1. 專題討論 2016/5/26 1 授課老師：謝平城教授 指導老師 : 詹勳全副教授 學生：温祐霆 學號： 7102042012

2. 2016/5/26 2 Analysis of topographic and vegetative factors with data mining for landslide verification Fuan Tsai a,b, ∗, Jhe-Syuan Lai b, Walter W. Chen c, Tang-Huang Lin a a Center for Space and Remote Sensing Research, National Central University, Zhongli, Taoyuan 320, Taiwan b Department of Civil Engineering, National Central University, Zhongli, Taoyuan 320, Taiwan c Department of Civil Engineering, National Taipei University of Technology, Taipei 10608, Taiwan Ecological Engineering ： 2013

3. Contents Introduction 1 Landslide analysis and data mining 2 Materials and methods 3 Results 4 2016/5/26 Discussion 5 Conclusions 6 3

4. Introduction  Taiwan is located in East Asia where Eurasian continent and Philippine Sea plates collide with each other.  Taiwan is also located in the passing route of Western Pacific tropical cyclones (typhoons).  The geological and climate conditions in conjunction with the dense population make Taiwan one of the most vulnerable countries to natural disasters as listed by the World Bank (Dilley et al.,2005). 2016/5/26 4

5. Introduction  Among the natural disasters in Taiwan, landslides are commonly triggered by earthquakes and heavy rainfall, especially in the mountainous regions.  For example, the Chi–Chi earthquake in 1999 caused numerous landslides in central Taiwan (Lin et al.,2006; Lo et al., 2010); and Typhoon Morakot in 2009 also induced catastrophic landslides and debris flows in southern Taiwan (Tsaiet al., 2010). 2016/5/26 5

6. Introduction  These types of natural hazards often result in not only serious property and infrastructure damages but also human casualties.  Therefore, landslide analysis and assessment has become an important issue in hazard mitigation and prevention in Taiwan.  In order to better understand the relationship between landslides and various topographic and vegetative factors, this study utilized data mining techniques to analyze the factors with collected landslide events in the Shimen reservoir watershed in northern Taiwan. 2016/5/26 6

7. Landslide analysis and data mining 2016/5/26 7 Quantitative Analysis deterministic method heuristic method statistical method 1.based on the physical laws 2. suitable for small and relatively homogenous regions 1. rank and weight the causative factors of landslides 2. The processing is usually subjective 1. will occur on similar conditions from past and present instability

8. Landslide analysis and data mining 2016/5/26 8  spatial technologies and data have been used intensively to effectively investigate and monitor natural hazards, including landslides.  data mining (DM) is an important and effective technique in the field of knowledge discovery that can extract knowledge from complicated data, database or data warehouse.  A few landslide-related analysis methods have integrated DM algorithms in different forms, including Decision,Bayesian Network,artificial neural network and object-oriented methods

9. Landslide analysis and data mining 2016/5/26 9  Decision Tree (DT) algorithm is a classical, universal and comprehensible method.  Bayesian Network (BN) has also been proved an effective data mining approach for landslide related assessment.  this study integrates data mining techniques and spatial analysis to analyze topographic and vegetative factors of landslides from collected spatial data sets and landslide inventories for constructing landslide factor models.

10. Materials and methods 2016/5/26 10  763.4 km 2  2500 mm/year  between May and October every year  250 to 3500 m  Slop>55%  60% area 30%<slop<55%  29% area Study area

11. Materials and methods 2016/5/26 11 Study area

12. Materials and methods 2016/5/26 12 Study area

13. Materials and methods 2016/5/26 13  Based on the long-term monitoring project of the study site (Tsai and Chen, 2007), although there were a few earthquakes, they did not cause significant landslides in the Shi-men reservoir watershed.  In the data-driven landslide analysis system proposed in this paper, all landslides are assumed to be triggered by heavy rainfall in the study site.  In addition, this study does not distinguish different types of landslides. Materials and data preprocessing

14. Materials and methods 2016/5/26 14 Materials and data preprocessing Original dataFactorResolution/scale DEMElevation40 m × 40 m Slope Aspect Curvature Satellite imagesNDVI10 m × 10 m Stream mapDistance to river1/5,000 Road mapDistance to road1/5,000 Fault mapDistance to fault1/50,000 Geology mapGeology1/50,000 Soil mapSoil1/25,000 Land-cover mapLanduse1/5,000 Resample 10 m × 10 m

15. Materials and methods 2016/5/26 15 Materials and data preprocessing  landslide inventory consists of landslide extents identified with satellite remote sensing and spatial analysis.  Most of the landslides are small to medium in terms of size.  Typhoon Aere triggered a few large-scale landslides in the southwest region of the watershed in 2004.

