Výsledky vyhledávání - "Shunji Kotsuki"

Akademický článek

Application of a tuning-free burned area detection algorithm to the Chornobyl wildfires in 2022

Autor: Jun Hu, Yasunori Igarashi, Shunji Kotsuki, Ziping Yang, Mykola Talerko, Volodymyr Landin, Olha Tyshchenko, Mark Zheleznyak, Valentyn Protsak, Serhii Kirieiev

Publikováno v: Scientific Reports, Vol 13, Iss 1, Pp 1-6 (2023)

Abstract The wildfires in the Chornobyl Exclusion Zone (ChEZ) have caused widespread public concern about the potential risk of radiation exposure from radionuclides resuspended and redistributed due to the fires in 2020. The wildfires were also conf

Externí odkaz: https://doaj.org/article/6852d14e49a8415d9df03824b3ca4699

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Akademický článek

Employment of hydraulic model and social media data for flood hazard assessment in an urban city

Autor: Mao Ouyang, Shunji Kotsuki, Yuka Ito, Tomochika Tokunaga

Publikováno v: Journal of Hydrology: Regional Studies, Vol 44, Iss , Pp 101261- (2022)

Study regionAn urban city in the coastal region of East Japan.Study focusThis study implemented the hydraulic model to investigate the flood events in Mobara city, Japan. The model was calibrated by the flood on July 1, 1970, and then validated again

Externí odkaz: https://doaj.org/article/49c149c67e7e461d9330012c5532ad65

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Akademický článek

Regional-scale data assimilation with the Spatially Explicit Individual-based Dynamic Global Vegetation Model (SEIB-DGVM) over Siberia

Autor: Hazuki Arakida, Shunji Kotsuki, Shigenori Otsuka, Yohei Sawada, Takemasa Miyoshi

Publikováno v: Progress in Earth and Planetary Science, Vol 8, Iss 1, Pp 1-15 (2021)

Abstract This study examined the regional performance of a data assimilation (DA) system that couples the particle filter and the Spatially Explicit Individual-based Dynamic Global Vegetation Model (SEIB-DGVM). This DA system optimizes model paramete

Externí odkaz: https://doaj.org/article/ba04092e9d9b4d94b5833041a972f981

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Akademický článek

Oversampling Reflectivity Observations From a Geostationary Precipitation Radar Satellite: Impact on Typhoon Forecasts Within a Perfect Model OSSE Framework

Autor: James Taylor, Atsushi Okazaki, Takumi Honda, Shunji Kotsuki, Moeka Yamaji, Takuji Kubota, Riko Oki, Toshio Iguchi, Takemasa Miyoshi

Publikováno v: Journal of Advances in Modeling Earth Systems, Vol 13, Iss 7, Pp n/a-n/a (2021)

Abstract For the past two decades, precipitation radars (PR) onboard low‐orbiting satellites such as Tropical Rainfall Measuring Mission (TRMM) have provided invaluable insight into global precipitation variability and led to advancements in numeri

Externí odkaz: https://doaj.org/article/22ba9232a9f7425c9ea02d8bb4fca0c4

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Akademický článek

Applying the Sinkhorn Algorithm for Resampling of Local Particle Filter.

Autor: Ken Oishi¹, Shunji Kotsuki^2,3,4,5 shunji.kotsuki@chiba-u.jp

Publikováno v: SOLA. 2023, Vol. 19, p185-193. 9p.

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Emulating Rainfall-Runoff-Inundation Model using Deep Neural Network with Dimensionality Reduction

Autor: Masahiro Momoi, Shunji Kotsuki, Ryota Kikuchi, Satoshi Watanabe, Masafumi Yamada, Shiori Abe

Publikováno v: Artificial Intelligence for the Earth Systems. :1-25

Predicting the spatial distribution of maximum inundation depth (depth-MAP) is important for the mitigation of hydrological disasters induced by extreme precipitation. However, physics-based rainfall-runoff-inundation (RRI) models, which are used ope

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::b67ab306073ebdc8a49d3a797beb6b6e
https://doi.org/10.1175/aies-d-22-0036.1

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