Smart Dam: Upstream Sensing, Hydro-Blockchain, and Flood Feature Extractions for Dam Inflow Prediction

  • Takato YasunoEmail author
  • Akira Ishii
  • Masazumi Amakata
  • Junichiro Fujii
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1129)


Heavy rain occurs frequently during extreme weather, and the associated resultant flood damage represents a social problem. The present study aims to redefine the dam watershed as a smart dam and attempts to systematize the technology for flood prediction by integrating upstream sensing, dam inflow prediction, and a hydro-blockchain. In order to detect high water levels, we devise an upstream sensing method to observe the water level at the uppermost stream of a dam watershed, and summarize potential implementation hurdles. We also propose a hydro-blockchain configuration that provides a basis for the fair transaction of water rights. We implement a field study in the Kanto region, Japan, to observe the upstream water level using the devised water level sensor. We analyze the relationship between the measured water levels and the dam inflow and also analyze the response of the time difference. Furthermore, we propose a flood-feature extraction using a 20 year hydro-dataset of rainfall and water levels, and propose dam inflow prediction models using various time series machine learning algorithms. We demonstrate the application of our model results and discuss their usefulness.


River sensing Hydrologic blockchain Flood feature extraction Time series machine learning 



We thank Mr. Shinichi Kuramoto and Mr. Takuji Fukumoto (MathWorks Japan) for providing stable resources for machine and deep learning.


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Takato Yasuno
    • 1
    Email author
  • Akira Ishii
    • 1
  • Masazumi Amakata
    • 1
  • Junichiro Fujii
    • 1
  1. 1.Research Institute for Infra. Paradigm ShiftTokyoJapan

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