HTT09-P01

会場:コンベンションホール

時間:5 月 25 日 18:15-19:30

降雨強度データを用いた機械学習による簡易水害リスク評価手法の構築
Simple Evaluation Method for Flood Risk Using Rainfall Intensity Data and MachineLearning
佐藤 李菜 1∗ ; 小口 高 2
SATO, Rina1∗ ; OGUCHI, Takashi211東京大学大学院新領域創成科学研究科, 2 東京大学空間情報科学研究センター
Graduate School of Frontier Science, The University of Tokyo, 2 CSIS, The University of Tokyo

Floods especially pluvial flooding, is a major disaster in Japanese urban areas. Simulations are often used to assess flood risk,
but such approaches tend to be highly complicated. Therefore, some simple methods using topographical indices, land use andrainfall data with statistical approach have been proposed. However, the accuracy of such methods is still low. This study aims
to analyze the characteristics of flooded areas in the 23 wards of Tokyo and construct a simple method for evaluating flood risk
using rainfall intensity data and machine learning. Radar rainfall data from the Japan Meteorological Agency were analyzed
using Random Forest, a method of machine learning. The accuracy of the models constructed by Random Forest for flooded
areas is almost 100% in many districts, but the accuracy for non-flooded areas are low. It means that the models can well predictflood occurrence, but many non-flooded areas are estimated as flooded areas. Therefore, it is necessary to improve the model.

キーワード: 水害, 降雨強度, 機械学習, GIS
Keywords: flood, rainfall inrensity, machine learning, GIS

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HTT09-P02

会場:コンベンションホール

時間:5 月 25 日 18:15-19:30

航空機 LiDAR 点群データによる山地河川の粗度の計測
Survey on grain roughness in a mountain river by using airborne LiDAR point cloud data
蒋 香旭 1 ; 清水 武志 1∗ ; 藤村 直樹 1 ; 石塚 忠範 1
JIANG, Xiangxu1 ; SHIMIZU, Takeshi1∗ ; FUJIMURA, Naoki1 ; ISHIZUKA, Tadanori1
11

国立研究開発法人 土木研究所
Public Works Research Institute

Small-scale topographic features are commonly found on a mountain river. One of the important features is grain roughness for
affecting the water flow or debris flow resistance of the mountain channel. One of the difficulties is to measure grain roughness
in a mountain river because a lot of grains and rocks are larger than a few meter diameter.
In this study, we use a point-cloud data earned by airborne LiDAR to calculate simple statistical indexes of the roughness
in the lattice bins covered with the objective region of the mountain river. The results comparative with the in-situ grain size
distribution earned by the grid count method show relatively good relationship.

キーワード: 山地河川, 粗度, 航空機 LiDAR
Keywords: mountain river, grain roughness, airborne LiDAR

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HTT09-P03

会場:コンベンションホール

時間:5 月 25 日 18:15-19:30

The annual net ecosystem exchange in a subtropical broadleaf plantation in southern Taiwan.
The annual net ecosystem exchange in a subtropical broadleaf plantation in southern Taiwan.
LAI, Yenjen1∗ ; YU, Jui-chu1 ; CHANG, Cheng-sheng1 ; CHEN, Jiquan2 ; TSAI, Ming-jer3 ; WANG, Ya-nan3
LAI, Yenjen1∗ ; YU, Jui-chu1 ; CHANG, Cheng-sheng1 ; CHEN, Jiquan2 ; TSAI, Ming-jer3 ; WANG, Ya-nan3
1

The Experimental Forest, National Taiwan University, Taiwan, 2 IceMe, Nanjing University of Information Science & Technology, China; and University of Toledo, USA, 3 School of Forestry and Resource Conservation, National Taiwan University, Taiwan
1The Experimental Forest, National Taiwan University, Taiwan, 2 IceMe, Nanjing University of Information Science & Technology, China; and University of Toledo, USA, 3 School of Forestry and Resource Conservation, National Taiwan University, Taiwan
Carbon flux of regenerating vegetation is considered as a major factor in determining the sequestration strength and associateduncertainty of terrestrial ecosystems and its role in slowing down the increase of atmospheric CO2 concentration. In the purposeto quantify the long-term net exchanges of carbon, water, and energy between the broadleaf plantation and the atmosphere, thisstudy picked an existed reforested sugar cane farm which was reforested by multiple broadleaf species from 2002 to 2005 inPingdong, southern Taiwan as a field site and installed an open-path eddy-covariance tower since 2008. The tower-based annual
net ecosystem exchange (NEE) were -1.0, 1.3, 0.5, and -1.0 Mg C ha−1 yr−1 for 2009-2012, respectively. However, these results were conflicted with biometric investigation. A further study using Hsieh’s footprint model (Hsieh et al., 2000) combinedwith geographic information system (GIS) showed most of the flux sources were within the range of 250 m and monsoon andperiodic winds existed clearly. The tower-based NEE could be mis-explained because the daytime and nighttime flux sources
actually came from different plantations/species. The tower-based Re (ecosystem respiration) was over-estimated about 0.56
umol m−2 s−1 . The annual mean NEE in plot no.10 and no.11 was -3.12, -0.87, -1.58, -3.09 Mg C ha−1 yr−1 for 2009 to 2012,
respectively after a simple revision.
キーワード: carbon dioxide flux, eddy covariance, net ecosystem exchange (NEE), plantations of multiple broadleaf species,
footprint
Keywords: carbon dioxide flux, eddy covariance, net ecosystem exchange (NEE), plantations of multiple broadleaf species,
footprint

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