16. Materials and methods 2016/5/26 16 Data mining analysis Using error matrix to calculate 1.Overall Accuracy (OA), 2.Producer’s Accuracy (PA), 3.User’s Accuracy (UA) 4. Kappa coefficient indexes

17. Materials and methods 2016/5/26 17  This study employs two algorithms for the kernel computation of data mining.  Decision Tree (DT)  Bayesian Net-work (BN) Data mining kernel computing

18. Materials and methods 2016/5/26 18 Data mining kernel computing Decision Tree (DT) ◎假設有 16 筆顧客資料，曾購買 NB 有 4 筆，未曾購買有 12 筆。 ◎將 16 位顧客分成 2 組： 1. 年齡小於 30 歲：曾買 NB 有 1 筆，未買 NB 有 5 筆。 2. 年齡大於或等於 30 歲：曾買 NB 有 3 筆，未買 NB 有 7 筆。 I(p,n)=I(4,12)=0.8113 E(age)=(6/16)I(1,5)+(10/16)I(3,7)=0.7946 Gain(age)=0.8113-0.7946=0.0167 分別計算依年齡、婚姻、收入等三個屬性資料獲利，以資訊獲利最大者 為第一分類依據

19. 19 Materials and methods 2016/5/26 Data mining kernel computing Decision Tree (DT) ◎屬性值配對共有七種：年齡小於 30 歲、年齡大於或等於 30 歲、婚姻狀態為單身、 婚姻狀態為已婚、收入為低、收入為中、收入為高。 PRISM_Gain( 婚姻＝單身 )=log(3/7)= -1.224 1. 計算此七種屬性值配對的資訊獲利 2. 分別計算其他屬性值配對的資訊獲利 Prism( 年齡 >=30)= log 2 (3/3)= -1 PRISM( 收入＝高 )= log 2 (1/1)=0

20. Materials and methods 2016/5/26 20 Data mining kernel computing Bayesian Network(BN)  Bayesian Network is a Directed Acyclic Graph (DAG) consisting of nodes and connectors.  Nodes represent the independent variables or conditional attributes.  End-nodes are the dependent variables or decision attributes.

21. Materials and methods 2016/5/26 21  It is necessary to analyze the significance of different landslide factors in the detection, check, and prediction phases in order to better understand their impacts.  Every condition attribute of continuous data has a standard deviation, σ.  Calculate each Mean and Standard Deviation.  Kept rule is between the Plus-Minus n*Standard Deviation.  After an empirical analysis, 5σ was selected as the threshold to filter out data uncertainties in this study. Factor analysis and uncertainty filtering

22. Materials and methods 2016/5/26 22  The landslide factor models constructed from data mining analysis can be used as the basis for landslide susceptibility assessment.  This study constructed landslide factor models are applied to the prediction dataset to obtain probability values.  The resultant susceptibility regions are categorized into three different levels: very high (>85%), high (70–85%), and medium to high (50–70%). Susceptibility assessment

23. Results 2016/5/26 23  Both the training and check data were generated from landslide inventories from 2004 to 2007.  The constructed models were then applied to analyze the 2008 data set for potential landslide assessment (prediction). Red-prediction Yellow-ground truth

24. Results 2016/5/26 24 after filtering out before filtering out 29% 20%

25. Discussion 2016/5/26 25 Factor significance analysis Factor significance analysis after uncertainty filtering.

26. Discussion 2016/5/26 26 Red-prediction Yellow-ground truth

27. Discussion 2016/5/26 27

28. Conclusions 11 Using data mining and spatial analysis to analyze topographic and vegetative factors of landslides in the Shimen reservoir. 22 Decision Tree and Bayesian Network data mining algorithms were used for landslide detection to verify the effectiveness of the constructed models. 33 To reduce the data uncertainties, a statistics- based mechanism was developed to filter out data uncertainty 2016/5/26 28

29. Conclusions 44 after filtering out data uncertainties, the accuracy increased and the Kappa coefficients for DT and BN analysis have also increased by 29% and 20 %. 55 NDVI, land-use, distance to fault, and distance to river are the most significant latent factors of landslides in the study site. 66 Bayesian Network data mining approach produced better results in landslide detection and prediction in this study 2016/5/26 29

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