Existing Decision Support System Research Articles

Flood modelling for a real-time decision support system of the covered Lower Paillons River, Nice, France

Abstract Nice Metropolis in Alpes Maritimes, France is prone to flood. The city is crossed by the Lower Paillons River (LPR). Its discharge for a return period of 100 years is estimated at 794 m3/s. Part of the river is covered by 2 km. In addition, there are two retention storages in the river bed and a floodable road tunnel on the left bank. Due to the increase in urban development, flood management is challenging. An existing decision support system, Aquavar, uses DHI Mike tools to reproduce runoff for the Lower Var River in the same region. To extend this system to the LPR and reinforce flood management, a new modelling tool adapted to the characteristics of the LPR is needed. Consequently, this research utilizes the DHI MIKEPLUS tool to develop a 1D -2D coupled model for real-time flood management. The results demonstrate that flood events like those in 2017 and 2019 were correctly reproduced. The linear regression R2 is above 0.8 for all stations. It was also estimated that the covered river should stay clean to avoid widespread flooding in the urban area. Overall, the model is useful for simulating flow in real time and can help sustain urban development.

Supervised learning for maritime search operations: An artificial intelligence approach to search efficiency evaluation

We present a metamodeling approach, based on supervised learning, to estimate the probability of success of maritime search and rescue operations. The objective is to improve search planning in a context where lives are at risk and time is of the essence. The proposed approach has been evaluated both in terms of its predictive performance (Can the probability of success be closely approximated?), and of its added value to the decision support system operationally used by the Canadian Coast Guard (To what extent does the approach improve the current system?). We conducted extensive experimentations to evaluate and compare four machine learning algorithms namely, random forest, k-nearest neighbor, support vector machine regression, and feed forward neural networks with a single layer. Our experimental results, based on real-life data, show that the learned models can approximate the probability of success with sufficient precision and that a heuristic, implementing a k-nearest neighbor model within the existing decision support system, can recommend search plans with higher probabilities of success, which has the potential to save more lives.

Multiple-Fine-Tuned Convolutional Neural Networks for Parkinson’s Disease Diagnosis From Offline Handwriting

Existing decision support system frameworks for diagnosing Parkinson’s disease (PD) through handwriting, speech, or gait characteristics share very similar pipelines. Although in some cases, patient data can be captured by commercially available devices, specialized devices or even custom-made prototypes are often required for such tasks. Captured data are used for extracting features that are carefully designed on the basis of domain and problem knowledge. These features are then fed to classifiers that provide a final decision. In this article, we present an approach in which end-to-end processing by a convolutional neural network (CNN) is utilized to diagnose PD from handwriting images, without the use of additional signals. This eliminates any need for specialized devices or feature engineering. To improve the performance of the proposed pretrained CNN, we propose the idea of multiple fine tuning to bridge the gap between semantically different source and target datasets and facilitate more efficient transfer learning. The proposed architecture, which is based on multiple fine tuning and an ensemble of multiple-fine-tuned CNNs, achieves 94.7% accuracy in the classification of PD from offline handwriting.

