Premium
Evaluation of the benefits of using a backward chaining decision support expert system for local flood forecasting and warning
Author(s) -
Zhang Xiaoyin,
Moynihan Gary P.,
Ernest Andrew N. S.,
Gutenson Joseph L.
Publication year - 2018
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/exsy.12261
Subject(s) - computer science , expert system , flood myth , warning system , flood forecasting , decision support system , knowledge base , flood warning , operations research , risk analysis (engineering) , data mining , artificial intelligence , business , telecommunications , philosophy , theology , engineering
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.