The knowledge expression on debris flow potential analysis through PCA + LDA and rough sets theory: a case study of Chen-Yu-Lan watershed, Nantou, Taiwan

Abstract

Debris flow is often performed through identifying and analyzing the soil condition, hydraulic, geomorphological factors and vegetation conditions. In the present study, a spatial information analysis system is combined with a linear statistical method (principle components analysis with linear discriminant analysis, PCA ? LDA) and an advanced data mining technique (discrete rough sets, DRS) to investigate the debris flow occurrence based on geomorphological and vegetation conditions factors. The analyzed data sources include (1) digital elevation model: to investigate the variation in the landscape, and (2) remote sensing data: to analyze the vegetation and plant conditions on the ground surface. The objective of this research is to define a method with the ability to forecast the level of debris flow susceptibility through the parallel study of statistical outcomes (PCA ? LDA) and data mining results (DRS). The outcomes from PCA ? LDA are inadequate due to the thresholds of the influenced variables not being examined. In this study, the DRS approach not only showed satisfactory results for the thresholds of influenced variables in the study area, but also the occurrence rules of debris flow are generated. Finally, the results show superior classification accuracy (70.8% for debris flow occurrence) for the DRS method over those of PCA ? LDA analysis (54.2% for debris flow occurrence) for the analysis of debris flow occurrence. Therefore, this is an encouraging preliminary approach in the hazard assessment of debris flow.

Debris Flow Susceptibility Assessment in the Wudongde Dam Area, China Based on Rock Engineering System and Fuzzy C-Means Algorithm

“…The stream density is expressed as the total length of all the streams in a catchment divided by the total area [33,38]. This factor reflects the interactions among lithology, geological structures, and weathering degree of rocks in a catchment because drainages often develop in weak area [33].…”

“…This factor reflects the interactions among lithology, geological structures, and weathering degree of rocks in a catchment because drainages often develop in weak area [33]. In addition, it can affect the shape of a river's hydrograph during a rainstorm [38]. Investigations suggest that catchments carved by numerous streams are prone to the initiation of debris flows.…”

A novel hybrid KPCA and SVM with PSO model for identifying debris flow hazard degree: a case study in Southwest China

“…Moreover, establishing evaluation models tend to be subjective. Consequently, identifying debris flow hazard degree remains a challenge (Lei et al 2011). In the past decades, several well-developed identification techniques have been applied to solve the aforementioned problems in the hazard assessment.…”

“…Each technique follows its respective criteria for its analytical procedures. For example, back-propagation neural network (BPNN), Monte Carlo simulations, extension theory, Bayesian network, rough sets theory, the stepwise discriminant technique, and other empirical approaches have been applied and have achieved promising results (Mohamed et al 2005;Calvo and Savi 2009;Zhang et al 2010;Liang et al 2012;Lei et al 2011;Niu et al 2014;Xu et al 2014). However, each method has its own advantages and disadvantages.…”

Landslide susceptibility assessment at Wadi Jawrah Basin, Jizan region, Saudi Arabia using two bivariate models in GIS

“…Several different statistical approaches have been developed such as; general instability index (Carrara, 1983), frequency index (Temesgen et al, 2001), surface percentage index (Uromeihy and Mahdvifar, 2000), statistical index method , weighting factor (Cevik and Topal, 2003), certainty factor (Binaghi et al, 1998;Devkota et al, 2013;, conditional analysis (Clerici et al, 2002;Duman et al, 2005;Pourghasemi et al, 2012), weights of evidence (Neuhäuser and Terhorst, 2007;Thiery et al, 2007;Regmi et al, 2014), landslide susceptibility analysis (Süzen and Doyuran, 2004), and information value method (Yin and Yan, 1988). Multivariate methods also can be named as; multiple linear regression analysis (Carrara, 1983), discriminant analysis (Santacana et al, 2003;Guzzetti et al, 2005), and logistic regression analysis (Lee and Evangelista, 2006;Yilmaz, 2009;Nefeslioglu et al, 2010;Pradhan, 2010;Bai et al, 2011;Lei et al, 2011;Pradhan, 2011;. Deterministic models (a.k.a physically based models) mainly rely on slope instability assessment according to physical theories (Bellugi and De Asua, 2001;Guzzetti et al, 2002;Crosta and Agliardi, 2004;Youssef et al, 2009).…”