An object-oriented approach to structuring multicriteria decision support in natural resource management problems

Includes bibliographical references.The undertaking of MCDM (Multicriteria Decision Making) and the development of DSSs (Decision Support Systems) tend to be complex and inefficient, leading to low productivity in decision analysis and DSSs. Towards this end, this study has developed an approach based on object orientation for MCDM and DSS modelling, with the emphasis on natural resource management. The object-oriented approach provides a philosophy to model decision analysis and DSSs in a uniform way, as shown by the diagrams presented in this study. The solving of natural resource management decision problems, the MCDM decision making procedure and decision making activities are modelled in an object-oriented way. The macro decision analysis system, its DSS, the decision problem, the decision context, and the entities in the decision making procedure are represented as "objects". The object-oriented representation of decision analysis also constitutes the basis for the analysis ofDSSs

Methodology for the optimal management design of water resources system under hydrologic uncertainty

Un sistema de gestión de sequías apropiado requiere de la anticipación de los posibles efectos que un episodio de este tipo tenga sobre el sistema de recursos hídricos. Esta tarea sin embargo resulta más complicada de lo que parece. En primer lugar, debido al alto grado de incertidumbre existente en la predicción de variables hidrológicas futuras. Y en segundo, debido al riesgo de sobrerreacción en la activación de medidas de mitigación generando falsa sensación de escasez, o sequía artificial. A este respecto, los planes especiales de sequía proveen de herramientas para la gestión eficiente de situaciones con escasez de recursos y la preparación de cara a futuros eventos. De todos modos, las diferentes estrategias de operación seguidas en cada sistema de recursos hídricos hacen que las herramientas que en algunos casos resultaron altamente útiles no lo sean tanto cuando se aplican en sistemas distintos. Debido a la falta de tiempo y/o al exceso de confianza en los trabajos realizados por terceros, con excelentes resultados en sus respectivos casos, a veces se cae en el error de implementar metodologías no del todo apropiadas en sistemas con requisitos completamente distintos. El desarrollo y utilización de metodologías generalizadas aplicables a diferentes sistemas y capaces de proporcionar resultados adaptados a cada caso es, por tanto, muy deseable. Este es el caso de las herramientas de modelación de sistemas de recursos hídricos generalizadas. Estas permiten homogeneizar los procesos mientras siguen siendo los suficientemente adaptables para proporcionar resultados apropiados para cada caso de estudio. Esta tesis presenta una serie de herramientas destinadas a avanzar en el análisis y comprensión de los sistemas de recursos hídricos, haciendo énfasis en la prevención de sequías y la gestión de riesgos. Las herramientas desarrolladas incluyen: un modelo de optimización generalizado para esquemas de recursos hídricos, con capacidad para la representación detallada de cualquier sistema de recursos hídricos, y una metodología de análisis de riesgo basada en la optimización de Monte Carlo con múltiples series sintéticas. Con estas herramientas es posible incluir tanto la componente superficial como la subterránea del sistema estudiado dentro del proceso de optimización. La optimización está basada en la resolución iterativa de redes de flujo. Se probó la consistencia y eficiencia de diferentes algoritmos de resolución para encontrar un balance entre la velocidad de cálculo, el número de iteraciones, y la consistencia de los resultados, aportando recomendaciones para el uso de cada algoritmo dadas las diferencias entre los mismos. Las herramientas desarrolladas se aplican en dos casos de estudio reales en la evaluación y posibilidad de complementación de los sistemas de monitorización y alerta temprana de sequías existentes en los mismos. En el primer caso, se propone un enfoque alternativo para la monitorización de la sequía en el sistema de operación anual del río Órbigo (España), complementándolo con la utilización de la metodología de análisis de riesgo. En el segundo caso, las herramientas se emplean en un sistema con una estrategia de operación completamente distinta. Se estudia como el análisis de riesgo de la gestión óptima puede ayudar a la activación anticipada de los escenarios de sequía en los sistemas de los ríos Júcar y Turia, cuya operación es hiperanual. En esta ocasión, el sistema de indicadores existente goza de una gran confianza por parte de los usuarios. La metodología de análisis de riesgo es, sin embargo, capaz de anticipar los eventos de sequía con mayor alarma, aspecto que es deseable si se quiere evitar que los episodios en desarrollo vayan a más. En ambos casos se muestra como la evaluación anticipada de las posibles situaciones futuras del sistema permiten una definición confiable de los escenarios de sequía con suficiente antelación para la activación efectiva de medidas de prevención y/o mitigación en caso de ser necesarias. La utilización de indicadores provenientes de modelos frente a indicadores basados en datos observados es complementaria y ambos deberían utilizarse de forma conjunta para mejorar la gestión preventiva de los sistemas de recursos hídricos. El empleo de modelos de optimización en situaciones de incertidumbre hidrológica es muy apropiado gracias a la no necesidad de definir reglas de gestión para obtener los mejores resultados del sistema, y teniendo en cuenta que las reglas de operación habituales pueden no ser completamente adecuadas en estas ocasiones.Haro Monteagudo, D. (2014). Methodology for the optimal management design of water resources system under hydrologic uncertainty [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/45996TESI

A decision support tool to improve binational water quality planning and management in the lower Rio Grande/Río Bravo

This dissertation describes the development of a decision support tool designed to facilitate and enhance collaborative binational decision making associated with integrated transboundary water quality planning and management in the Lower Rio Grande/Río Bravo. The Lower Rio Grande Water Quality Initiative Decision Support System (LRGWQIDSS) is the result of a multidisciplinary effort to integrate the results of qualitative social science research and traditional and novel engineering and geographic information systems (GIS) methods associated with the modeling, analysis, and visualization of watershed, water quality and natural resources data. The LRGWQIDSS incorporates information currently used by urban planning and natural resource management organizations working along the Texas-Mexico border area and provides a means to analyze and display the information in a way that is useful to institutional and noninstitutional actors involved in transboundary water quality planning efforts. The analysis of the institutional arrangements currently in place to protect water quality in the Lower Rio Grande/Río Bravo played an important role in the design and development of the LRGWQIDSS and its successful application. The tool’s development represents a case study in the importance of the role of institutional analysis in the successful development of decision support systems for transboundary water quality management.Public Polic

Integrating Knowledge for River Basin Management: Progress in Thailand

Resource /Energy Economics and Policy,

The development of a single strategy for the integration of quantitative and qualitative data types for the production of decision support systems

The research described in this thesis expresses the importance of quantitative and qualitative data types and how these can be incorporated and combined to produce an agricultural management decision support system (DSS). Researchers cannot solely depend on numerical data and relationships when designing, modelling and producing decision management tools. The relevance of the social sciences and peoples interpretations of these tools is equally important. The DSS described here focuses on the management of rainwater harvesting (RWH) in Tanzania. Numerical data related to natural resources (water and nutrients) and yields of rice and maize have been collected for the production of the DSS. With regard to the social science factors, the DSS tackles the concept of common pool resources (CPR) of water and nutrients. The importance of CPR is well understood, however their inclusion in the production of models is a relatively new concept. Criteria related to social status is linked with the by laws that govern the allocation of natural resources in Tanzania to help derive a numerical method for including CPR within the DSS. The production of the DSS is a novel way of combining this research into a tool that aims to benefit all socio-economic community groups. During the production of the DSS, a single generic approach for the inclusion of quantitative and qualitative information has developed. Particular focus was on the development of a model base (programming and mathematical relationship building), database (storage of the data used for the relationships) and a dialog system (the user-interface and communication strategy). This method is termed the ‘dialog, data, and models (DDM)’ paradigm (Sprague and Carlson, 1982). From this research, a DSS has been produced that aims to optimise RWH management in Tanzania with the aim of alleviating poverty and enhancing sustainable agriculture for all community members. Also an overall strategy for the production of DSSs has been produced. It illustrates how both quantitative (numerical and physical data) and qualitative (socio-economic considerations) can be utilised individually and in combination for the production of DSSs and can be extrapolated for further research and to new areas

