Discrete rough set analysis of two different soil-behavior-induced landslides in National Shei-Pa Park, Taiwan

Abstract The governing factors that influence landslide occurrences are complicated by the different soil conditions at various sites. To resolve the problem, this study focused on spatial information technology to collect data and information on geology. GIS, remote sensing and digital elevation model (DEM) were used in combination to extract the attribute values of the surface material in the vast study area of Shei-Pa National Park, Taiwan. The factors influencing landslides were collected and quantification values computed. The major soil component of loam and gravel in the Shei-Pa area resulted in different landslide problems. The major factors were successfully extracted from the influencing factors. Finally, the discrete rough set (DRS) classifier was used as a tool to find the threshold of each attribute contributing to landslide occurrence, based upon the knowledge database. This rule-based knowledge database provides an effective and urgent system to manage landslides. NDVI (Normalized Difference Vegetation Index), VI (Vegetation Index), elevation, and distance from the road are the four major influencing factors for landslide occurrence. The landslide hazard potential diagrams (landslide susceptibility maps) were drawn and a rational accuracy rate of landslide was calculated. This study thus offers a systematic solution to the investigation of landslide disasters.

[1]  Gene H. Golub,et al.  Generalized cross-validation as a method for choosing a good ridge parameter , 1979, Milestones in Matrix Computation.

[2]  Shiuan Wan,et al.  A landslide expert system: image classification through integration of data mining approaches for multi-category analysis , 2012, Int. J. Geogr. Inf. Sci..

[3]  V. Prasannakumar,et al.  Estimation of soil erosion risk within a small mountainous sub-watershed in Kerala, India, using Revised Universal Soil Loss Equation （RUSLE） and geo-information technology , 2012 .

[4]  B. Khazai,et al.  Evaluation of factors controlling earthquake-induced landslides caused by Chi-Chi earthquake and comparison with the Northridge and Loma Prieta events , 2004 .

[5]  J. Corominas,et al.  A GIS-Based Multivariate Statistical Analysis for Shallow Landslide Susceptibility Mapping in La Pobla de Lillet Area (Eastern Pyrenees, Spain) , 2003 .

[6]  Wen-Chieh Chou,et al.  Vegetation recovery patterns assessment at landslides caused by catastrophic earthquake: A case study in central Taiwan , 2009, Environmental monitoring and assessment.

[7]  M. Matteucci,et al.  Artificial neural networks and cluster analysis in landslide susceptibility zonation , 2008 .

[8]  Wen-Chieh Chou,et al.  Vegetation recovery assessment at the Jou-Jou Mountain landslide area caused by the 921 Earthquake in Central Taiwan , 2004 .

[9]  R. Anbalagan,et al.  Landslide hazard evaluation and zonation mapping in mountainous terrain , 1992 .

[10]  Saro Lee,et al.  Determination and application of the weights for landslide susceptibility mapping using an artificial neural network , 2004 .

[11]  A. Zerger.  Examining GIS decision utility for natural hazard risk modelling , 2002, Environ. Model. Softw..

[12]  Shiuan Wan,et al.  A spatial decision support system for extracting the core factors and thresholds for landslide susceptibility map , 2009 .

[13]  A. K. Turner,et al.  Landslides : investigation and mitigation , 1996 .

[14]  David M. Cruden,et al.  LANDSLIDE TYPES AND PROCESSES , 1958 .

[15]  A. Burak Göktepe,et al.  Soil clustering by fuzzy c-means algorithm , 2005, Adv. Eng. Softw..

[16]  Majid H. Tangestani,et al.  Landslide susceptibility mapping using the fuzzy gamma approach in a GIS, Kakan catchment area, southwest Iran , 2004 .

[17]  T. Chou,et al.  The knowledge rules of debris flow event: A case study for investigation Chen Yu Lan River, Taiwan , 2008 .

[18]  Saro Lee,et al.  Landslide susceptibility analysis using GIS and artificial neural network , 2003 .

[19]  Saro Lee,et al.  Statistical analysis of landslide susceptibility at Yongin, Korea , 2001 .

