探討強風是否為崩塌致災因子與建立崩塌機器學習模型

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謝芮云(Jui-Yun Hsieh) 查詢紙本館藏

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土木工程學系

論文名稱

探討強風是否為崩塌致災因子與建立崩塌機器學習模型
(Investigating Strong Winds as a Risk Factor for Landslides and Establishing a Landslide Machine Learning Model)

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至系統瀏覽論文 (2026-6-28以後開放)

摘要(中)

台灣因天氣型態與地質地形條件，導致常有坡地災害的發生，造成人員大量傷亡以及財產損失。傳統上，針對由颱風引發崩塌的研究主要探討的致災因子包含強降雨、地質和地形條件，而強降雨已被證明是最重要的因子之一。然而，颱風常伴隨著瞬間強風，並劇烈搖晃樹木導致土壤受到擾動，降低了坡地的穩定性。此外，有部分崩塌事件發生於有樹木覆蓋的邊坡上，且只受颱風帶來強風的影響，而非特別受到強降雨的影響。因此本研究採用資料驅動(data-driven)的方式，將雨和風同時納入探討，以另一個角度證明颱風帶來的強風是不可忽視的崩塌致災因子，特別是持續數小時的強風。
我們透過三維直方圖和曼-惠特尼U檢驗，探討了結合強風和降雨對崩塌的影響。結果顯示，崩塌事件發生時的風雨指標皆顯著高於未發生崩塌事件時，且隨著強風持續時間增加，崩塌發生的機率亦會增加。另外，我們建立了機器學習隨機森林模型，將強降雨、強風、地質和地形條件等因素作為訓練特徵變數進行崩塌預測，結果表明將強風納入考量可以提高預測準確度，因此，在颱風侵襲之下，除了強降雨，強風亦是提高或引發坡地災害的致災因子之一，在考量由颱風誘發的崩塌地時，若忽略強風的影響可能導致嚴重的損失。

摘要(英)

Landslides, which result in numerous casualties and significant property losses, are a major natural disaster in Taiwan. Traditional landslide studies focused on heavy rainfall, geological condition and topographical condition as trigger factors, often overlooking the impact of strong winds. However, typhoons often bring intense wind gusts that can severely sway trees, leading to the soil disturbance which decreasing the slope stability. Additionally, some landslide events occurred on broad-leaved forest along the slopes where were primarily affected by strong winds of the typhoon rather than its heavy rainfall.

We examined the significance of the combined rain-wind influence on landslides by Three-dimensional (3D) Histogram and Mann-Whitney U test. The Mann-Whitney U test results reveal that wind and rain conditions are both significant differences at a significant level of P≤0.001 between typhoon-induced landslide events and non-landslide events. And 3D Histogram results demonstrate a positive skewed distribution similar to the rainfall direction in strong wind duration axis, which implies that the probability of landslides occurring increased with an increase in the duration of strong wind. Furthermore, we construct machine learning Random Forest models based on factors, such as heavy rain, strong wind, traditional geological conditions, and topographical factors. The model that includes strong wind factors shows better accuracy than the model that does not include strong wind factors. Therefore, apart from heavy rainfall, strong wind is also one of the important factors that may increase or trigger the risk of landslides. Ignoring the effect of strong wind when investigating typhoon-induced landslides might lead to severe damage.

關鍵字(中)

★ 風雨效應
★ 強風
★ 崩塌
★ 颱風
★ 隨機森林

關鍵字(英)