Decision-support system for domestic water demand forecasting and management

PhD ThesisA generic but flexible decision-support system for domestic water demand forecasting and management (DFMS) has been developed as part of a highlyintegrated decision-support system for river-basin management. Its purpose is to provide water-resources planners with the facilities for estimating future water demand for any demand region and time period, having regard to the possibility of introducing demand-management measures. The system has the capability of predicting domestic-water demand by various methods according to the data availability, computing conservation effectiveness due to the implementation of various demand-management measures, forecasting the number of customers for different consumption units (person, household, water connection) and facilitating the development of demand-scenarios for eveluating various options. The system is designed in such a way that makes it easy to use for both novice and experienced users since it is driven by a menu system which relies on a mouse rather than the keyboard. Moreover, the communication between user and the system is by means of a user-friendly interface which makes extensive use of hypertext and colour graphics in presenting the results. Briefly, DFMS comprises the following components: a GIS that stores, displays and analyses all geo-coded information such as satellite imagery, urban areas, cities and towns, etc.; • a database which provides access to non-spatial data such as demand-area location and characteristics including top-level descriptors such as population, total demand, per-capita consumption, etc.; • an expert system which uses the rule-based inference for data entry and predicting values (quantitative or qualitative) of variables from the knowledgebase; . four methods of demand forecasting ranging from superficial to detailed, namely time extrapolation, econometric variables, end-uses variables and households classification; a multi-objective decision component which helps the user to determine the most appropriate forecasting method and conservation measures; • a set of mathematical models to provide the analytical capability for quantifying descriptors, producing multiple outputs etc.; • a user-interface with access to the various functional components of the system and the various help/explain files; • a set of pre- and post-processors which support editing of the inputs data and the visualisation or analysis of model output, in addition to handling scenarios for each of the models or variables; • a set of help files which are used to provide the user with the necessary assistance if for any reason, a more detailed explanation is required, based on a hypertext; In order to demonstrate the system capability, DFMS has been applied to the Swindon demand area of Thames Water Utilities Ltd.Department of Civil Engineering, University of Newcastle upon Tyne, World Bank (Joint/Japan Scholarship Program) British Embassy, Amma

The development of a single strategy for the integration of quantitative and qualitative data types for the production of decision support systems

The research described in this thesis expresses the importance of quantitative and qualitative data types and how these can be incorporated and combined to produce an agricultural management decision support system (DSS). Researchers cannot solely depend on numerical data and relationships when designing, modelling and producing decision management tools. The relevance of the social sciences and peoples interpretations of these tools is equally important. The DSS described here focuses on the management of rainwater harvesting (RWH) in Tanzania. Numerical data related to natural resources (water and nutrients) and yields of rice and maize have been collected for the production of the DSS. With regard to the social science factors, the DSS tackles the concept of common pool resources (CPR) of water and nutrients. The importance of CPR is well understood, however their inclusion in the production of models is a relatively new concept. Criteria related to social status is linked with the by laws that govern the allocation of natural resources in Tanzania to help derive a numerical method for including CPR within the DSS. The production of the DSS is a novel way of combining this research into a tool that aims to benefit all socio-economic community groups. During the production of the DSS, a single generic approach for the inclusion of quantitative and qualitative information has developed. Particular focus was on the development of a model base (programming and mathematical relationship building), database (storage of the data used for the relationships) and a dialog system (the user-interface and communication strategy). This method is termed the ‘dialog, data, and models (DDM)’ paradigm (Sprague and Carlson, 1982). From this research, a DSS has been produced that aims to optimise RWH management in Tanzania with the aim of alleviating poverty and enhancing sustainable agriculture for all community members. Also an overall strategy for the production of DSSs has been produced. It illustrates how both quantitative (numerical and physical data) and qualitative (socio-economic considerations) can be utilised individually and in combination for the production of DSSs and can be extrapolated for further research and to new areas

Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped upon decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers