Using an Adaptive Neuro-fuzzy Inference System in the Development of a Real-Time Expert System for Flood Forecasting
This chapter describes the development of a prototype flood forecasting system provided in a real-time expert system shell called COGSYS KBS. Current efforts on the development of flood forecasting approaches have highlighted the need for fuzzy-based lear
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Using an Adaptive Neuro-fuzzy Inference System in the Development of a Real-Time Expert System for Flood Forecasting I.D. Cluckie, A. Moghaddamnia and D. Han
Abstract This chapter describes the development of a prototype flood forecasting system provided in a real-time expert system shell called COGSYS KBS. Current efforts on the development of flood forecasting approaches have highlighted the need for fuzzy-based learning strategies to be used in extracting rules that are then encapsulated in an expert system. These strategies aim to identify heuristic relationships that exist between forecast points along the river. Each upstream forecast point automatically produces extra knowledge for target downstream forecast points. Importantly, these strategies are based on the adaptive network-based fuzzy inference system (ANFIS) technique, which is used to extract and incorporate the knowledge of each forecast point and generate a set of fuzzy “if–then” rules to be exploited in building a knowledge base. In this study, different strategies based on ANFIS were utilised. The ANFIS structure was used to analyse relationships between past and present knowledge of the upstream forecast points and the downstream forecast points, which were the target forecast points at which to forecast 6-hour-ahead water levels. During the latter stages of development of the prototype expert system, the extracted rules were encapsulated in COGSYS KBS. COGSYS KBS is a realtime expert system with facilities designed for real-time reasoning in an industrial context and also deals with uncertainty. The expert system development process showed promising results even though updating the knowledge base with reliable new knowledge is required to improve the expert system performance in real time. Keywords Expert system · adaptive neuro-fuzzy inference system (ANFIS) · COGSYS KBS · flood forecasting I.D. Cluckie Water and Environmental Management Research Centre (WEMRC), Department of Civil Engineering, University of Bristol, Bristol, BS8 1UP, UK, e-mail: [email protected] A. Moghaddamnia Formerly of Water and Environmental Management Research Centre (WEMRC), Department of Civil Engineering, University of Bristol, Bristol, BS8 1UP, UK (currently at University of Zabol, Iran) D. Han Water and Environmental Management Research Centre (WEMRC), Department of Civil Engineering, University of Bristol, Bristol, BS8 1UP, UK
R.J. Abrahart et al. (eds.), Practical Hydroinformatics. Water Science c Springer-Verlag Berlin Heidelberg 2008 and Technology Library 68,
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15.1 Introduction In recent years, the utilisation of expert systems in real-time flood forecasting has been an issue of considerable importance. The real-time flood forecasting systems development process is essentially an experience-based process. An expert system is defined as a system that uses human knowledge captured in a computer to solve problems that ordinarily require human expertise (Turban and Aronson, 2001). Expert systems can represent that exper
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