★ Wind and Rain Effect
★ Strong Wind
★ Landslide
★ Typhoon
★ Random Forest

論文目次

摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vii
表目錄 ix
第一章緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 論文架構 4
第二章文獻回顧 5
2.1 颱風強度與災害情形 5
2.2 崩塌與降雨之間的力學關係 6
2.3 樹與颱風與崩塌之間的關係 9
2.4 遙測影像於崩塌地之相關研究 11
2.4.1 遙測影像結合深度學習與機器學習之應用 13
2.5 機器學習與深度學習於崩塌事件上的應用 14
2.5.1 隨機森林 15
第三章研究方法 16
3.1 研究架構 16
3.2 資料蒐集與描述 19
3.2.1 颱風基本資料 19
3.2.2 氣象觀測資料 21
3.2.3 坡地災害資料 24
3.2.4 網格高程數值模型(DEM) 25
3.2.5 資料前處理步驟 25
3.3 颱風風雨指標建立 27
3.3.1 颱風風雨指標空間推估 27
3.4 統計顯著性差異檢定 30
3.4.1 曼-惠特尼U檢定(Mann-Whitney U-test) 31
3.5 聚類分析(Cluster Analysis) – k-means 32
3.5.1 肘部法(Elbow method) 34
3.5.2 輪廓係數分析法(Silhouette method) 35
3.6 颱風類型指數(TTI) 37
3.7 集成式機器學習於崩塌潛勢之預測 38
3.7.1 不平衡資料 39
3.7.2 隨機森林(Random Forest) 40
3.7.3 隨機森林模型之評估標準 43
第四章結果分析與討論 47
4.1 崩塌事件之風雨指標統計顯著性差異分析 47
4.2 風雨指標之三維直方圖統計分析 51
4.3 崩塌點聚類分析 53
4.4 隨機森林模型分析 56
4.4.1 模型訓練及驗證 56
4.4.2 模型分類結果之評估 58
4.4.3 特徵重要性評估結果 61
4.5 結果比較與討論 64
第五章結論與建議 66
5.1 結論 66
5.2 建議 68
5.3 貢獻 69
第六章參考文獻 70
附錄一 78
附錄二 100
評審意見回覆表 112

