Abstract
Floods belong to the most hazardous natural disasters and their disaster management heavily relies on precise forecasts. These forecasts are provided by physical models based on differential equations. However, these models do depend on unreliable inputs such as measurements or parameter estimations which causes undesirable inaccuracies. Thus, an appropriate data-mining analysis of the physical model and its precision based on features that determine distinct situations seems to be helpful in adjusting the physical model. An application of fuzzy GUHA method in flood peak prediction is presented. Measured water flow rate data from a system for flood predictions were used in order to mine fuzzy association rules expressed in natural language. The provided data was firstly extended by a generation of artificial variables (features). The resulting variables were later on translated into fuzzy GUHA tables with help of Evaluative Linguistic Expressions in order to mine associations. The found associations were interpreted as fuzzy IF-THEN rules and used jointly with the Perception-based Logical Deduction inference method to predict expected time shift of flow rate peaks forecasted by the given physical model. Results obtained from this adjusted model were statistically evaluated and the improvement in the forecasting accuracy was confirmed.
Highlights
Disaster management is generally becoming more and more important task
We attempted to deal with an adjustment of a physical model of water flow rate during floods with the help of linguistic associations mining
As any physical model based on differential equations is highly dependent on many unreliable parameters, it seems reasonable to perform some real data analysis that would inform us, when and under which conditions the model is time lagged or vice-versa too much ahead
Summary
Disaster management is generally becoming more and more important task. Among many natural disasters, floods are the one of the most hazardous, and one of the most frequently occurring in the region of the central Europe. The task is to build a model that would (based on the flow rate measurements and the Model-1D performance in the past) provide disaster management with a valuable information about possible horizontal imprecision of the Math-1D model and, that would provide the disaster management with an estimation about the peak shift. This peak shift estimation could be used in the corrections of the forecasts
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