EVALUATION OF DRIFTWOOD GENERATION IN THE NORTHERN KYUSHU HEAVY RAIN IN 2017 BY LOGISTIC REGRESSION
Takahiro SHOGAKI, Akiyoshi TSUSUE,and Shinichiro YANO (Kyushu University), Kiyonobu KASAMA(Tokyo Institute of Technology, Japan)

Introduction

Results

Recently, historical recorded heavy rain disasters
have occurred in Japan, such as, the West Japan Heavy
Rain in 2018, the Typhoon Hagibis in 2019, and theNorthern Kyushu Heavy Rain in 2017. These heavy
rains may be caused by the progress of global warming.
In these events, not only inundation, but also
deposition of sediments and driftwoods were widely
seen in the devastated area (Fig.1). Thus, river Figure 1. A large amount of driftwood anddamage to private houses
manager requires to evaluate a risk of floods with(The Northern Kyushu Heavy Rain in 2017)sediments and driftwoods in the heavy rain event.

Table 1. Examples of the combination of Xi

In this research, we tried to develop a new model toestimate a possibility of slope failure as a source ofdriftwood generation in a given watershed area. The
dataset of slope failure area in the Northern Kyushu
Heavy Rain in 2017 was used for the development,
because a lot of the simultaneous generation were seenin the event. The logistic regression model was applied
as a multivariable statistical way to express thepossibility of slope failure generation under a given
Figure 2. The river basins as a survey target
precipitation condition.

Model(1) Collect and create factor datafor 30m mesh

(1) The logistic regression model(2) Create a logistic model for eachcase

𝟏𝐞𝐱 𝐩( 𝒛𝑷 𝒛 ==𝟏 + 𝐞𝐱 𝐩( − 𝒛
𝟏 + 𝐞𝐱 𝐩( 𝒛𝒛 = 𝜷𝟎 + 𝜷𝟏 𝑿𝟏 + 𝜷𝟐 𝑿𝟐 + ⋯ + 𝜷𝒏 𝑿𝒏(2) The formula to calculate the amount of driftwood𝑽 = 𝜷𝒘 𝑨𝒔𝒇(3) Calculate and illustrate slope
failure probability for each 30m
mesh and each mountain stream
Create a scatter plot with actual
collapsed land area ratio(5) From the simple regression
equation, calculate and illustrateof predicted collapsed land area
ratio

★slope failure areas was3,659 meshes★not including them was209,257 meshes(6) Calculate predicted andactual collapse area
(7) Calculate predicted andactual driftwood generationfrom the formulas)(8) Compare the amount ofdriftwood generation andcheck model validity

Figure 3. The research process(1) P(z) is the possibility of the objective variable z, bi is the regression coefficient, and Xi are explanatory
variables. These factors of 𝑋𝑖 are the geographical data. In application of the logistic regression model, both
numbers of the meshes should be equal. Therefore, 3,659 non-collapsed meshes were selected by random
sampling. 20 trials of the random selection were performed to reduce a bias for them. It was confirmed that there
was no bias in the random sampling. In this research, we choose 38 combinations for the explanatory variables.
(2) 𝑉 is the amount of driftwood generation in each river, bw is the volume of standing trees per unit area, and Asf isthe surface area of slope failure. In the present research, bw =54,900 m3/km2 is adapted.

Figure 4. Map of actual slope failure area ratio

Figure 5. Map of estimated slope failure area ratio
in the optimal case (case24)✓ The case 24 can reproduce the actual results mostprecisely. Additionally, other cases with high reproducibility
were chosen. These cases included the maximum
accumulative precipitation for 3 hrs or/and 6 hrs.
✓Figure 6 shows a comparison between the estimation ofdriftwood generation in each river by the model and theactual generation by MLIT. These estimation shows result
within accuracy of 20%.
✓ However, in the case including multi precipitation
variables like case 24, some regression coefficient for
accumulative precipitation shows negative value such as 3
hours and 12 hours accumulative precipitation in the case 24(Table 1). It may be caused by the multicollinearity amongFigure 6. Comparison between estimation and actualaccumulative precipitation.
record for driftwood generation for each river

Conclusions✓As a result, it was succeeded to reproduce the slope failure and driftwood generation by logistic regression analysis in the Northern Kyushu Heavy Rain in 2017.
✓In further research, we will confirm the proposed model applicability by adapting it to other big flood events, e.g., heavy rain by the Typhoon 1919.
✓In addition, we will study how to incorporate parameters that take into account the soil rainfall index and the rainfall multicollinearity.

Reference
Ministry of Land, Infrastructure, Transport and Tourism (MLIT) and Fukuoka
Prefecture. (2018). Report of the Committee for a policy of restoration in theRight Bank Area of the Middle Chikugo River.
Yano, S., Okubo, R., Tsusue, A., Takemura, D., Tomita, K., Kasama, K., Nihei, Y.
(2018). Analysis on the causes of the Northern Kyushu heavy rain in 2017,
Journal of JSCE, B1, 74(5), I_1063-I_1068.
Ministry of Agriculture, Forestry and Fisheries. (2012). Guide for debris flow anddriftwood measures.