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Landslide susceptibility zonation method based on c5.0 decision tree and K-means cluster algorithms to improve the efficiency of risk management

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Geoscience Frontiers 2021年第6期12卷 243-261页

作者： Zizheng Guo Yu Shi Faming Huang Xuanmei Fan Jinsong Huang Faculty of Engineering China University of GeosciencesWuhan 430074China School of civil Engineering and Architecture of Engineering Nanchang UniversityNanchang 330031China State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Chengdu University of TechnologyChengdu 610059China ARc centre of Excellence for Geotechnical Science and Engineering University of NewcastleNSW 2287Australia

Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments, but most studies use GIS-based classification methods to conduct susceptibility *** study presents a machine learning approach based on the c5.0 decision tree(DT) model and the K-means cluster algorithm to produce a regional landslide susceptibility map. Yanchang county, a typical landslide-prone area located in northwestern china, was taken as the area of interest to introduce the proposed application procedure. A landslide inventory containing 82 landslides was prepared and subsequently randomly partitioned into two subsets: training data(70% landslide pixels) and validation data(30% landslide pixels). Fourteen landslide influencing factors were considered in the input dataset and were used to calculate the landslide occurrence probability based on the c5.0 decision tree *** zonation was implemented according to the cut-off values calculated by the K-means cluster algorithm. The validation results of the model performance analysis showed that the AUc(area under the receiver operating characteristic(ROc) curve) of the proposed model was the highest, reaching 0.88,compared with traditional models(support vector machine(SVM) = 0.85, Bayesian network(BN) = 0.81,frequency ratio(FR) = 0.75, weight of evidence(WOE) = 0.76). The landslide frequency ratio and frequency density of the high susceptibility zones were 6.76/km^(2) and 0.88/km^(2), respectively, which were much higher than those of the low susceptibility zones. The top 20% interval of landslide occurrence probability contained 89% of the historical landslides but only accounted for 10.3% of the total *** results indicate that the distribution of high susceptibility zones was more focused without containing more " stable" pixels. Therefore, the obtained susceptibility map is suitable for application to landslide risk management practices.

关键词： Landslide susceptibility Frequency ratio c5.0 decision tree K-means cluster classification Risk management

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FPGA-Based Network Traffic Security: Design and Implementation Using c5.0 decision tree classifier

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Journal of Electronic Science and Technology 2013年第4期11卷 393-403页

作者： Tarek Salah Sobh Mohamed Ibrahiem Amer the Information Systems Department Egyptian Armed Forces

In this work, a hardware intrusion detection system （IDS） model and its implementation are introduced to perform online real-time traffic monitoring and analysis. The introduced system gathers some advantages of many IDSs： hardware based from implementation point of view, network based from system type point of view, and anomaly detection from detection approach point of view. In addition, it can detect most of network attacks, such as denial of services （DOS）, leakage, etc. from detection behavior point of view and can detect both internal and external intruders from intruder type point of view. Gathering these features in one IDS system gives lots of strengths and advantages of the work. The system is implemented by using field programmable gate array （FPGA）, giving a more advantages to the system. A c5.0 decision tree classifier is used as inference engine to the system and gives a high detection ratio of 99.93%.

关键词： c5.0 decision tree field programm-able gate array network monitoring network security.

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Data mining and well logging interpretation: application to a conglomerate reservoir

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Applied Geophysics 2015年第2期12卷 263-272,276页

作者：石宁李洪奇罗伟平中国石油大学(北京) 地球物理与信息工程学院北京102249 石油数据挖掘北京市重点实验室北京102249

Data mining is the process of extracting implicit but potentially useful information from incomplete, noisy, and fuzzy data. Data mining offers excellent nonlinear modeling and self-organized learning, and it can play a vital role in the interpretation of well logging data of complex reservoirs. We used data mining to identify the lithologies in a complex reservoir. The reservoir lithologies served as the classification task target and were identified using feature extraction, feature selection, and modeling of data streams. We used independent component analysis to extract information from well curves. We then used the branch-and- bound algorithm to look for the optimal feature subsets and eliminate redundant information. Finally, we used the c5.0 decision-tree algorithm to set up disaggregated models of the well logging curves. The modeling and actual logging data were in good agreement, showing the usefulness of data mining methods in complex reservoirs.

关键词： Data mining well logging interpretation independent component analysis branch-and-bound algorithm c5.0 decision tree

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