Comprehensive Decision Support System Research Articles

Development of Advanced Advisory System for Anomalies (AAA) to Predict and Detect the Abnormal Operation in Fired Heaters for Real Time Process Safety and Optimization

As the technology is emerging, the process industries are actively migrating to Industry 4.0 to optimize energy, production, profit, and the quality of products. It should be noted that real-time process monitoring is the area where most of the energies are being placed for the sake of optimization and safety. Big data and knowledge-based platforms are receiving much attention to provide a comprehensive decision support system. In this study, the Advanced Advisory system for Anomalies (AAA) is developed to predict and detect the abnormal operation in fired heaters for real-time process safety and optimization in a petrochemical plant. This system predicts and raises an alarm for future problems and detects and diagnoses abnormal conditions using root cause analysis (RCA), using the combination of FMEA (failure mode and effects analysis) and FTA (fault tree analysis) techniques. The developed AAA system has been integrated with databases in a petrochemical plant, and the results have been validated well by testing the application over an extensive period. This AAA online system provides a flexible architecture, and it can also be integrated into other systems or databases available at different levels in a plant. This automated AAA platform continuously monitors the operation, checks the dynamic conditions configured in it, and raises an alarm if the statistics exceed their control thresholds. Moreover, the effect of heaters’ abnormal conditions on efficiency and other KPIs (key performance indicators) is studied to explore the scope of improvement in heaters’ operation.

Improving Horse Welfare and Environmental Sustainability in Horse Husbandry: Linkage between Turnout and Nitrogen Surplus

A scientific consultation tool is currently being developed in Germany to assess, analyze, and improve animal welfare on site and simultaneously consider aspects of environmental sustainability in horse husbandry in order to contribute to a resource-conserving and responsible handling of natural resources and the welfare of living beings. To date, no study has assessed to what extent species-appropriate free-range locomotion possibilities are implemented on horse farms and to what extent turnout areas are affected by unwanted nutrient inputs from horse excretions. Using the indicators “species-appropriate turnout” (hereafter: “turnout”) and “unwanted nitrogen inputs in water bodies” (based on “nitrogen surplus”), we exemplarily examine animal- and environmentally relevant aspects of horse husbandry. We conducted 88 assessments on 46 horse farms (n = 2220 horses) to test literature-based indicators of animal welfare and environmental sustainability. We found that the indicator “nitrogen surplus,” used to assess an aspect of environmental sustainability, was mostly a problem in the wintertime (summertime = −4.24, range: −109.27–58.97; wintertime: mean: 12.01, range: −35.19–468.00 nitrogen surplus per hectare, n = 44 farms, p < 0.001), when the horses had a reduced space allowance for free locomotion. On most farms, “turnout” was provided daily for several hours, but in many of the single housing systems, not all horses had the possibility for free locomotion (24.1 ± 20.4% of horses per farm), which is unacceptable in terms of animal welfare. Husbandry systems with a large enough space allowance for turnout (≥200 m2 per horse) were found to be measures with a valuable synergetic effect, providing a resource which is an opportunity both for an environmentally sustainable and welfare-friendly horse husbandry. Demanuring remarkably decreased the nitrogen surplus (p < 0.001). Hence, based on the study, we recommend to daily demanure the pasture or paddock if the space allowance is less than 200 m2 per horse. In conclusion, regarding animal welfare, group housing is favorable, but regarding a site-specific environmentally friendly or even biodiversity-enhancing management, space allowance per horse is a crucial factor rather than the type of housing system. The two selected exemplary indictors demonstrate the need for a holistic and comprehensive decision support system that considers the linkage between horse welfare and environmental sustainability in order to assist peoples’ decision-making with horses under their care.

DESIDS: An Integrated Decision Support System for the Planning, Analysis, Management and Rehabilitation of Pressurised Irrigation Distribution Systems

Pressurized irrigation distribution systems (PIDSs) play a vital role in irrigation intensification, especially in the Mediterranean region. The design, operation and management of these systems can be complex, as they involve several intertwined processes which need to be considered simultaneously. For this reason, numerous decision support systems (DSSs) have been developed and are available to deal with these processes, but as independent components. To this end, a comprehensive DSS called DESIDS has been developed and tested. This DSS has been developed to bear in mind the needs of irrigation district managers for an integrated tool that can assist them in taking strategic decisions for managing and developing reliable, adequate and sustainable water distribution plans which provide the best services to farmers. Hence, four modules were integrated in DESIDS: (i) irrigation demand and scheduling module; (ii) hydraulic analysis module; (iii) operation and management module; and (iv) design and rehabilitation module. DESIDS was tested on different case studies, proving itself a valuable tool for irrigation district managers, as it provides a wide range of decision options for the proper operation and management of PIDSs. The developed DSS can be used as a platform for future integrations and expansions, and to include other processes needed for better decision-making support.

