DEPENDENCY ON NETWORK STRUCTURE AND INFORMATION DENSITY OF DEEP LEARNING BASED RIVER STAGE PREDICTION

DOI Web Site 5 References
  • TOKUDA Daisuke
    東京大学大学院 工学系研究科社会基盤学専攻
  • KOO Eunho
    株式会社エル・ティー・エス，東京大学生産技術研究所
  • KIM Hyungjun
    東京大学生産技術研究所

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  • 深層学習を用いた河川水位予測モデルにおけるネットワーク構造と入力データ量の依存性

Abstract

<p> A few of recent studies have applied deep learning technique for flood forecast. This study utilizes Recurrent Neural Network (RNN) with Gated Recurrent Units (GRUs) to hindcast the Kanto-Tohoku Flood in September 2015. Additionally, based on linear reservoir function, it applies Exponential Filtering (EF) as a preprocessor of input data to transform the statistical characteristics of input variable (i.e., rainfall) to of the target variable (i.e., river water stage). Compared with Feed Forward Network (FFN) model and RNN model without EF, the proposed model outperforms the flood events in Kinu river basin. In particular, it results reduced error for the highest water level which is 3.43 meter higher than of the highest level during the training period . Also, we investigate dependency of prediction skill on neural network structure and input data information density in Tone-river and Teshio-river basin, which shows critical number to predictability of water level and rainfall observation sites differs among target stations and basins.</p>

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