Publication of Paper on “Less Is More: Short-Term Window Calibration Improves Seasonal Shoreline Prediction in Modeling”

A collaborative research team consisting of Professor Nobuhito Mori of the Disaster Prevention Research Institute (DPRI), Kyoto University;  Senior Researcher Xinyu Chen, and Group Leader Masayuki Banno, both from the Port and Airport Research Institute, Yokosuka, Japan has announced a groundbreaking method for predicting seasonal changes in shorelines.

It demonstrates that training with short-term data (just two years) significantly improves prediction accuracy compared to the conventional approach of optimizing numerical models using long-term data. The “finding demonstrate that short-window calibration substantially enhances model capability for capturing wave-driven seasonal shoreline changes, offering a practical solution for coastal risk assessment using limited observational data. This approach is particularly valuable given increasing availability of satellite-derived shoreline data and the need for accurate seasonal predictions under changing climate conditions.” (excerpt from Abstract)

Click below to review the full article:

• Less Is More: Short-Term Window Calibration Improves Seasonal Shoreline Prediction in Modeling

The results of this research were published in the international academic journal “Geophysical Research Letters” on August 28, 2025.