K. John Holmes

Bio: K. John Holmes is an academic researcher. The author has contributed to research in topics: Decision support system & Graphics software. The author has an hindex of 1, co-authored 1 publications receiving 53 citations.

Operational Guidance During Droughts: Expert System Approach

Abstract: Seattle, Washington, suffered its most extreme drought on record during the summer and fall of 1987. Severe and continuing water use restrictions were required to limit the drought's impact on municipal water supplies, fish populations, and navigation. This paper describes a decision support system used to aid in drought decisions. Its components include an expert system, a linear programming model, database management tools, and computer graphics. The expert system incorporates operator experience and intuition using a rule base developed through interviews with management personnel from the Seattle Water Department. The expert system also integrates the other programming techniques into a single system. A linear programming model determines system yield and optimal operating policies for past hydrologic regimes. Database management and graphics software store and allow the display of over two thousand operating policies to decision‐makers. The system provides user‐friendly support to help decision‐maker...

Reservoir‐System Simulation and Optimization Models

Abstract: A broad array of computer models has been developed for evaluating reservoir operations. Selecting a modeling and analysis approach for a particular application depends upon the characteristics of ...

An Intelligent Decision Support System for Management of Floods

Abstract: 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.

Decision Support System for Conjunctive Stream-Aquifer Management

Abstract: A decision support system (DSS) is presented for conjunctive management of surface water and ground water under prior appropriation. The DSS is constructed around the generalized river basin network flow model MODSIM, providing an open architecture allowing access to input and output databases and modification and verification at all levels of the modeling process. The graphical user interface for the MODSIM DSS provides spatially referenced database capabilities whereby the user can create and link river-basin network objects on the display and populate and import data for that object interactively. Geographic information system tools are used to prepare grid-based spatial data for input into MODRSP, a modified version of the USGS three-dimensional finite-difference ground water model MODFLOW. Response functions generated by MODRSP are provided to MODSIM for simulating spatially varied and time-lagged return/depletion flows from stream-aquifer interactions. Capabilities of the MODSIM DSS are demonstrated on a case study for a portion of the Lower South Platte River Basin, Colorado. Results of the case study indicate significant differences between using ground water response coefficients developed from preassigned stream depletion factor (SDF) values, as currently used in the basin, and those generated using a finite-difference ground water model.

Short-term water demand forecast modeling techniques: Conventional methods versus AI

Abstract: A variety of forecast modeling techniques, from conventional techniques such as regression and time series analyses to relatively new artificial intelligence (AI) techniques such as expert systems and artificial neural networks (ANNs), were investigated for use in short-term water demand forecasting. Daily water demand, daily maximum air temperature, and daily total rainfall data from Lexington, Ky., for 1982-92 were used to develop and test several forecast models. The performance of each model was evaluated using two standard statistical parameters. On the basis of the measured statistical parameters, the Al models outperformed the conventional models. Both expert system and ANN technologies should be further explored by water utility engineers and managers because these techniques have the potential to enhance the operational performance of various water supply and delivery systems.