參考文獻

Abancó, C., Bennett, G. L., Matthews, A. J., Matera, M. A. M., & Tan, F. J. (2021). The role of geomorphology, rainfall and soil moisture in the occurrence of landslides triggered by 2018 Typhoon Mangkhut in the Philippines. Natural Hazards and Earth System Sciences, 21(5), 1531-1550.
Abdulhafedh, A. (2021). Incorporating k-means, hierarchical clustering and pca in customer segmentation. Journal of City and Development, 3(1), 12-30.
Adhikary, P. P., & Dash, C. J. (2017). Comparison of deterministic and stochastic methods to predict spatial variation of groundwater depth. Applied Water Science, 7, 339-348.
Ali, M. Z., Chu, H.-J., Chen, Y.-C., & Ullah, S. (2021). Machine learning in earthquake- and typhoon-triggered landslide susceptibility mapping and critical factor identification. Environmental Earth Sciences, 80(6), 233.
Alimohammadlou, Y., Najafi, A., & Yalcin, A. (2013). Landslide process and impacts: A proposed classification method. Catena, 104, 219-232.
Althuwaynee, O. F., Pradhan, B., Park, H.-J., & Lee, J. H. (2014). A novel ensemble decision tree-based CHi-squared Automatic Interaction Detection (CHAID) and multivariate logistic regression models in landslide susceptibility mapping. Landslides, 11, 1063-1078.
Azarafza, M., Azarafza, M., Akgün, H., Atkinson, P. M., & Derakhshani, R. (2021). Deep learning-based landslide susceptibility mapping. Scientific reports, 11(1), 24112.
Benjamin, D. J., Berger, J. O., Johannesson, M., Nosek, B. A., Wagenmakers, E.-J., Berk, R., Bollen, K. A., Brembs, B., Brown, L., & Camerer, C. (2018). Redefine statistical significance. Nature human behaviour, 2(1), 6-10.
Biau, G., & Scornet, E. (2016). A random forest guided tour. Test, 25, 197-227.
Breiman, L. (2001). Random forests. Machine learning, 45, 5-32.
Bui, D. T., Tsangaratos, P., Nguyen, V.-T., Van Liem, N., & Trinh, P. T. (2020). Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment. Catena, 188, 104426.
Casagli, N., Intrieri, E., Tofani, V., Gigli, G., & Raspini, F. (2023). Landslide detection, monitoring and prediction with remote-sensing techniques. Nature Reviews Earth & Environment, 4(1), 51-64.
Chang, K. t., Chiang, S. H., & Lei, F. (2008). Analysing the relationship between typhoon‐triggered landslides and critical rainfall conditions. Earth Surface Processes and Landforms: The Journal of the British Geomorphological Research Group, 33(8), 1261-1271.
Chen, W., Xie, X., Wang, J., Pradhan, B., Hong, H., Bui, D. T., Duan, Z., & Ma, J. (2017). A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility. Catena, 151, 147-160.
Chen, Y.-H. (2023). Investigating the Correlation between the Characteristics of Seismic Activity and Environmental Variables in Taiwan.
Cheng, Y.-S., Yu, T.-T., & Son, N.-T. (2021). Random Forests for Landslide Prediction in Tsengwen River Watershed, Central Taiwan. Remote Sensing, 13(2), 199.
Choy, C.-w., of Insurers, T. H. K. F., Wu, M.-c., & Lee, T.-c. (2020). Assessment of the damages and direct economic loss in Hong Kong due to Super Typhoon Mangkhut in 2018. Tropical Cyclone Research and Review, 9(4), 193-205.
Collini, E., Palesi, L. I., Nesi, P., Pantaleo, G., Nocentini, N., & Rosi, A. (2022). Predicting and understanding landslide events with explainable AI. IEEE Access, 10, 31175-31189.
Dai, F., & Lee, C. (2001). Frequency–volume relation and prediction of rainfall-induced landslides. Engineering Geology, 59(3-4), 253-266.
Dai, F., Lee, C. F., & Ngai, Y. Y. (2002). Landslide risk assessment and management: an overview. Engineering Geology, 64(1), 65-87.
Dang, V.-H., Dieu, T. B., Tran, X.-L., & Hoang, N.-D. (2019). Enhancing the accuracy of rainfall-induced landslide prediction along mountain roads with a GIS-based random forest classifier. Bulletin of Engineering Geology and the Environment, 78, 2835-2849.
De Winter, J. C., & Dodou, D. (2010). Five-point Likert items: t test versus Mann-Whitney-Wilcoxon. Practical assessment, research & evaluation, 15(11), 1-12.
Dhakal, A. S., & Sidle, R. C. (2004). Distributed simulations of landslides for different rainfall conditions. Hydrological Processes, 18(4), 757-776.
Du Toit, W. (2008). Radial basis function interpolation Stellenbosch: Stellenbosch University].
Fan, C.-C., Li, S.-C., & Lu, J.-Z. (2022). Modeling the effect of high soil moisture on the wind resistance of urban trees. Forests, 13(11), 1875.
Finlay, P. J., Fell, R., & Maguire, P. K. (1997). The relationship between the probability of landslide occurrence and rainfall. Canadian Geotechnical Journal, 34(6), 811-824.
