Abstract
Probabilistic flood forecasting, which provides uncertain information in the forecasting of floods, is practical and informative for implementing flood-mitigation countermeasures. This study adopted various machine learning methods, including support vector regression (SVR), a fuzzy inference model (FIM), and the k-nearest neighbors (k-NN) method, to establish a probabilistic forecasting model. The probabilistic forecasting method is a combination of a deterministic forecast produced using SVR and a probability distribution of forecast errors determined by the FIM and k-NN method. This study proposed an FIM with a modified defuzzification scheme to transform the FIM’s output into a probability distribution, and k-NN was employed to refine the probability distribution. The probabilistic forecasting model was applied to forecast flash floods with lead times of 1–3 hours in Yilan River, Taiwan. Validation results revealed the deterministic forecasting to be accurate, and the probabilistic forecasting was promising in view of a forecasted hydrograph and quantitative assessment concerning the confidence level.
Highlights
A real-time flood forecasting model is an essential nonstructural component in a flood warning system
This study proposed a fuzzy inference model (FIM) with a modified defuzzification scheme to deduce the probability distribution of forecast errors
The probabilistic flood-stage forecasting results were obtained by adding the probability
Summary
A real-time flood forecasting model is an essential nonstructural component in a flood warning system. The present study adopted a method that combines deterministic forecasts and the probability distribution of forecast errors to produce probabilistic forecasts [1]. Such a method was adopted because forecast-error data can quantify the total uncertainty of forecasting. This study applied multiple machine learning methods to achieve probabilistic flood-stage forecasting. Combining deterministic flood-stage forecasting and the probability distribution of forecast errors yielded probabilistic flood-stage forecasts. Validation results regarding actual flash flood events proved the capability of the proposed model in view of the forecasted hydrograph and a quantitative assessment concerning the confidence level.
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