Research Trends and Results
Technical Study for Improving the Accuracy of Flood Forecasts--- Application of Particle Filter --KAWASAKI Masaki, Head; INOMATA Hironori, ResearcherWater Cycle Division, River Department
Keywords: flood forecasting system, actual measurement adjustment approach, granular filter
Figure 1: Forecast of One-Hour-After Discharge
at B Point of River "A" Where Particle Filter isApplied

2. Outline of Particle Filter
A particle filter generates multiple random quantities
as statuses that are corrected in real-time (each random
quantity is called a "particle"), and conducts simulationswith the flood forecasting model for each of the particles
to select the particles of high conformity between thecalculation result and observed value.
With thisapproach, the best parameters can be estimated inreal-time for each flood.

4. Conclusion and issues
As described, the particle filter estimates the optimal
parameters for each flood through a process of repeating
the calculations of the flood forecasting model the same
number of times as the number of particles. It can beinstalled without substantially altering the flood
forecasting model already adopted on site and isadvantageous towards improving the accuracy offorecasts. For real-time operation, it is necessary to
study the required calculation time and physical validity
concerning temporal changes in the quantity of status. It
is expected that particle filters will be widely used in
on-site flood forecasting systems.

3. Application example
Figure 1 compares the results of calculations using and
not using the particle filter to forecast the discharge onehour after at the flood control point (B point). In thisexample, the roughness coefficient of the model is usedas the target of sequential correction by the particle filter.
As a result, the accuracy was higher in the case where theparticle filter was applied than the case where not.
Figure 2 shows the temporal changes in the optimal
roughness coefficient estimated by the particle filter.
Clearly, the particle filter enhances the forecasting
accuracy of the flood forecasting model.

Flow rate (m3/s)

1. Introduction
Under Article 10 of the Flood Control Law, if a flood
is likely to occur in a river under the direct control of thegovernment or other large river, JMA and MLIT are
jointly required to disseminate forecast information onwater level, etc. from a base station ("flood forecast") tothe prefectural governor and the public. It is, therefore,
necessary to provide highly accurate flood forecasts to
support flood fighting activities and minimize damage.
However, the accuracy of flood forecasting models used
for the rivers under the direct government control in
direct control rivers is by no means adequate.
At present, it is difficult to determine a flood
forecasting model with a parameter set that can calculate
all floods to a satisfactory degree of accuracy. Given thecircumstances, it is important to use real-time observation
data on discharge, etc. to the extent possible, to estimate
the best parameters in real-time and maintain the flood
forecasting model suitable for the flood then likely tooccur. (This process is called "real-time adjustment".)
This paper introduces a flood forecasting calculation
using particle filter, which is a real-time adjustment
method based on the statistical theory and has often beenused in recent years in the river management field.

Actual measurement flow rate
Result of calculation without granular filter
Result of calculation with granular filter
Forecast of one-hour-after (Average of allgrains)

Roughness coefficient(Correction magnification)

Figure 2: Temporal Changes in Roughness
Coefficient