Fushiki, T. (2011). Estimation of prediction error by using K-fold cross-validation. Statistics and Computing, 21, 137-146.
Haibo, M., & Gonghui, W. (2022). Evolution mechanism of rainstorm-induced shallow landslides on slopes covered by arbors considering the influence of wind-induced vibration. 地质科技通报, 41(2), 60-70.
Hayat, M. J. (2010). Understanding statistical significance. Nursing research, 59(3), 219-223.
Helming, K. (1999). Wind speed effects on rain erosivity. Sustaining the global farm-Selected papers from the 10th International Soil Conservation Organization Meeting, held in,
Huang, J., & Ling, C. X. (2005). Using AUC and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering, 17(3), 299-310.
Iverson, R. M. (2015). Scaling and design of landslide and debris-flow experiments. Geomorphology, 244, 9-20.
Jain, A. K., Murty, M. N., & Flynn, P. J. (1999). Data clustering: a review. ACM computing surveys (CSUR), 31(3), 264-323.
Korup, O., & Stolle, A. (2014). Landslide prediction from machine learning. Geology today, 30(1), 26-33.
Lai, J.-S., & Tsai, F. (2019). Improving GIS-based landslide susceptibility assessments with multi-temporal remote sensing and machine learning. Sensors, 19(17), 3717.
Li, D., Yin, K., & Leo, C. (2010). Analysis of Baishuihe landslide influenced by the effects of reservoir water and rainfall. Environmental Earth Sciences, 60(4), 677-687.
Li, Y., Wang, Y., Ma, C., Zhang, H., Wang, Y., Song, S., & Zhu, J. (2016). Influence of the spatial layout of plant roots on slope stability. Ecological Engineering, 91, 477-486.
Lin, C.-W., Chang, W.-S., Liu, S.-H., Tsai, T.-T., Lee, S.-P., Tsang, Y.-C., Shieh, C.-L., & Tseng, C.-M. (2011). Landslides triggered by the 7 August 2009 Typhoon Morakot in southern Taiwan. Engineering Geology, 123(1-2), 3-12.
Lin, Y.-C., Wang, W.-H., Lai, C.-Y., & Lin, Y.-Q. (2020). Typhoon Type Index: A New Index for Understanding the Rain or Wind Characteristics of Typhoons and Its Application to Agricultural Losses and Crop Vulnerability [The study of Typhoon characteristics on agricultural damage, landslide disasters, and the remote sensing image recognition on landslides.]. Journal of Applied Meteorology and Climatology, 59(5), 973-989.
Lin, Y.-J., Lin, J.-H., & Tan, Y.-C. (2016). Rainfall Threshold of Triggering Landslide-an Example of Typhoon Soudelor in 2015. EGU General Assembly Conference Abstracts,
Liu, J.-K., & Shih, P. T. Y. (2013). Topographic Correction of Wind-Driven Rainfall for Landslide Analysis in Central Taiwan with Validation from Aerial and Satellite Optical Images. Remote Sensing, 5(6), 2571-2589.
Lobo, J. M., Jiménez‐Valverde, A., & Real, R. (2008). AUC: a misleading measure of the performance of predictive distribution models. Global ecology and Biogeography, 17(2), 145-151.
Ma, Z., Mei, G., & Piccialli, F. (2021). Machine learning for landslides prevention: a survey. Neural Computing and Applications, 33, 10881-10907.
Mann, H. B., & Whitney, D. R. (1947). On a test of whether one of two random variables is stochastically larger than the other. The annals of mathematical statistics, 50-60.
Matsuura, S., Asano, S., & Okamoto, T. (2008). Relationship between rain and/or meltwater, pore-water pressure and displacement of a reactivated landslide. Engineering Geology, 101(1-2), 49-59.
McElduff, F., Cortina-Borja, M., Chan, S.-K., & Wade, A. (2010). When t-tests or Wilcoxon-Mann-Whitney tests won′t do. Advances in physiology education, 34(3), 128-133.
McKnight, P. E., & Najab, J. (2010). Mann‐Whitney U Test. The Corsini encyclopedia of psychology, 1-1.
Mishra, P. K., Nath, S. K., Sen, M. K., & Fasshauer, G. E. (2018). Hybrid Gaussian-cubic radial basis functions for scattered data interpolation. Computational geosciences, 22, 1203-1218.
Mohan, A., Singh, A. K., Kumar, B., & Dwivedi, R. (2021). Review on remote sensing methods for landslide detection using machine and deep learning. Transactions on Emerging Telecommunications Technologies, 32(7), e3998.
Moriwaki, H., Inokuchi, T., Hattanji, T., Sassa, K., Ochiai, H., & Wang, G. (2004). Failure processes in a full-scale landslide experiment using a rainfall simulator. Landslides, 1(4), 277-288.
Nelson, O., Kassim, A., Yunusa, G. H., & Talib, Z. A. (2015). Modelling the effect of wind forces on landslide occurrence in Bududa district, Uganda.