КОРЕЛЯЦІЙНО-РЕГРЕСІЙНИЙ АНАЛІЗ КОНКУРЕНТОСПРОМОЖНОСТІ ПРОДУКЦІЇ ПІДПРИЄМСТВА

The economic purpose of correlation-regression analysis is to determine the possible options for product competitiveness management, as well as an assessment of possible ways to achieve the desired result. The developed model can be used to improve planning and increase the level of product competitiveness. The forecast of results, though for the short term, gives the chance to learn about the prospects of obtaining the appropriate level of competitiveness of products in accordance with the degree of application of the impact on it. The forecast is dynamic and adapts to changes based on the latest data. The proposed model can be integrated into the existing decision support system to increase the competitiveness of products. In addition, correlation-regression analysis makes it possible to estimate the current situation using a regression equation. The mathematical reflection of the study of product competitiveness is the economic-mathematical model, which determines its functioning and assessment of changes in its effectiveness in the event of possible changes in the characteristics of economic activity. The parameters of economic models are estimated using the methods of mathematical statistics according to real statistical information. The task of correlation-regression analysis is to construct and analysis of the economic-mathematical model of the regression equation (correlation equation, which reflects the dependence of the resultant feature on several factor features and gives an estimate of the degree of connection density. Using data on the magnitude and direction of action of the analyzed factors, you can get the data that can be obtained to assess the relevant impact on the current level of product competitiveness. That is, such an analysis is a powerful and flexible tool for studying the relationships between product competitiveness indicators. The use of this method makes it possible to better understanding of the level of influence of factors on the competitiveness of products, and, accordingly, learn to manage the processes that take place, as well as more accurately predict their further interaction. These studies are important for the formation and implementation of management decisions to increase the competitiveness of products, because it narrows the choice of indicators with the greatest impact on its level. The ability to determine short-term forecasting of such impacts makes it possible to determine regional perspectives under the conditions of implemented measures.

A Dominance-Based Rough Set Approach for an Enhanced Assessment of Seasonal Influenza Risk.

Accounting for about 290,000-650,000 deaths across the globe, seasonal influenza is estimated by the World Health Organization to be a major cause of mortality. Hence, there is a need for a reliable and robust epidemiological surveillance decision-making system to understand and combat this epidemic disease. In a previous study, the authors proposed a decision support system to fight against seasonal influenza. This system is composed of three subsystems: (i) modeling and simulation, (ii) data warehousing, and (iii) analysis. The analysis subsystem relies on spatial online analytical processing (S-OLAP) technology. Although the S-OLAP technology is useful in analyzing multidimensional spatial data sets, it cannot take into account the inherent multicriteria nature of seasonal influenza risk assessment by itself. Therefore, the objective of this article is to extend the existing decision support system by adding advanced multicriteria analysis capabilities for enhanced seasonal influenza risk assessment and monitoring. Bearing in mind the characteristics of the decision problem considered in this article, a well-known multicriteria classification method, the dominance-based rough set approach (DRSA), was selected to boost the existing decision support system. Combining the S-OLAP technology and the multicriteria classification method DRSA in the same decision support system will largely improve and extend the scope of analysis capabilities. The extended decision support system has been validated by its application to assess seasonal influenza risk in the northwest region of Algeria.

A Holistic Framework for Supporting Maintenance and Asset Management Life Cycle Decisions for Power Systems

The outburst of population as well as increasing industrialisation have triggered a very prominent imbalance between electricity demand and supply in emerging economies such as Indonesia. Based on this premise, electricity generation and distribution firms such as Perusahaan Listrik Negara (PLN) are faced with an urgent need to enhance availability and reliability through capacity expansion as well as the institutionalisation of cost-effective maintenance and asset management (MAM) principles. Some of the principles recommended here involve embedding customised overall health index (OHI) and total life cycle cost (LCC) estimation principles into engineering decisions that relate to asset renewal and/or replacement. While discussions about the fundamental theories and estimation approaches for OHI and LCC for power transformers (PTs) already exist in the current body of literature, however, they are mostly in a generic form which has somewhat limited proper implementation of these valuable principles in practice. This study is unique because it provides a very systematic framework towards achieving cost-effective MAM through a case study. Additionally, the proposed framework is all-encompassing, as it also assesses the impacts of human unreliability through the application or proven risk assessment techniques. The proposed framework commences with the evaluation of existing decision support system at PLN through a MAM audit, whereby the performance of the West Java arm of PLN with regards to critical MAM elements was examined.

Exploring Adoption of the Wildland Fire Decision Support System: End User Perspectives

AbstractAbstractThe increasing complexity of wildland fire management highlights the importance of sound decision making. Numerous fire management decision support systems (FMDSS) are designed to enhance science and technology delivery or assist fire managers with decision-making tasks. However, few scientific efforts have explored the adoption and use of FMDSS by fire managers. This research couples existing decision support system research and in-depth interviews with US Forest Service fire managers to explore perspectives surrounding the Wildland Fire Decision Support System (WFDSS). Results indicate that fire managers appreciate many WFDSS components but view it primarily as a means to document fire management decisions. They describe on-the-ground actions that can be disconnected with decisions developed in WFDSS, which they attribute to the timeliness of WFDSS outputs, the complexity of the WFDSS design, and how it was introduced to managers. We conclude by discussing how FMDSS development could address concerns raised by managers.

