The solution leverages Fujitsu Human Centric AI Zinrai, a comprehensive portfolio that encompasses Fujitsu’s wide range of AI technologies and techniques, and utilizes a model that incorporates insights from hydrology to produce an AI that achieves predictions with greater precision.
In recent years, local governments across Japan have grappled with the challenges of managing rivers that cause serious flood damage in the wake of frequent, highly localized heavy rain events. Smaller rivers flowing through urban areas in particular often experience sudden water level rises. This is due to the impact of unpredictable, yet powerful rainstorms and typhoons.
Year after year, the risk of significant flood damage occurring very rapidly represents a sporadic, yet increasingly severe threat. This data underlines an urgent need for enhanced flooding countermeasures.
As part of these countermeasures, water level predictions have been conducted for large rivers designated as at risk of flooding. For smaller rivers or areas where water level sensors are relatively new, however, making accurate predictions has proven difficult. This is due to a lack of sufficient water level data and the latest flow rate observation results.
To address this, Fujitsu has developed a new technology that accurately predicts water levels even for rivers with limited measurement data. This consequently empowers disaster prevention personnel to take early preemptive action to mitigate damage.
Fujitsu has developed a mathematical model that can find optimal parameters using rainfall and water level data to train it. The model creates functions based on the tank model concept, which expresses water discharge from a river basin within hydrology.
Using this model, the AI predicts future water levels based on data collected to present on rainfall and water level data along with forecasts for the next several hours transmitted to local governments by various meteorological organizations.
Researchers can also optimize the prediction model very quickly; even following changes in the river environment or the introduction of new infrastructure. In cases like this, researchers can retrain the model using rainfall and water level data taken after any changes.