Parmar, A., Katariya, R., & Patel, V. (2019). A review on random forest: An ensemble classifier. International conference on intelligent data communication technologies and internet of things (ICICI) 2018,
Ran, Q., Hong, Y., Li, W., & Gao, J. (2018). A modelling study of rainfall-induced shallow landslide mechanisms under different rainfall characteristics. Journal of Hydrology, 563, 790-801.
Reichenbach, P., Rossi, M., Malamud, B. D., Mihir, M., & Guzzetti, F. (2018). A review of statistically-based landslide susceptibility models. Earth-science reviews, 180, 60-91.
Rigatti, S. J. (2017). Random forest. Journal of Insurance Medicine, 47(1), 31-39.
Scaioni, M., Longoni, L., Melillo, V., & Papini, M. (2014). Remote sensing for landslide investigations: An overview of recent achievements and perspectives. Remote Sensing, 6(10), 9600-9652.
Schindler, D., Bauhus, J., & Mayer, H. (2012). Wind effects on trees. In (Vol. 131, pp. 159-163): Springer.
Strobl, C., Boulesteix, A.-L., Zeileis, A., & Hothorn, T. (2007). Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC bioinformatics, 8(1), 1-21.
Stumpf, A., & Kerle, N. (2011). Object-oriented mapping of landslides using Random Forests. Remote sensing of environment, 115(10), 2564-2577.
Sufi, F. K. (2021). AI-Landslide: Software for acquiring hidden insights from global landslide data using Artificial Intelligence. Software Impacts, 10, 100177.
Sun, D., Wen, H., Wang, D., & Xu, J. (2020). A random forest model of landslide susceptibility mapping based on hyperparameter optimization using Bayes algorithm. Geomorphology, 362, 107201.
Syakur, M., Khotimah, B., Rochman, E., & Satoto, B. D. (2018). Integration k-means clustering method and elbow method for identification of the best customer profile cluster. IOP conference series: materials science and engineering,
Taalab, K., Cheng, T., & Zhang, Y. (2018). Mapping landslide susceptibility and types using Random Forest. Big Earth Data, 2(2), 159-178.
Tangirala, S. (2020). Evaluating the impact of GINI index and information gain on classification using decision tree classifier algorithm. International Journal of Advanced Computer Science and Applications, 11(2), 612-619.
Tien Bui, D., 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, 361-378.
Tu, X., Kwong, A., Dai, F., Tham, L., & Min, H. (2009). Field monitoring of rainfall infiltration in a loess slope and analysis of failure mechanism of rainfall-induced landslides. Engineering Geology, 105(1-2), 134-150.
Usbeck, T., Wohlgemuth, T., Dobbertin, M., Pfister, C., Bürgi, A., & Rebetez, M. (2010). Increasing storm damage to forests in Switzerland from 1858 to 2007. Agricultural and Forest Meteorology, 150(1), 47-55.
Wang, F., Franco-Penya, H.-H., Kelleher, J. D., Pugh, J., & Ross, R. (2017). An analysis of the application of simplified silhouette to the evaluation of k-means clustering validity. Machine Learning and Data Mining in Pattern Recognition: 13th International Conference, MLDM 2017, New York, NY, USA, July 15-20, 2017, Proceedings 13,
Wang, W.-H. (2020). 探討颱風特性於農損及坡地災害遙測影像辨識之研究 National Central University].
Wang, Z. C., Zhao, Q. H., & Han, J. (2013). Physical modeling of the effect of vegetation on slope stability under typhoon. Journal of Natural Disasters, 22(4), 145-152.
Wong, T.-T. (2015). Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern recognition, 48(9), 2839-2846.
Wong, T.-T., & Yeh, P.-Y. (2019). Reliable accuracy estimates from k-fold cross validation. IEEE Transactions on Knowledge and Data Engineering, 32(8), 1586-1594.
Wu, T. H. (1984). Effect of vegetation on slope stability.
Yan, Z.-x., Song, Y., Jiang, P., & Wang, H.-y. (2010). Mechanical analysis of interaction between plant roots and rock and soil mass in slope vegetation. Applied Mathematics and Mechanics, 31(5), 617-622.
Yang, R., & Xing, B. (2021). A comparison of the performance of different interpolation methods in replicating rainfall magnitudes under different climatic conditions in Chongqing Province (China). Atmosphere, 12(10), 1318.
Yuan, C., & Yang, H. (2019). Research on K-value selection method of K-means clustering algorithm. J, 2(2), 226-235.
Zhuang, Y., Xing, A., Jiang, Y., Sun, Q., Yan, J., & Zhang, Y. (2022). Typhoon, rainfall and trees jointly cause landslides in coastal regions. Engineering Geology, 298, 106561.
國家災害防救科技中心. (2016). 臺灣氣候變遷災害衝擊風險評估報告.
楊樹榮, 林忠志, 鄭錦桐, 潘國樑, 蔡如君, & 李正利. (2011). 臺灣常用山崩分類系統. The 14th conference on current researhes in geotechnical engineering in Taiwan
詹繡襄. (2021). 降雨引致之坡地崩塌災害損失評估.

指導教授

林遠見(Yuan-Chien Lin)

審核日期

2024-6-28

推文