Heart Disease Risk Predictor

Cardiovascular disease is one of the focused areas is medical area because its origins sickness and death amongst the population of the entire world. Data mining techniques play an important role to convert the large amount of raw data into meaningful information which will help in prediction and decision of Cardiovascular disease. The prediction models were technologically advanced using diverse amalgamation structures and sorting techniques such as k-NN, Naive Bayes, LR, SVM, Neural Network, Decision Tree. It is very necessary for the recital of the prediction models to choose the exact amalgamation of momentous features. The main Aim of the propose System is to develop an develop an Intelligent System using data mining modeling technique. The proposed system retrieves the data set and compare the data set with the predefined trained data set. The existing decision support system cannot predict the complex question for diagnosing the heart disease but the proposed system predicts the complex queries which will help and assist the healthcare practitioners to take appropriate decisions. This proposed system aims to provide a web platform to predict the occurrences of disease on the basis of various symptoms. The user can select various symptoms and can find the diseases with their probabilistic figures.

Temperature effects on egg and larval development and phenological forecasting of the smaller fruit tortrix,Grapholita lobarzewskii

The effects of temperature on egg and larval development of Grapholita lobarzewskii Nowicki (Lepidoptera: Tortricidae) were studied under controlled conditions to complement the basis for phenological forecasting and thus to optimize the timing of monitoring and control measures with respect to sustainable pest management. Egg development lasted on average 28.1 days at 12.7 °C and 5.5 days at 26.1 °C. Egg mortality was generally low, varying between 12 and 14% within a temperature range of 12.7–22.0 °C, but slightly increased to 20% at 26.1 °C. For egg development, a lower thermal threshold of 9.7 °C and a thermal constant of 90.6 degree days were established. Larval development took on average 76.0 days at 12.7 °C and 21.5 days at 26.1 °C. Larval mortality was 51% at 12.7 °C but only 6–12% at temperatures above 17 °C. The lower thermal threshold and the thermal constant for larval development were 7.6 °C and 389.2 degree days, respectively. Final larval weight increased with temperature from 18.6 mg at 12.7 °C to a maximum of 23.9 mg at 22.0 °C. Based on mortality rates, the optimal temperature range was between 12.7 and 22.0 °C for egg development and between 17.1 and 22.0 °C for larval development, which was confirmed based on the weight of fully grown larvae. These biological parameters of egg and larval development enabled us to parameterise a phenology model for G. lobarzewskii, which was incorporated into an existing decision support system for fruit pests. Precise forecasts of pest phenology facilitate the optimal timing of monitoring and control measures, improve their efficiency, and thereby contribute to sustainable crop protection.

Regional Modeling of Climate Change Impacts on Groundwater Resources Sustainability in Peninsular Malaysia

Projection of climate for the 2020s and 2080s from an ensemble of global climate models (GCMs) run under A2, A1B and B1 emission scenarios are used for regional modeling of climate change impacts on groundwater resources sustainability in Peninsular Malaysia. Few studies that have modeled climate change impacts on groundwater resources used the physically-based surface-subsurface flow model. In this paper, the suite of GCM outputs were modeled for the impact studies via integrative approach involving empirical modeling equation, GIS-based geostatistical technique and model forecasting accuracy optimization. A range of predictions is obtained by modeling the precipitation and temperature change factors derived from fifteen (15) GCMs forced with three (3) future emission scenarios for 50 years periods between 2000 to 2049 (2020s) and 2050 to 2099 (2080s) reference to the baseline period (1950 to 1999). The ensemble average suggests there will be a 1% reduction in monthly recharge in 2020s and 7-10% recharge increment in 2080s across the study area. The spread of predictions for recharge and PET rates across the area ranges from 12.05 to 17.83 mm/day and 3.72 to 4.05, respectively. Geostatistical analysis enabled generation of recharge rate and PET rate prediction maps. The prediction maps were classified into low (L), medium (M) and high (H) recharge and PET rated zones in GIS environment. The recharge rate model map revealed that more than 60% of the area coverage in the study area is characterized by low rating recharge, particularly within 2020s future period. The multiple climate models results provided the highest likelihood mean estimate as well as a measure of its uncertainty and less probable outcomes. Results suggest that recharge reduction in the area is an evidence of water resources scarcity in 2020s. The results of this work provided the basis for the inclusion of representative climate scenarios into the Peninsular Malaysia water resources’ existing decision support system model useful for policy and decision making in the area.

