結合隨機森林與自組織映射於淹水災害分區評估

黃韻如

doi:10.6342/NTU201902265

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結合隨機森林與自組織映射於淹水災害分區評估

Assessment of flood hazard zoning using an integrated approach of random forest and self-organizing map

黃韻如 , Masters　　Advisor：林國峰　　

繁體中文 DOI： 10.6342/NTU201902265

淹水災害分區模式；淹水潛勢圖；隨機森林；自適應增強；自組織映射； Flood hazard zoning model ； Flood susceptibility map ； Random forest ； Adaptive boosting ； Self-organizing map

Abstract │ Reference (42) │ Altmetrics

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Taiwan is located on the main track of western Pacific typhoons, and approximately three to four typhoons hit Taiwan per year. The heavy rainfall frequently results in severe flooding in downstream areas. Moreover, it is accepted that the number of typhoons which attack Taiwan will decrease, the proportion of strong typhoons and days of extreme rainfall will increase considerably in recent years due to climate change. This changes have increased the chances of rainfall-induced flooding. Flood cause hung losses of human life, property, and devastations to environment. Therefore, the flood hazard zoning model is an important tool for flood prevention and can efficiently mitigate the disasters.
In this study, a flood hazard zoning model based on machine learning and cluster algorithm is proposed. The model contains two steps. First, two kinds of machine learning, random forest (RF) and adaptive boosting (AdaBoost), are employed to construct flood susceptibility models to yield flood susceptibility values, respectively. Second, the flood susceptibility values are then input to self-organizing map (SOM) to obtain the flood hazard zones. In addition, two different inputs for SOM are considered: (i) only the flood susceptibility value of the grid itself is used as input, and (ii) flood susceptibility values of the self and surrounding grids are used as inputs. After construct all flood hazard zoning models, decide the proposed models which using the better inputs for SOM in the RF based models and AdaBoost based models. For comparison with the proposed models, the traditional models based on the natural break (NB) are also constructed.
Ten villages at Yilan County in northeastern Taiwan are selected as the study area. All the flood-related factors and the historical flooding data are based on GIS technology. Flood events from 2004 to 2015 are collected. Moreover, twelve flood-related factors are used in this study, namely elevation, slope, aspect, curvature, plan curvature, profile curvature, topographic wetness index, stream power index, distance to river, distance to drainage, land use and maximum hour rainfall.
The results confirm that the proposed models with the flood susceptibility values of the self and surrounding grids does improve the assessment performance. The proposed models are better performed than the traditional models. Furthermore, the proposed model, based on the RF integrated with the SOM using the flood susceptibility values of the self and surrounding grids as input, yields the most reasonable flood hazard zoning results. Finally, regarding the relation of factors and flood hazard zoning, the land use, distance to drainage, elevation, slope and the maximum 1-h rainfall have great influence on flood hazard zoning. The proposed flood hazard zoning model is expected to be useful to support the formulation of adequate disaster mitigation strategies.

Reference ( 42 ) 〈TOP〉

1. Abella, E. A. C., Van Westen, C. J., 2008. Qualitative landslide susceptibility assessment by multicriteria analysis: a case study from San Antonio del Sur, Guantánamo, Cuba. Geomorphology 94(3-4), 453-466.
2. Alfieri, L., Salamon, P., Bianchi, A., Neal, J., Bates, P., Feyen, L., 2014. Advances in pan‐European flood hazard mapping. Hydrological processes 28(13), 4067-4077.
3. Al-Abadi, A. M., 2018. Mapping flood susceptibility in an arid region of southern Iraq using ensemble machine learning classifiers: a comparative study. Arabian Journal of Geosciences 11(9), 218.
4. Bui, D. T., Tuan, T. A., Klempe, H., Pradhan, B., Revhaug, I., 2016. Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides 13(2), 361-378.
5. Breiman, L., Friedman, J.H., Olsen, R.A., Stone, C.J., 1984. Classification and regression trees. Wadsworth and Brooks, New York, USA.

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