Rule-Based Expert System For Sensor Deployment In Drinking Water Systems For Rural Communities

Meeting security objectives for a water utility is a challenging task. The need for thoughtful and comprehensive planning for prevention, mitigation, response and recovery from an event became even clearer after the attacks of 9/11. In response, an Emergency Response Plan (ERP) for water supply systems is now required under the Bioterrorism Act of 2002 in the US. As part of this process, each US utility will incorporate the results of their Vulnerability Assessment (VA) into an existing or new ERP. This could result in a need to deploy a suite of sensors in the drinking water networks to replace the traditional manual sampling campaigns. However, rural communities that have very little resources might not be able to come up a solid ERP. The goal of this paper is to present the architecture of a prototype expert system for decision support of sensor deployment in local water utilities. It presents the integration of knowledge acquisition and management for structuring an effective sensor network for rural communities. VA can then be made possible dynamically with respect to the targeted chemicals and microorganisms in the tap water

Short-Term Streamflow Forecasting With Global Climate Change Implications - A Comparative Study Between Genetic Programming And Neural Network Models

Sustainable water resources management is a critically important priority across the globe. While water scarcity limits the uses of water in many ways, floods may also result in property damages and the loss of life. To more efficiently use the limited amount of water under the changing world or to resourcefully provide adequate time for flood warning, the issues have led us to seek advanced techniques for improving streamflow forecasting on a short-term basis. This study emphasizes the inclusion of sea surface temperature (SST) in addition to the spatio-temporal rainfall distribution via the Next Generation Radar (NEXRAD), meteorological data via local weather stations, and historical stream data via USGS gage stations to collectively forecast discharges in a semi-arid watershed in south Texas. Two types of artificial intelligence models, including genetic programming (GP) and neural network (NN) models, were employed comparatively. Four numerical evaluators were used to evaluate the validity of a suite of forecasting models. Research findings indicate that GP-derived streamflow forecasting models were generally favored in the assessment in which both SST and meteorological data significantly improve the accuracy of forecasting. Among several scenarios, NEXRAD rainfall data were proven its most effectiveness for a 3-day forecast, and SST Gulf-to-Atlantic index shows larger impacts than the SST Gulf-to-Pacific index on the streamflow forecasts. The most forward looking GP-derived models can even perform a 30-day streamflow forecast ahead of time with an r-square of 0.84 and RMS error 5.4 in our study. © 2008 Elsevier B.V. All rights reserved

Short-term streamflow forecasting with global climate change implications -- A comparative study between genetic programming and neural network models

Sustainable water resources management is a critically important priority across the globe. While water scarcity limits the uses of water in many ways, floods may also result in property damages and the loss of life. To more efficiently use the limited amount of water under the changing world or to resourcefully provide adequate time for flood warning, the issues have led us to seek advanced techniques for improving streamflow forecasting on a short-term basis. This study emphasizes the inclusion of sea surface temperature (SST) in addition to the spatio-temporal rainfall distribution via the Next Generation Radar (NEXRAD), meteorological data via local weather stations, and historical stream data via USGS gage stations to collectively forecast discharges in a semi-arid watershed in south Texas. Two types of artificial intelligence models, including genetic programming (GP) and neural network (NN) models, were employed comparatively. Four numerical evaluators were used to evaluate the validity of a suite of forecasting models. Research findings indicate that GP-derived streamflow forecasting models were generally favored in the assessment in which both SST and meteorological data significantly improve the accuracy of forecasting. Among several scenarios, NEXRAD rainfall data were proven its most effectiveness for a 3-day forecast, and SST Gulf-to-Atlantic index shows larger impacts than the SST Gulf-to-Pacific index on the streamflow forecasts. The most forward looking GP-derived models can even perform a 30-day streamflow forecast ahead of time with an r-square of 0.84 and RMS error 5.4 in our study. © 2008 Elsevier B.V. All rights reserved

Application of genetic programming models incorporated in optimization models for contaminated groundwater systems management

Two different applications of Genetic Programming (GP) for solving large scale groundwater management problems are presented here. Efficient groundwater contamination management needs solution of large sale simulation models as well as solution of complex optimal decision models. Often the best approach is to use linked simulation optimization models. However, the integration of optimization algorithm with large scale simulation of the physical processes, which require very large number of iterations, impose enormous computational burden. Often typical solutions need weeks of computer time. Suitably trained GP based surrogate models approximating the physical processes can improve the computational efficiency enormously, also ensuring reasonably accurate solutions. Also, the impact factors obtained from the GP models can help in the design of monitoring networks under uncertainties. Applications of GP for obtaining impact factors implicitly based on a surrogate GP model, showing the importance of a chosen monitoring location relative to a potential contaminant source is also presented. The first application utilizes GP models based impact factors for optimal design of monitoring networks for efficient identification of unknown contaminant sources. The second application utilizes GP based ensemble surrogate models within a linked simulation optimization model for optimal management of saltwater intrusion in coastal aquifers