Evaluation of driftwood generation in the Northern Kyushu heavy rain in 2017 by logistic regression

Takahiro Shogaki; Akiyoshi Tsusue; Shinichiro Yano; Kiyonobu Kasama

Evaluation of driftwood generation in the Northern Kyushu heavy rain in 2017 by logistic regression

Takahiro Shogaki, Akiyoshi Tsusue, Shinichiro Yano, Kiyonobu Kasama

Laboratory of Hydrosphere Sutainability Engineering

Research output: Contribution to conference › Paper › peer-review

Abstract

We tried to develop a model to calculate the amount of driftwood generation from a given watershed area in a specific heavy rain event. In this research, slope failure dataset in the Northern Kyushu Heavy Rain in 2017 was used for the logistic regression model to estimate the possibility of slope failure generation. In this model, C-X band radar precipitation, geographical data, geological data, and land use data were adapted. As a result of the present research, it is found that the best model can express the slope failure in the heavy rain with very high accuracy. In addition, the model can estimate the driftwood generation from 10 rivers with 20% of accuracy the model.

Original language	English
Publication status	Published - 2020
Event	22nd Congress of the International Association for Hydro-Environment Engineering and Research-Asia Pacific Division: Creating Resilience to Water-Related Challenges, IAHR-APD 2020 - Sapporo, Virtual, Japan Duration: Sept 14 2020 → Sept 17 2020

Conference

Conference	22nd Congress of the International Association for Hydro-Environment Engineering and Research-Asia Pacific Division: Creating Resilience to Water-Related Challenges, IAHR-APD 2020
Country/Territory	Japan
City	Sapporo, Virtual
Period	9/14/20 → 9/17/20

All Science Journal Classification (ASJC) codes

Ecology
Environmental Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Cite this

Shogaki, T., Tsusue, A., Yano, S., & Kasama, K. (2020). Evaluation of driftwood generation in the Northern Kyushu heavy rain in 2017 by logistic regression. Paper presented at 22nd Congress of the International Association for Hydro-Environment Engineering and Research-Asia Pacific Division: Creating Resilience to Water-Related Challenges, IAHR-APD 2020, Sapporo, Virtual, Japan.

Evaluation of driftwood generation in the Northern Kyushu heavy rain in 2017 by logistic regression. / Shogaki, Takahiro; Tsusue, Akiyoshi; Yano, Shinichiro et al.
2020. Paper presented at 22nd Congress of the International Association for Hydro-Environment Engineering and Research-Asia Pacific Division: Creating Resilience to Water-Related Challenges, IAHR-APD 2020, Sapporo, Virtual, Japan.

Research output: Contribution to conference › Paper › peer-review

Shogaki, T, Tsusue, A, Yano, S & Kasama, K 2020, 'Evaluation of driftwood generation in the Northern Kyushu heavy rain in 2017 by logistic regression', Paper presented at 22nd Congress of the International Association for Hydro-Environment Engineering and Research-Asia Pacific Division: Creating Resilience to Water-Related Challenges, IAHR-APD 2020, Sapporo, Virtual, Japan, 9/14/20 - 9/17/20.

Shogaki T, Tsusue A, Yano S , Kasama K. Evaluation of driftwood generation in the Northern Kyushu heavy rain in 2017 by logistic regression. 2020. Paper presented at 22nd Congress of the International Association for Hydro-Environment Engineering and Research-Asia Pacific Division: Creating Resilience to Water-Related Challenges, IAHR-APD 2020, Sapporo, Virtual, Japan.

Shogaki, Takahiro ; Tsusue, Akiyoshi ; Yano, Shinichiro et al. / Evaluation of driftwood generation in the Northern Kyushu heavy rain in 2017 by logistic regression. Paper presented at 22nd Congress of the International Association for Hydro-Environment Engineering and Research-Asia Pacific Division: Creating Resilience to Water-Related Challenges, IAHR-APD 2020, Sapporo, Virtual, Japan.

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abstract = "We tried to develop a model to calculate the amount of driftwood generation from a given watershed area in a specific heavy rain event. In this research, slope failure dataset in the Northern Kyushu Heavy Rain in 2017 was used for the logistic regression model to estimate the possibility of slope failure generation. In this model, C-X band radar precipitation, geographical data, geological data, and land use data were adapted. As a result of the present research, it is found that the best model can express the slope failure in the heavy rain with very high accuracy. In addition, the model can estimate the driftwood generation from 10 rivers with 20% of accuracy the model.",

author = "Takahiro Shogaki and Akiyoshi Tsusue and Shinichiro Yano and Kiyonobu Kasama",

note = "Funding Information: This research was supported by JSPS KAKENHI, Grant-in-Aid for Scientific Research (A) (19H00812), the River Fund of the River Foundation, Japan in 2018 and 2019, the Research Fund of the Foundation of River & basin Integrated Communications (FRICS) in 2018, and the Social Implementation Program on Climate Change Adaptation Technology (SI-CAT) of MEXT, Japan. Publisher Copyright: {\textcopyright} 2020 22nd Congress of the International Association for Hydro-Environment Engineering and Research-Asia Pacific Division, IAHR-APD 2020: {"}Creating Resilience to Water-Related Challenges{"}. All rights reserved.; 22nd Congress of the International Association for Hydro-Environment Engineering and Research-Asia Pacific Division: Creating Resilience to Water-Related Challenges, IAHR-APD 2020 ; Conference date: 14-09-2020 Through 17-09-2020",

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N1 - Funding Information: This research was supported by JSPS KAKENHI, Grant-in-Aid for Scientific Research (A) (19H00812), the River Fund of the River Foundation, Japan in 2018 and 2019, the Research Fund of the Foundation of River & basin Integrated Communications (FRICS) in 2018, and the Social Implementation Program on Climate Change Adaptation Technology (SI-CAT) of MEXT, Japan. Publisher Copyright: © 2020 22nd Congress of the International Association for Hydro-Environment Engineering and Research-Asia Pacific Division, IAHR-APD 2020: "Creating Resilience to Water-Related Challenges". All rights reserved.

PY - 2020

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N2 - We tried to develop a model to calculate the amount of driftwood generation from a given watershed area in a specific heavy rain event. In this research, slope failure dataset in the Northern Kyushu Heavy Rain in 2017 was used for the logistic regression model to estimate the possibility of slope failure generation. In this model, C-X band radar precipitation, geographical data, geological data, and land use data were adapted. As a result of the present research, it is found that the best model can express the slope failure in the heavy rain with very high accuracy. In addition, the model can estimate the driftwood generation from 10 rivers with 20% of accuracy the model.

AB - We tried to develop a model to calculate the amount of driftwood generation from a given watershed area in a specific heavy rain event. In this research, slope failure dataset in the Northern Kyushu Heavy Rain in 2017 was used for the logistic regression model to estimate the possibility of slope failure generation. In this model, C-X band radar precipitation, geographical data, geological data, and land use data were adapted. As a result of the present research, it is found that the best model can express the slope failure in the heavy rain with very high accuracy. In addition, the model can estimate the driftwood generation from 10 rivers with 20% of accuracy the model.

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Evaluation of driftwood generation in the Northern Kyushu heavy rain in 2017 by logistic regression

Abstract

Conference

All Science Journal Classification (ASJC) codes

UN SDGs

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