Drought modeling-A review

During the past three decades, the application of the systems approach to reservoir management and operations has been established as one of the most important advances made in the field of water resources engineering A primary role of systems analysis is to provide an improved basis for decision making It has been concluded that a gap still exists between research studies and the application of a systems approach in practice The objective of this paper is to provide a short review of the mathematical models used in reservoir management and operations, to present conclusions reached by previous state-of-the-art reviews, and to provide two ideas for closing the gap between theory and practice First, a simple simulation-optimization model for reservoir sizing has been presented as an example of systems approach respond to practical needs of water resources engineers The second example illustrates the benefits of knowledge-based technology with regard to single-multipurpose reservoir analysis

Reservoir Systems Analysis: Closing Gap between Theory and Practice

Integrating human knowledge with modeling tools, an intelligent decision support system (DSS) is developed to assist decision makers during different phases of flood management. The DSS is developed as a virtual planning tool and can address both engineering and non-engineering issues related to flood management. Different models (hydrodynamic, forecasting, and economic) that are part of the DSS share data and communicate with each other by providing feedback. The DSS is able to assist in: selecting suitable flood damage reduction options (using an expert system approach); forecasting floods (using artificial neural networks approach); modeling the operation of flood control structures; and describing the impacts (area flooded and damage) of floods in time and space. The proposed DSS is implemented for the Red River Basin in Manitoba, Canada. The results from the test application of DSS for 1997 flood in the Red River Basin are very promising. The DSS is able to predict the peak flows with 2% error and reveals that with revised operating rules the contribution of Assiniboine River to the flooding of Winnipeg city can be significantly reduced. The decision support environment allows a number of "what-if" type questions to be asked and answered, thus, multiple decisions can be tried without having to deal with the real life consequences.

An Intelligent Decision Support System for Management of Floods

Demands on fresh water by human beings have been continuously increasing due to population growth, living standard improvement, and economic development. At the same time, many regions are suffering greatly from floods and droughts. Those are the results of ineffective management of water resources due to the associated complexities. In this study, a decision support system (DSS) was developed for supporting integrated water resources management in Daegu city, Republic of Korea. The developed DSS contained four subsystems including database, modelbase, and knowledgebase, as well as general user interface (GUI). It was then connected with the National Water Management Information System (WAMIS). A flow prediction could be conducted through the incorporated HEC-HMS Version 3.0.1. Also, an urban water demand forecasting model was developed using an artificial neural network (ANN) based model. At the same time, a water resources management model based on genetic algorithm (GA) was developed in the DSS, facilitating efficient allocation of water resources among different regions within a city. The result indicated that the developed DSS is very useful to deal with complex water resources management problems and could be further applied to similar cities in South Korea.

Development of a web-based decision support system for supporting integrated water resources management in Daegu city, South Korea

Heuristic knowledge has been integrated with analytical tools to support decision making for flood management. Development of an expert system called Intelligent Flood Management System for the selection of appropriate flood damage reduction measures for a given area is described. The selection of flood damage reduction measures is based on hydraulic, hydrological, geotechnical, environmental, and economic factors related to the river system and the area to be protected from floods. The knowledge base of the Intelligent Flood Management System is generic and can be used to identify a suitable flood management option for any area. The model base of the Intelligent Flood Management System consists of the hydraulic analysis package HEC-RAS, the flood damage analysis program HEC-FDA, and a model for economic analysis. The graphical user interface is developed for effective communication with the system. The developed system has been implemented to identify appropriate flood damage reduction options for the to...

Integration of heuristic knowledge with analytical tools for the selection of flood damage reduction measures

Expert systems have been proposed for a wide range of civil engineering applications. They have been applied most successfully in situations governed by simple rules. Many civil engineering decisions, however, must be made in environments in which simple rules are not possible, but rather large amounts of data must be analyzed and evaluated. This paper presents a methodology in which an expert system shell (INSIGHT 2+) is used to evaluate and display information on drought management planning. A linear programming model is used to generate optimal operating policies as a function of numerous past drought experiences. These policies are incorporated into an expert system where the user is required to identify the degree to which the current drought situation is similar to past events. The expert system then aids the user in developing appropriate management policies based on time of year, descriptions of drought intensity, and other system characteristics.

Robert M. Tull

Papers

Expert System for Drought Management Planning