  Data-driven flood forecasting methods are useful, especially for the rivers
that lack hydrological information to build physical models. Although these
former methods can forecast river stages using only past water levels and
rainfall data, they cannot handle previously unexperienced water levels easily,
and require a large amount of data to build accurate models. Here, we focus on
phase-space reconstruction approaches, and develop a practical data-driven
forecasting method to overcome the existing problems. The proposed method can
handle the unexperienced water levels and provide forecasts using only a small
number of water rise events. We apply the method to data from actual rivers,
and it achieved the best forecast performance among existing methods, including
a physical runoff model, a data-driven multi-layer perceptron, and a
conventional method based on phase-space reconstruction. In addition, the
proposed method also forecasted the exceedance of the evacuation warning level
6 h earlier for steep rivers. Given its performance and maintainability, the
proposed method can be applied to many actual rivers for early evacuation.
