Akure metropolis. Twenty (20) different roads were selected for the study. Data such as road names, coordinates, distances, and infrastructure or facilities unique to each road were collected through reconnaissance surveys, GPS tools, administrative road maps of the city, and street guides clearly showing Akure’s road network. The data were processed using Microsoft Excel, and classifications of the selected roads were made for easy referencing. Database design and creation were carried out using GIS tools within the ArcGIS environment. Digitization of data, labeling of features, symbology, layout design, hyperlinking, and other spatial features were implemented using ArcGIS applications. Each road was linked to its characteristic information, allowing users to access and update information as needed. The result shows that 95% of the total selected roads lack traffic lights and pedestrian bridge facilities. Forty-five percent of the roads have street light facilities. 5% of the road has traffic light facilities. 75% have no bus shelters. Only 5% of the total selected roads have all the identified road facilities. It was concluded that some of the selected roads were not in good condition. While some road facilities were defective, other roads lacked the necessary road facilities that could aid traffic flow. However, the data and associated attributes of each road can be used to support administrative decisions regarding maintenance.

Smart embedded technologies and materials for enhanced pavement management

Integrated pest management (IPM) prioritizes non-chemical alternatives for managing pests and/or diseases, reserving chemicals as last solution, when economically and environmentally justified. The holistic consideration of the agroecosystem makes decision-making in crop protection more complex, as it requires selecting the most appropriate action at the right time. Decision support systems (DSS) for IPM help manage the risks of pests and diseases affecting crops by predicting potential threats and suggesting suitable control measures. However, their adoption remains modest among farmers, despite demonstrated benefits. An anonymous online survey was conducted among farmers, advisors, and other agricultural stakeholders to investigate factors influencing DSS uptake, or lack thereof. The survey distinguished between DSS users and non-users, enabling targeted questions on different aspects of DSS use and adoption. In total, 78 respondents were classified as DSS users and 29 as DSS non-users . Data were analyzed through clustering, followed by statistical validation of clusters and induction of a classification tree, to identify the most influential features shaping cluster formation among both DSS users and DSS non-users . The analysis identified four clusters in the DSS users’ dataset and two clusters in the DSS non-users’ dataset. For DSS users , DSS output reliability and usefulness were identified as the most influential features. Membership in a farmers’ network and DSS ease of use also contributed to cluster separation. For DSS non-users , DSS availability for different cropping systems was the most important attribute determining cluster separation. The results indicate that farmer involvement in DSS development and calibration may enhance trust and, therefore, adoption. Moreover, the need for user-friendly systems providing simple and clear information was highlighted. Although the lack of DSS tailored to specific cropping systems emerged as the main barrier, limited awareness and concerns about costs were also important obstacles. The applied methodological approach presents a reproducible and robust AI-based framework, built on open-source tools, to analyze stakeholders’ perceptions of IPM DSS and extract knowledge from complex survey data. This study provides valuable insights to facilitate DSS adoption for more sustainable crop protection and underscores the importance of both DSS technical performance and end-user needs.