Tools for Assessing the Safety Impact of Interventions on Road Safety

This paper is the result of a PhD in Transport Infrastructure, realized at CTL, aimed to investigate issues related to the evaluation of interventions producing changes in traffic configuration and to develop an information system (Road safety Impact Assessment - RIA Module) to be integrated in an existing Decision Support System on road safety.The research created a library of SPF and developed an information system, called “RIA Module”, updating the existing “DSS - ISIDE”, developed by CTL.

Steps towards an early warning model for flood forecasting in Durazno city in Uruguay

AbstractA hydrologic–hydrodynamic model of the Yi River basins in Uruguay has been developed as a support tool to the Emergency Coordination Centre of Durazno city. The purpose was to improve the existing decision support system and the emergency planning by providing information on gauge height and its permanence in time, and the risk of flooded areas. Four past flood events of high return period were used for calibration and validation with accurate results. The input data to the operational model in real time are hourly observed rainfall and gauge height, as well as rainfall forecasts by several international sources. The use of predictions from numerical weather forecasts allows for the generation of pre‐alert scenarios with larger lead time. These scenarios can warn the emergency coordinators, and thus are of great value to premanage a probable emergency. Operation of a meteorological weather alert issued by the National Meteorological Board is discussed.

Centralized versus Decentralized Wastewater Reclamation in the Houghton Area of Tucson, Arizona

Reclaimed wastewater is increasingly important to satisfaction of water-sustainability objectives in water-short municipalities throughout the United States and particularly in the Southwest. Water reclamation and reuse present new challenges for urban planners, who now tend to consider renewable freshwater and reclaimed wastewater as unique parts of a single water resources portfolio. Efficiency objectives in geographically dispersed communities lead planners to explore the relative merits of centralized versus decentralized wastewater-treatment capacity when new construction is required. However, the complexity of the planning landscape—in which existing water distribution and sewerage capacities; geographic factors; and uncertainty in growth projections, energy cost, and even the sustainability of existing freshwater supplies contribute to plan selection—suggests that decision support methods can usefully supplement engineering judgment to find a near-optimal level of decentralization in facilities planning. In this study, an existing decision support system (DSS) was modified to include costs attributable to infrastructure construction, operation, and maintenance for wastewater collection and transmission of both potable and reclaimed water at the regional (city or city subsection) level to aid water supply planning. The modified DSS was then applied to a study area in southeast Tucson, Arizona. Several scenarios are developed and compared on the basis of cost and energy consumption. A sensitivity analysis is provided. In general, increased peripheral demand, limited existing capacity, greater elevation differences, and lower discount rates favor decentralized design and construction.

Modeling climate change impacts – and uncertainty – on the hydrology of a riparian system: The San Pedro Basin (Arizona/Sonora)

An assessment of climate change impacts in the water resources of a semi-arid basin in southeastern Arizona and northern Sonora is presented using results from an ensemble of 17 global circulation models (GCMs) and four different climate change scenarios from the Intergovernmental Panel on Climate Change (IPCC). Annual GCM precipitation data for the region is spatially downscaled and used to derive spatially distributed recharge estimates in the San Pedro Basin. A three dimensional transient groundwater-surface water flow model is used to simulate the hydrology of the current century, from 2000 to 2100, under different climate scenarios and model estimates. Groundwater extraction in the basin was maintained constant and equal to current. The use of multiple climate model results provides a highest-likelihood mean estimate as well as a measure of its uncertainty and a range of less probable outcomes. Results suggest that recharge in the San Pedro basin will decrease, affecting the dynamics of the riparian area in the long term. It is shown that mean net stream gain, i.e. base flow, will decrease and the effects on the riparian area could be significant. The results of this work provide a basis for the inclusion of representative climate scenarios into the basin’s existing decision support system model.

