دانلود رایگان مقاله انگلیسی استفاده از سیستم تخصصی پشتیبانی تصمیم گیری زنجیره عقب مانده در پیش بینی سیل محلی - وایلی 2018

Abstract

Nationwide flood forecasting and warning are available through mass media. However, running the complex numerical models requires enormous computational resources. In addition, the comparatively low accuracy of prediction for a certain region such as a small town, a community, or a single house, causes false alarms and improper responses and thus the unnecessary loss of property and/or life. One potential solution to advance forecast accuracy without occupying substantial computational resources is to develop a stand‐alone local flood forecasting and warning expert system incorporating sufficient data on local hydraulic and hydrological factors and local historical experience. To date, there has been a limited amount of work developing expert systems in this area. In this paper, we discuss the development and implementation of an expert system for local flood forecasting and warning. With its extensible knowledge base combined with the information provided by users, this expert system provides reasoning routines and forecasting on the flood warning stages, possible consequences, and recommendations for community managers, landowners, or the public in general.

7 | CONCLUSIONS

To break through the bottleneck of knowledge acquisition in local flood forecasting and warning, we identified the knowledge necessary to LFFWS and assimilated these dynamic and static knowledge primitives into the knowledge base. The case study illustrates that the collected knowledge works successfully to initialize the system and provide flood forecasting and warning messages on current or proposed data of rainfall, stream flows, and local conditions.

In addition, this study shows that, to provide local flood forecasting and warning, developing an expert system is more effective than procedural code. The advantages of rapid development and easy maintenance stem from the system architecture of expert systems. The explicit knowledge base and packaged expert system shell ensure that (a) new facts, rules, questions, and goals can be easily added to the extensible knowledge base by domain experts, such as environmental engineers or even skilled users to adapt to more scenarios; (b) all goals can be skipped or proved in various sequences automatically (forward chaining) or according to users' demands (backward chaining); (c) partially developed expert systems can be functional; (d) logic or syntax errors and outdated data can be rapidly identified and corrected; and (e) the users can understand and learn the reasoning simultaneously when they obtain the reports.

Furthermore, the backward chaining method is shown to work more effectively than forward chaining to satisfy local flood managers' evolving demands (referred to as goals) and growing new information on LFFWS. Backward chaining enables the inference engine of expert systems to work with incomplete information at the beginning and to keep running as more and more data become available. With a backward chaining inference engine, our expert system can quickly figure out and optimally collect the necessary data from previous analysis results or user interviews based on the evolving goals and efficiently develop reports and recommendations with the growing information at hand. In the meantime, without sacrificing convenience for skilled users, to ease the difficulty of goal selection and shorten the practice for common users, an ultimate goal (Gfore) is set up to acquire a complete picture of the current or proposed situation and issue all likely forecasting and warning messages.