[20]  M. Terlien,et al.  Prediction of the occurrence of slope instability phenomenal through GIS-based hazard zonation , 1997 .

[21]  A. Akgun,et al.  Landslide susceptibility mapping for Ayvalik (Western Turkey) and its vicinity by multicriteria decision analysis , 2010 .

[22]  Saro Lee,et al.  Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models , 2006 .

[23]  Wen-Tzu Lin,et al.  Earthquake‐induced landslide hazard monitoring and assessment using SOM and PROMETHEE techniques: A case study at the Chiufenershan area in Central Taiwan , 2008, Int. J. Geogr. Inf. Sci..

[24]  P. Reichenbach,et al.  Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy , 1999 .

[25]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[26]  Desire L. Massart,et al.  Rough sets theory , 1999 .

[27]  Biswajeet Pradhan,et al.  Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling , 2010, Environ. Model. Softw..

[28]  Shiuan Wan,et al.  Development of a spatial decision support system for monitoring earthquake-induced landslides based on aerial photographs and the finite element method , 2010, Int. J. Appl. Earth Obs. Geoinformation.

[29]  Z. Pawlak.  Rough Sets: Theoretical Aspects of Reasoning about Data , 1991 .

[30]  Richard M. Iverson,et al.  Landslide triggering by rain infiltration , 2000 .

[31]  T. Lei,et al.  The comparison of PCA and discrete rough set for feature extraction of remote sensing image classification – A case study on rice classification, Taiwan , 2008 .

[32]  R. Gloaguen,et al.  Optimal parameter selection for qualitative regional erosion risk monitoring: A remote sensing study of SE Ethiopia , 2011 .

[33]  José I. Barredo,et al.  Comparing heuristic landslide hazard assessment techniques using GIS in the Tirajana basin, Gran Canaria Island, Spain , 2000 .

[34]  I. Yilmaz.  Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine , 2010 .

[35]  P. Aleotti,et al.  Landslide hazard assessment: summary review and new perspectives , 1999 .

[36]  Tien-Yin Chou,et al.  A novel data mining technique of analysis and classification for landslide problems , 2009 .

[37]  Arie C. Seijmonsbergen,et al.  Engineering geomorphology of the widentobel catchment, appenzell and sankt gallen, switzerland. A geomorphologuical inventory system applied to geotechnical appraisal of slope stability , 1988 .

[38]  B. Pradhan,et al.  Weights-of-evidence model applied to landslide susceptibility mapping in a tropical hilly area , 2010 .

[39]  S. Wan,et al.  An enhanced supervised spatial decision support system of image classification: consideration on the ancillary information of paddy rice area , 2010, Int. J. Geogr. Inf. Sci..

[40]  Carlo Atzeni,et al.  Long term landslide monitoring by ground‐based synthetic aperture radar interferometer , 2006 .

[41]  Jin Ma,et al.  Spatial-temporal variation of the land surface temperature field and present-day tectonic activity , 2010 .

[42]  M. Turrini,et al.  An objective method to rank the importance of the factors predisposing to landslides with the GIS methodology: application to an area of the Apennines (Valnerina; Perugia, Italy) , 2002 .

[43]  C. F. Lee,et al.  Landslide characteristics and, slope instability modeling using GIS, Lantau Island, Hong Kong , 2002 .

[44]  P. Frattini,et al.  Geomorphological and historical data in assessing landslide hazard , 2003 .

[45]  Sinh Hoa Nguyen,et al.  Pattern Extraction from Data , 1998, Fundam. Informaticae.

[46]  R. Soeters,et al.  Slope instability recognition, analysis, and zonation , 1996 .

[47]  Wenpeng Li,et al.  A review of regional groundwater flow modeling , 2011 .

[48]  V. Doyuran,et al.  Data driven bivariate landslide susceptibility assessment using geographical information systems: a method and application to Asarsuyu catchment, Turkey , 2004 .

[49]  D. Varnes.  SLOPE MOVEMENT TYPES AND PROCESSES , 1978 .

[50]  Manfred F. Buchroithner,et al.  Landslide hazard and risk analyses at a landslide prone catchment area using statistical based geospatial model , 2011 .