Improving supply chain integration using a workload control concept and web-functionality

The use of the Internet within supply chain management (SCM) and manufacturing planning and control (MPC) is growing rapidly. To date, this technology has been embraced to the greatest extent by large companies and rather standardised planning tools. In contrast, tools designed for customised industries have received less attention, while small- and medium-sized enterprises (SMEs) lag behind in their adoption of the Internet. Workload control (WLC) is a MPC concept designed for complex production environments and is particularly attractive to SMEs with limited financial resources. This paper explores the implications of enhancing an existing decision support system (DSS), based on a WLC concept, with web-functionality in order to improve both the practical applicability of the tool and the integration of the supply chain. This extension is based on evidence gathered from the literature, a case study company and the requests of a repeat customer of this company. Ten propositions for future research emerge from this process. These propositions cover issues such as: (1) the potential for interference with internal planning processes when web-functionality provides customers with the opportunity to monitor the progress of jobs; (2) the potential for reduced uncertainty during customer enquiry management when more impersonalised contact encourages bid rejection information to be shared; and (3) the potential effect of information sharing on the frequency of customer queries regarding the likely due date adherence of jobs.

A distributed environment for the integration of multiple high-performance decision support systems into clinical workflow

Current studies conclude that clinical decision support systems can help reduce serious medical errors. The importance of Causal Probabilistic Networks (CPNs) for constructing such systems is already well-known. However, the computational complexity of probabilistic inference, which results in unacceptably high response times, can hinder acceptance and integration into clinician workflow. This paper investigates the optimization and parallelization potential of complex CPN-based medical decision support systems and evaluates the results of implementing a parallel, high performance version of an existing decision support system concerning proper antibiotic treatment therapy. Furthermore, it discusses distributed computing techniques for making multiple high performance decision support systems available at the time and location of decision making, by exploiting computing resources residing inside, as well as outside the hospital walls optimally.

On-Line Detection of Multiple Faults in an Experimental Batch Process

This paper discusses detection of multiple faults based on a nonlinear state observer of an experimental batch process. A method for optimal classification of fault signatures based on Learning Vector Quantization (LVQ) is introduced. The results of on-line fault recognition are analysed and the possibility of integrating these methods into an existing decision support system for large chemical plants is investigated.

Reactive management and voltage monitoring and control

This paper reports the implementation and operational experience of a knowledge-based system for voltage/var control in a real-time environment. The developed on-line EMS application is a multi-functional, multi-paradigm tool fully embedded and consistent with the existing decision support system. Especially designed as an assistant to operation, it consists mainly of a rule-based system for reactive power/voltage monitoring and control, and an optimal power flow (OPF) algorithmic function. The application has been in operation for the last two years. The adopted architectural solution is fully compatible with the existing control structure for process synchronization and task scheduling. Finally, the experience acquired during the project provided an in-depth understanding of past, present and future trends which oriented the planning of future development work.

Cooperation in conflict analysis

Definitions and procedures are presented for modeling and analyzing cooperation that can take place in a dispute. In particular, improved definitions on cooperation are given for the situation where two decision makes can cooperate with one another in a conflict consisting of two or more decision makers. To determine the equilibria or compromise resolutions when performing a stability analysis of a game involving cooperation, a sequential stability solution concept is defined. Because the definitions for modeling and analysis include both the cooperative and the noncooperative actions that decision makers can take, the definitions provide a bridge between cooperative and noncooperative game theory. Using a simple application in labor-management negotiations, the way in which the definitions can be easily applied is explained. Future research directions are suggested, including the incorporation of cooperation into an existing decision support system for carrying out conflict analysis studies.

Integration of Algorithmic Aids into Decision Support Systems

This article reports on an application for which a districting algorithm was integrated into an existing decision support system. From this experience a set of guidelines was derived which may be helpful to the system designer who has a similar task. These guidelines aim to improve the interface, feedback, flexibility, and control of the integrated system.