Modelling of post-diapause development and spring emergence of Cydia nigricana (Lepidoptera: Tortricidae).

The prediction of the post-diapause emergence is the first step towards a comprehensive decision support system that can contribute to a considerable reduction of pesticide use by forecasting a precise spraying date. The cumulative field emergence can be described as a function of the cumulative development rate. We investigated the impact of seven constant temperatures and five light regimes on post-diapause development in laboratory experiments. Development rate depended significantly on temperature but not on photoperiod. We therefore fit non-linear thermal performance curves, a better and more modern approach over past linear models, to describe the development rate as a function of temperature. The four parameter Brière function was the most suitable and was subsequently applied to temperature data from 36 previous pea fields, where pea moth emergence was measured with pheromone traps in Northern Hesse (Germany). In order to describe the variation in development times between individuals, we fit five nonlinear distribution models to the cumulative development rate as a function of cumulative field emergence. The three parameter Gompertz model was selected as the best fitted model. We validated the model performance with an independent field data set. The model correctly predicted the first moth in the trap and the peak emergence in 81.82% of cases, with an average deviation of only 2.00 and 2.09 days respectively.

DEVELOPMENT OF INFORMATION TECHNOLOGIES OF ACMB MANAGEMENT IN THE CONTEXT OF MONITORING OF ACHIEVEMENTS OF SUSTAINABLE DEVELOPMENT GOALS

The article is devoted to substantiation and development of the project of the two-level information system of management of association of co-owners of multi-apartment building (ACMB) which creates conditions for monitoring and increase of the level of achievement of the Sustainable Development Goals (SDGs) at the level of housing management. Based on the analysis of the Sustainable Development Goals and the tasks for their achievement at the level of ACMB, a system of indicators and data acquisition mechanisms for monitoring the achievement of SDGs related to ACMB are proposed. It is determined that in Ukraine, there is no system for assessing the achievement of SDGs for housing and communal services, including ACMB. The necessity of developing an information system as a useful tool for data collection and analysis of the effectiveness of management decisions at the level of ACMB in the context of achieving SDGs is substantiated. A project of twolevel information system has been developed to manage and support the activities of ACMB. In particular, the functions for achieving the SDGs at the levels of the board of ACMB and local governments have been identified. The proposed solution provides the principle of openness and accessibility of information by consolidating data in a single information space. This system allows monitoring and analysis of indicators of achievement of SDGs, coordinate the activities of the board of ACMB and local governments, evaluate the results of activities for the development of ACMB, generate reports on their activities, including the use of data mining tools. The advantage of the proposed two-level system is that the subsystem of the first level does not have special requirements (for its implementation, developer, etc.), except for its interoperability. The proposed project can be used in the development of a comprehensive decision support system in the field of housing at the level of local government and self-government.

Balancing practicality and validity of elder abuse identification measures: using data from adult protective services investigations

ABSTRACT Background and Objectives. In testing a comprehensive decision support system for Adult Protective Services (APS), this study addressed two problems common in APS research and practice: the psychometric quality of the measures and measurement burden. Research Design and Methods. Data were generated on 1,472 APS cases over six months in two California counties using the Identification, Services and Outcomes (ISO) Matrix, a comprehensive decision support system for APS. The ISO Matrix uses Short-Forms developed from the Elder Abuse Decision Support System (EADSS). Mini-Forms were developed from the Short-Forms and tested in order to reduce measurement burden. Mini-Forms were developed on each measure using sensitivity and specificity of the items in predicting the criterion of substantiation (yes/no). Psychometric quality was addressed by estimating predictive validity and Cronbach’s alpha of Short-Forms. Predictive validity and reliability were also estimated on the Mini-Forms as was their correlation with the Short-Forms. Results. On Short-Forms, good predictive validity was found for all measures except those that were very rare. Results for even shorter Mini-Forms were mixed, and some will require further research on their reliability and validity. Discussion and Implications. Short-Forms had good psychometric properties and some Mini-Forms did as well. Ongoing adoption by several California counties and Montana demonstrates the viability and sustainability of using the ISO Matrix for research and practice.

