APPLICABILITY OF RECURRENT NEURAL NETWORK TO PREDICT FIELD MEASUREMENT DATA OF VOLUMETRIC WATER CONTENT

• ITO Shinichi

  鹿児島大学　学術研究院理工学域工学系

• ODA Kazuhiro

  大阪産業大学　工学部都市創造工学科

• KOIZUMI Keigo

  大阪大学　大学院工学研究科

• SAKO Kazunari

  鹿児島大学　学術研究院理工学域工学系

Bibliographic Information

Other Title

• 体積含水率の現地計測データの予測に対するリカレントニューラルネットワークの適用性

Abstract

It is important to identify the model that can simulate the field measurement data of soil moisture conditions to predict the occurrence of landslide disasters due to heavy rain. This study verified the applicability of the recurrent neural network to predict the field measurement data of volumetric water content. The recurrent neural network model was estimated by using the training data at the time of weak rain, and the estimated model simulated the test data at the time of heavy rain with enough accuracy. The simulation results led to the conclusion that the recurrent neural network was an effective method to predict the field measurement data of volumetric water content.

Journal

• Intelligence, Informatics and Infrastructure

  Intelligence, Informatics and Infrastructure 1 (J1), 445-452, 2020-11-11

  Japan Society of Civil Engineers

Keywords

• recurrent neural network
• field measurement data
• volumetric water content

Details

• CRID: 1390849376476766336
• NII Article ID: 130007940760
• DOI: 10.11532/jsceiii.1.j1_445
• ISSN: 24359262
• Text Lang: ja
• Data Source:
  • JaLC
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed