Drought modeling-A review

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

Proper management of reservoirs in flood conditions minimizes flood damage and keeps the reservoir in a stable condition. This paper presents a Web-based decision support system of reservoir flood management (WDRFM) for reservoirs with gated spillways. WDRFM is capable of estimating the current situation of the reservoir and before the flood reaches the reservoir, provides the operator with suggestions to have optimal control over gates, using multistage simulation-optimization models to minimize flood damage downstream. Investigating the possibility of changing the dam’s discharge gates, carrying out dam pre-release, and announcing relevant flood control warnings can all be performed by WDRFM, while allowing the operator to determine such observations and make final decisions on a time step basis. To assess the performance of WDRFM, 15 scenarios in four groups for flood management were defined on April 14, 2016 at Dez Dam, Iran. A comparison of one scenario with similar initial conditions with the occurrence of flood event has shown that a daily peak discharge shows 997 m/s decrease from that recorded by the operator. The ability to examine different scenarios based on the conditions at any time in the WDRFM enables the decision-makers and operators to confront various circumstances. doi: 10.2166/hydro.2020.185 ://iwaponline.com/jh/article-pdf/22/3/641/693338/jh0220641.pdf Mokhtar Ghobadi Hesam Seyed Kaboli (corresponding author) Jundi-Shapur University of Technology, Dezful, Iran E-mail: hkaboli@jsu.ac.ir

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Developing a Web-based decision support system for reservoir flood management

A daily drought-monitoring system was developed using the Clips Expert System software package. The monitoring system consists of four components: (1) the knowledge base, which contains rules in th...

Development of an Expert System for Daily Drought Monitoring

A daily drought monitoring system was developed using the Clips Expert System software package. It consists of four components: (1) The knowledge base that contains rules in the form of IF-THEN type for decision of assigning a daily drought severity; (2) The working memory that contains facts of historic drought characteristics including truncation levels, mean durations, and mean conditional probabilities estimated at gaging stations; (3) The inference engine that makes inferences to decide what rules are satisfied by current facts for assigning drought severity levels; (4) The user interface that provides the mechanism for a communication between the user and the system. A six-month period of the 1988 drought in the central Ohio region was used to test the developed system. It was found that the developed system could effectively detect daily drought occurrences.

X. A. Kleopa

Papers

Development of an Expert System for Daily Drought Monitoring

Development of an Expert System for Daily Drought Monitoring