Comprehensive Decision Support System for Managing Asphalt Pavements

AbstractTransportation authorities evaluate the condition of roadway pavements based on the existence of various defects and apply maintenance strategies, if needed, to secure the safety of roads. ...

A Framework for Characterizing and Regulating Ecosystem Services in a Management Planning Context

Sustainable management promises to improve the conservation and utilization of ecosystem services and their contribution to human wellbeing through management plans. This paper explores the concept of characterization and integration of ecosystem services in a management planning concept. The integration process involves the identification, quantification, valuation, assessment, and monitoring of ecosystem services over time. The quantification of common ecosystem services, such as soil erosion, water conservation, recreation, biodiversity conservation, and carbon sequestration was explored. A framework was developed to integrate ecosystem services into management planning process. Ecosystem services are classified as provisioning, regulating, supporting, and cultural services with a defined typology. The conceptual framework acts as an organizing structure and it serves as a model for the management of ecosystems with their contribution to human wellbeing. Ecosystem management with multi-criteria decision techniques, information technologies and a structured participation is a proposed approach for the sustainable management of ecological, economic, and socio-cultural functions. Establishing the quantitative relationships between ecosystem services and societal benefits is essential. The provision of a universally accepted clear measurement of regulating, supporting, and cultural services is challenging. A commitment, vision, and strong willingness are required to adopt policies, regulations, and management objectives in planning. Integration can only be realized with prioritizing ecosystem services with the involvement of stakeholders. Substantial understanding of both the ecological and social systems is a prerequisite for sustainable management of ecosystem services. The ecosystem services with significant benefits to the wellbeing of society should primarily be characterized, their relative importance be weighted, and prioritized through a participatory approach. A holistic approach with a comprehensive decision support system is essential in forecasting the future provision of ecosystem services and assessing the trade-off analysis, resulting in better policy formulation before on-the-ground implementation.

ARSPivot, A Sensor-Based Decision Support Software for Variable-Rate Irrigation Center Pivot Systems: Part A. Development

HighlightsThe ARSPivot software seamlessly integrates site-specific irrigation scheduling methods with weather, plant, and soil water sensing systems in the operation of variable-rate irrigation (VRI) center pivot systems.ARSPivot embodies an Irrigation Scheduling Supervisory Control and Data Acquisition (ISSCADA) system that incorporates site-specific irrigation scheduling methods and automates the collection and processing of data obtained from sensing systems supporting them.ARSPivot incorporates a friendly graphical user interface (GUI) that assists in the process of setting up a computerized representation of a coupled ISSCADA VRI center pivot system and simplifies the review of irrigation prescriptions automatically generated based on sensor feedback.ARSPivot’s GUI includes a geographic information system (GIS) that relates sensed data and imported GIS data to specific field control zones.Abstract. The commercial availability of variable-rate irrigation (VRI) systems gives farmers access to unprecedented control of the irrigation water applied to their fields. To take full advantage of these systems, their operations must integrate site-specific irrigation scheduling methods that in turn should be supported by a network of sensing systems. An Irrigation Scheduling Supervisory Control and Data Acquisition (ISSCADA) system patented by scientists with the USDA-Agricultural Research Service (ARS) at Bushland, Texas, incorporates site-specific irrigation scheduling methods informed by weather, plant, and soil water sensing systems. This article introduces a software package, ARSPivot, developed to integrate the ISSCADA system into the operation of VRI center pivot systems. ARSPivot assists the operation and integration of a complex network of sensing systems, irrigation scheduling methods, and irrigation machinery to achieve this end. ARSPivot consists of two independent programs interacting through a client-server architecture. The client program is focused on automatically collecting and processing georeferenced data from sensing systems and communicating with a center pivot control panel, while the server program is focused on communicating with users through a friendly graphical user interface (GUI) involving a geographic information system (GIS). The GUI allows users to visualize and modify site-specific prescription maps automatically generated based on sensor-based irrigation scheduling methods, and to control and monitor the application of irrigation amounts specified in these recommended prescription maps using center pivots equipped for VRI zone control or VRI speed control. This article discusses the principles and design considerations followed in the development of ARSPivot and presents tools implemented in the software for the virtual design and physical operation of a coupled ISSCADA VRI center pivot system. This article also illustrates how the ISSCADA system and ARSPivot constitute a comprehensive sensor-based decision support system (DSS) for VRI management that is accessible to users without in-depth knowledge of sensing systems or irrigation scheduling methods. Keywords: Center pivot irrigation, Decision support system, Precision agriculture, Sensors, Site-specific irrigation scheduling, Software, Variable rate irrigation.n

Online Nuclear Emergency Response System (ONERS) for consequence assessment and decision support in the early phase of nuclear accidents - Simulations for postulated events and methodology validation

Online Nuclear Emergency Response System (ONERS) is a comprehensive Decision Support System developed using Open source WebGIS (Geographical Information System) technologies for management of Off-Site Nuclear Emergencies for Nuclear Power Plant sites of India. The system is based on numerical meteorological and dispersion models, weather data and source term estimation using environmental radiation monitors. It simulates the pattern of atmospheric dispersion, concentration, radioactivity fallout, and radiation dose profiles in various pathways around the affected site. The system projects the current and future radiological scenario along with geographical and demographic information and simulates the consequences of atmospheric releases on population by spatial analysis. It also computes counter measures using national regulatory guidelines for suggestion of protective actions to decision makers and first responders. This work presents the salient features of the ONERS, its monitoring network and utility. The methodology of dose projections is tested by performing simulations for a postulated accident and the effectiveness of the system for decision making during emergency scenarios is illustrated for a hypothetical scenario.

A Novel Clinical Decision Support System for Diagnosis and Treatment of Heart Failure: Concordance with Expert Decision

Objectives Conundrum health care providers and institutions face is how to deliver evidence-based medical care at lower cost to an ever expanding portion of society in need. Translation of clinical studies to daily practice and adoption of national and international guidelines have not met expectations. This disappointing shortfall stems primarily from lack of interface between ever growing and changing vast medical literature and individual patient profile. Concurrently, threats of penalties for underperformance and poor reimbursement related to improper documentation are increasing. Hence, transition to comprehensive clinical decision support systems (CDSS) will not just be inevitable but also rewarding. Purpose Examine the level of correlation between the novel CDSS and health care providers' clinical decisions. Methods CDSS for diagnosis and treatment of HF (Cardiologist Evaluator) was developed using a USPO patented methodology with artificial intelligence to function as an assistant to health care provider. It serves as a search engine to select evidence-based diagnostic and therapeutic options for an individual patient. A total of 389 patients with suspected HF were available between 2017 and 2018 at a suburban tertiary care hospital. Both Cardiologist Evaluator and health care providers (mid-level practitioner or junior faculty) gave recommendations regarding diagnosis and treatment for each patient independently. Concordance rate between Cardiologist Evaluator recommendations and actual health care providers’ decisions was evaluated. Furthermore, ICD-10 coding provided by Cardiologist Evaluator was compared to ICD-10 coding of health care provider for immediate cost effectiveness evaluation. Recommendations given by Cardiologist Evaluator were later presented to board certified HF cardiologists who were then asked if they find them acceptable or unacceptable for care of each individual patient. New concordance rate, this time between Cardiologist Evaluator and HF specialist was evaluated. Results Overall concordance rate between Cardiologist Evaluator recommendations and actual healthcare providers’ decisions was 69.7%. Most discrepancies were related to adoption of new therapies, dose advancements, workup for new onset heart failure, and/or referral for LVAD. HF specialists found recommendations given by Cardiologist Evaluator acceptable 99.3% of the time. Overall concordance rate between Cardiologist Evaluator and HF specialists was 98.9%. Cardiologist evaluator provided superior, automated ICD-10 coding in order of relevance. Conclusion Cardiologist Evaluator shows high accuracy in evidence-based diagnosis and treatment of HF. It may be useful for consistent implementation of clinical guidelines and more reliable coding, especially in centers without HF specialists.

Short Term Firm-Specific Stock Forecasting with BDI Framework

In today’s information age, a comprehensive stock trading decision support system which aids a stock investor in decision making without relying on random guesses and reading financial news from various sources is the need of the hour. This paper investigates the predictive power of technical, sentiment and stock market analysis coupled with various machine learning and classification tools in predicting stock trends over the short term for a specific company. Large dataset stretching over a duration of ten years has been used to train, test and validate our system. The efficacy of supervised non-shallow and prototyping learning architectures are illustrated by comparison of results obtained through myriad optimization, classification and clustering algorithms. The results obtained from our system reveals a significant improvement over the efficient market hypothesis for specific companies and thus strongly challenges it. Technical parameters and algorithms used have shown a significant impact on the predictive power of the system. The predictive accuracy obtained is as high as 70–75% using linear vector quantization. It has been found that sentiment analysis has strong correlation with the future market trends. The proposed system provides a comprehensive decision support system which aids in decision making for stock trading. We also present a novel application of the BDI framework to systematically apply the learning and prediction phases.

Service quality measurement model integrating an extended SERVQUAL model and a hybrid decision support system

One of the most effective solutions to sustain an organization in competitive environments is the strategy of continuous improvement. Consequently, the proposed strategy needs a continuous performance evaluation measurements. A solution to evaluate service quality measurements is to rank the service providers. The ranking procedure in similar complex problems or selection problems can be efficiently implemented with the support of multiple attribute decision making (MADM) methods. This study aims at developing a comprehensive decision support system (DSS) based on multiple MADM methods along with an extended quality of service evaluation model to assess the service quality of certain cultural centers. The suggested service quality measurement model in this study is recognized as a valid and reliable tool based on statistical modeling and validation methods. Moreover, the suggested DSS model has been developed based on integration of target-based F-MULTIMOORA and Fuzzy Axiomatic Design (FAD) methods combined with the best-worst method (BWM). Accordingly, a case study was investigated regarding the assessment of seven art galleries in Tehran, Iran. Ultimately, an extensive managerial investigation of the DSS model has been presented based on the real-world examples to show the practicality of the propositions of the current study.

Prospects for Using of Decision Support Systems for Assessment and Control of Technogenic Pressure on the Environment

Purpose. Designing the database concerning level of technogenic load on the environment. Development of the software for database control and zoning Ukrainian area by the techonogenic load. Methods. The GIS free software QGIS is used as main tool for spatial data analysis and designing the digital maps. The secondary tool is Environmental Decision Support Systems software which has been developed by author of the research. The main mathematical algorithms are cluster and factor analysis. Results. The comprehensive approach to multidimensional zoning has been introduced. The integral index of technogenic load on the environment has been defined. The integral index is based on particular indexes which describes technodenic impacts on atmosphere, water and soils. The territory of Ukraine has been zoned by the level of technogenic load on the environment. There has been calculated comprehensive map of spatial distribution for technogenic load on the environment of Ukraine. There have been designed The digital map database, which describes conditions of the environment of Ukraine, and appropriate database control system. Author has developed the comprehensive software Envoronmental Decision Support systems by utilizing objectice-oriented language C++. The core of the application is geoinrormational models and appropriate mathematical algorithms for spatial data analysis. Conclusions. The areas with high levels of technogenic load on the environment have been outlined. The developed approach and software might be useful for state and local authority institutions control activities which directed to reduction of negative impacts on the environment.

Integration of Soft Rock and Sand into Soil and Its Application Effect Evaluation

Mu us sandy land is a typical ecologically fragile area in our country, which will bring remarkable comprehensive benefits to its rational development and utilization. This research systematically explored the comprehensive improvement technology of Mu Us sandy land through indoor experiment, plot test and field demonstration, and refined the soft rock and sand combined sand-fixing ability improvement technology, efficient irrigation water-saving technology, water and fertilizer management technology, and developed a comprehensive development scale decision support system, which can provide decision support for the development of Mu Us sandy land. Through the integration of technologies, demonstration application has been carried out in Mu Us sandy land of northern Shaanxi and good ecological, social and economic benefits have been achieved.

Efficient In-Database Patient Similarity Analysis for Personalized Medical Decision Support Systems

Abstract Patient similarity analysis is a precondition to apply machine learning technology on medical data. In this sense, patient similarity analysis harnesses the information wealth of electronic medical records (EMRs) to support medical decision making. A pairwise similarity computation can be used as the basis for personalized health prediction. With n patients the amount of ( n 2 ) similarity calculations is required. Thus, analyzing patient similarity leads to data explosion when exploiting big data. By increasing the data size the computational burden of this analysis increases. A real-life medical application may exceed the limits of current hardware in a fairly short amount of time. Finding ways to optimize patient similarity analysis and handling this data explosion is the topic of this paper. Current implementations for patient similarity analysis require their users to have knowledge of complex data analysis tools. Moreover, data pre-processing and analysis are performed in synthetic conditions: the data are extracted from the EMR database and then the data preparation and analysis are processed in external tools. After all of this effort the users might not experience a superior performance of the patient similarity analysis. We propose methods to optimize the patient similarity analysis in order to make it scalable to big data. Our method was tested against two real datasets and a low execution time was accomplished. Our result hence benefits a comprehensive medical decision support system. Moreover, our implementation comprises a balance between performance and applicability: the majority of the workload is processed within a database management system to enable a direct implementation on an EMR database.

A deterministic contractor selection decision support system for competitive bidding

PurposeThe construction industry today is one of the biggest industries in the world. As projects continue to grow in complexity, project management continues to evolve. Contractor selection is a difficult task that owners and project managers face. Although previously researchers have worked on the subject of contractor selection, a comprehensive decision support system for contractor selection has not yet been developed. Recent reports of major delays and cost overruns in mega projects highlight the need for a model that is able to be flexible and comprehensive becomes evident. The paper aims to discuss these issues.Design/methodology/approachThe research focuses on obtaining insights from field experts using both quantitative and qualitative methods. Then, a model was developed in the light of the data collected. Accordingly, the model was tested on a case study.FindingsThis paper presents a model for contractor selection that is wholesome in its take on the topic. The model incorporates both managerial and technical aspects of the problem. The model was tested on a case study and it was proven to be feasible in real world applications. The contractor selection decision support system serves the needs of both academics and industry managers, as an integral part of project management.Originality/valueThe model presented in this paper is innovative in its take on the problems. MCDA tools have been uniquely modified in this paper to cater to the needs of the selection problem while accounting for the criteria hierarchy that incorporates aspects that are instrumental for proper evaluation of a contractor’s likelihood of success.

An uncertainty-based comprehensive decision support system for site-specific crop management

This paper presents a framework for the implementation of a decision support system that considers spatial, temporal and managerial factors in assessing the potential impact of crop amendments on the cost of a given production scenario. The proposed system includes a database and a numeric simulation model. The database is linked to previously recorded crop responses for a given agricultural input under different conditions while the numeric simulation model determines the probability of different levels of profit for each decision option. This system then determines the optimal uniform rate of application of an amendment to maximize profits, or to define the range of such rates for a case of variable rate application. Uncertainty-based treatment of each model input allows for a balance between the potential results of under-application or over-application.

Spatial data utilization for location pattern analysis

Retail industry growth in Indonesia is considered significant and will continue, related to Indonesian lifestyle changes. One of the important things to consider is location determination. The complexity of the location determination problem for the development of a business requires a comprehensive decision support system, using relevant data and appropriate technology so that it can accommodate a variety of data formats and information. This paper described the spatial pattern analysis approach to discover patterns of fast food restaurants. ArcGIS Spatial Autocorrelation (Moran’s I) tool was used to find evidence that the fast food restaurant distribution in Jakarta, as the biggest city in Indonesia, has a clustered pattern and were not the result of random chance. The clustered pattern was investigated using Kernel Density Estimation (KDE) to identify the clustered area of fast food restaurant.

Modeling the cost-effectiveness of stormwater best management practices in an urban watershed in Las Vegas Valley

The Las Vegas Valley is one of the fastest growing urban areas in the United States. Any future urbanization and climate change, especially if the climate becomes wetter and there are more intense and frequent storm events, will undoubtedly cause excessive urban stormwater runoff and exacerbate flooding in the Valley. Stormwater management is therefore one of the key challenges to the local government. The main goal of this study was to ascertain the utility of Best management Practices (BMPs) in mitigating stormwater runoff in the Duck Creek watershed in the Las Vegas Valley. The cost-effectiveness of different BMPs were also compared so as to determine the best BMP arrangement under the current and future climate and land use change conditions. By applying SUSTAIN (System for Urban Stormwater Treatment and Analysis INtegration) as a comprehensive GIS-based modeling and decision support system in the BMP analyses, modeling results show that although the installation of the existing three detention basins in the watershed can provide a 9% flow reduction, these three detention basins alone will not be adequate in the future with the impending changes in climate and land use. To alleviate the potential problems of water resources, SUSTAIN was further used to determine the number of additional BMPs required, the optimal types of BMPs (such as, detention and infiltration BMPs), and their locations in reducing storm runoff in the watershed under the future land-use and climate regimes. By comparing the performances of five potential BMP implementation scenarios, it was found that a mixed implementation of one additional detention BMP and one infiltration BMP to the existing detention BMPs in the downstream area of the watershed would be the most cost-effective stormwater runoff control solution in face of future urbanization and climate change. Depending on the locations of the new detention and infiltration BMPs, surface flow, even without BMP retrofit options, could be reduced by 27–31%. The cost for such installations would range from $1,141,000 to $1,319,000. This information may be useful to local planners and water resource managers in their efforts to develop alternative stormwater management plans in the arid Southwest.