Clinical decision support software (CDSS) has been increasingly implemented to assist improved prescribing practice. Reviews and studies report generally positive results regarding prescribing changes and, to a lesser extent, patient outcomes. Little information is available, however, concerning the use of CDSS in community pharmacy practice. Given the apparent paucity of publications examining this topic, we conducted a review to determine whether CDSS in community pharmacy practice can improve medication use and patient outcomes. A literature search of articles on CDSS relevant to community pharmacy and published between 1 January 2005 and 21 October 2013 was undertaken. Articles were included if the healthcare setting was community pharmacy and the article indicated that pharmacy use of CDSS was part of the study intervention. Eight studies were found which assessed counselling, selected drug interactions, inappropriate prescribing and under-prescribing. One study was halted due to insufficient data collection. Six studies showed statistically significant improvements in the measured outcomes: increased patient counselling, 31% reduced frequency of drug-drug interactions (DDIs), reduced frequency of inappropriate medications in the elderly (2·2-1·8% patients) and in pregnant women (5·5-2·9% patients), and increased pharmacists' interventions for under-prescribed low-dose aspirin (1·74 vs. 0·91 per 100 patients with type 2 diabetes) and over-prescribed high-dose proton-pump inhibitors (PPIs) (1·67 vs. 0·17 interventions per 100 high-dose PPI prescriptions). Most studies showed improved prescribing practice, via direct communication between pharmacists and doctors or indirectly via patient education. Factors limiting the impact of improved prescribing included alert fatigue and clinical inertia. No study investigated patient outcomes and little investigation had been undertaken on how CDSS could be best implemented. Few studies have been undertaken in community pharmacy practice, and based on the positive findings reported, further research should be directed in this area, including investigation of patient outcomes.

This work is concerned with introducing new methods and techniques for the projects management in construction industry. This research suggest that the Iraqi engineering staff, who work in construction sector, have a poor knowledge about the subject of Decision Support System (D.S.S.) in spite of that this subject have a wide uses in construction companies in the world. So the researcher presents this study to introduce and to increase the knowledge about the concept of Decision Support System. This research consist of the questionnaire process for construction companies, then the research use the questionnaire results in building a proposed Decision Support System, also the questionnaire process indicate the relation between the decision structure and the organizational levels. The results obtained from the questionnaire process shows that there is a requirement for Decision Support System in cost control decisions support. So the researcher build a Decision Support System for cost control process which can be used by the planner and estimator for different types of projects. The researcher also applies and evaluates the proposed system in some Iraqi construction companies. The application and evaluation process recommended the needs for applying the Decision Support System for project management in construction companies.

This paper aims to introduce the Decision Support System (DSS), which is an area informatics approach to area studies, and the research network complex based on DSS's application in Thailand. In particular, the paper presents and discusses current activities of the DSS research network at the national level in Thailand rather than being a theoretical and analytical study of the DSS mechanism or its cases. DSS has wide applications in Thailand, extending to various fields such as agricultural and natural resource management, economic and social management, historical and cultural preservation, and so on. DSS generally refers to a support system embedded into a computer system for providing intellectual resources with the appropriate numerical model necessary for decision making in production activities. Agricultural and natural resources have been the foundation of social and economic development in Thailand since the country's first national plan in the1960s. Decision making to maintain agricultural productivity as well as to protect natural resources requires well-integrated data sets. The Thailand Research Fund(TRF) established a DSS research and development network (TRF-DSS) in 2002 to help various research teams in Thailand develop DSS tools and components for addressing agricultural and natural resource management issues. Using a "systems"approach, the DSS framework allows researchers and users to identify and integrate key components as well as to define databases and model-base management systems. This paper focuses on the TRF-DSS research and development network, which consists of 12 universities, two line agencies in Thailand, and a line agency in Cambodia. During 2002–10, a total of 59 research projects were granted a budget allocation of 140.1 million baht, and a budget of 15.4 million baht was allocated to support activities of the network. In addition, 10 projects were funded to carry out post-project activities, with a total budget of 1.5 million baht. More than 20 DSS tools were developed and implemented by various users, ranging from policy makers to provincial and local government agencies engaged in short- and long-term planning and management. Most DSS tools were designed to allow the integration of biophysical and socioeconomic data as well as the decision support modules for alternatives evaluation and analysis. These approaches support the choices of dynamic simulation models as well as multi-criteria analyses for modelbase software development. They also allow users to evaluate various alternatives in agricultural and natural resource management. Networking is a powerful platform for DSS research and development and maybe applied to other types of research and development agenda in Thailand, such as area study projects. DSS tools contribute to an understanding of sustainability of production systems against a background of climate change, poverty reduction, and food security.

Design of a computerized decision support system for hatchery production management