A Novel Rule-Based Approach in Mapping Landslide Susceptibility

被引:19
作者
Roodposhti, Majid Shadman [1 ]
Aryal, Jagannath [1 ]
Pradhan, Biswajeet [2 ,3 ]
机构
[1] Univ Tasmania, Discipline Geog & Spatial Sci, Sch Technol Environm & Design, Churchill Ave, Hobart, Tas 7005, Australia
[2] Univ Technol Sydney, CAMGIS, Sydney, NSW 2007, Australia
[3] Sejong Univ, Dept Energy & Mineral Resources Engn, 209 Neungdongro Gwangjin Gu, Seoul 05006, South Korea
关键词
Shannon entropy; uncertainty; landslide susceptibility mapping (LSM); GIS; Tasmania; ANALYTICAL HIERARCHY PROCESS; EVIDENTIAL BELIEF FUNCTION; SUPPORT VECTOR MACHINE; LOGISTIC-REGRESSION; FREQUENCY RATIO; STATISTICAL-ANALYSIS; GIS TECHNOLOGY; DECISION TREE; FUZZY; MODELS;
D O I
10.3390/s19102274
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Despite recent advances in developing landslide susceptibility mapping (LSM) techniques, resultant maps are often not transparent, and susceptibility rules are barely made explicit. This weakens the proper understanding of conditioning criteria involved in shaping landslide events at the local scale. Further, a high level of subjectivity in re-classifying susceptibility scores into various classes often downgrades the quality of those maps. Here, we apply a novel rule-based system as an alternative approach for LSM. Therein, the initially assembled rules relate landslide-conditioning factors within individual rule-sets. This is implemented without the complication of applying logical or relational operators. To achieve this, first, Shannon entropy was employed to assess the priority order of landslide-conditioning factors and the uncertainty of each rule within the corresponding rule-sets. Next, the rule-level uncertainties were mapped and used to asses the reliability of the susceptibility map at the local scale (i.e., at pixel-level). A set of If-Then rules were applied to convert susceptibility values to susceptibility classes, where less level of subjectivity is guaranteed. In a case study of Northwest Tasmania in Australia, the performance of the proposed method was assessed by receiver operating characteristics' area under the curve (AUC). Our method demonstrated promising performance with AUC of 0.934. This was a result of a transparent rule-based approach, where priorities and state/value of landslide-conditioning factors for each pixel were identified. In addition, the uncertainty of susceptibility rules can be readily accessed, interpreted, and replicated. The achieved results demonstrate that the proposed rule-based method is beneficial to derive insights into LSM processes.
引用
收藏
页数:20
相关论文
共 50 条
[41]   Landslide susceptibility mapping based on self-organizing-map network and extreme learning machine [J].
Huang, Faming ;
Yin, Kunlong ;
Huang, Jinsong ;
Gui, Lei ;
Wang, Peng .
ENGINEERING GEOLOGY, 2017, 223 :11-22
[42]   Mapping of rainfall-induced landslide susceptibility in Wencheng, China, using support vector machine [J].
Su, Cheng ;
Wang, Lili ;
Wang, Xizhi ;
Huang, Zhicai ;
Zhang, Xiaocan .
NATURAL HAZARDS, 2015, 76 (03) :1759-1779
[43]   Landslide susceptibility mapping by frequency ratio and fuzzy logic approach: a case study of Mogods and Hedil (Northern Tunisia) [J].
Klai, Adel ;
Katlane, Rim ;
Haddad, Romdhane ;
Rabia, Mohamed Chedly .
APPLIED GEOMATICS, 2024, 16 (01) :91-109
[44]   Landslide Susceptibility Modeling: An Integrated Novel Method Based on Machine Learning Feature Transformation [J].
Al-Najjar, Husam A. H. ;
Pradhan, Biswajeet ;
Kalantar, Bahareh ;
Sameen, Maher Ibrahim ;
Santosh, M. ;
Alamri, Abdullah .
REMOTE SENSING, 2021, 13 (16)
[45]   Advancing Landslide Susceptibility Mapping in the Medea Region Using a Hybrid Metaheuristic ANFIS Approach [J].
Debiche, Fatiha ;
Benbouras, Mohammed Amin ;
Petrisor, Alexandru-Ionut ;
Ali, Lyes Mohamed Baba ;
Leghouchi, Abdelghani .
LAND, 2024, 13 (06)
[46]   Comparison of tree-based ensemble learning algorithms for landslide susceptibility mapping in Murgul (Artvin), Turkey [J].
Usta, Ziya ;
Akinci, Halil ;
Akin, Alper Tunga .
EARTH SCIENCE INFORMATICS, 2024, 17 (02) :1459-1481
[47]   A GIS-based comparative study of Dempster-Shafer, logistic regression and artificial neural network models for landslide susceptibility mapping [J].
Chen, Wei ;
Pourghasemi, Hamid Reza ;
Zhao, Zhou .
GEOCARTO INTERNATIONAL, 2017, 32 (04) :367-385
[48]   Frequency ratio model based landslide susceptibility mapping in lower Mae Chaem watershed, Northern Thailand [J].
Intarawichian, N. ;
Dasananda, S. .
ENVIRONMENTAL EARTH SCIENCES, 2011, 64 (08) :2271-2285
[49]   A Support Vector Machine for Landslide Susceptibility Mapping in Gangwon Province, Korea [J].
Lee, Saro ;
Hong, Soo-Min ;
Jung, Hyung-Sup .
SUSTAINABILITY, 2017, 9 (01)
[50]   A comprehensive review of machine learning-based methods in landslide susceptibility mapping [J].
Liu, Songlin ;
Wang, Luqi ;
Zhang, Wengang ;
He, Yuwei ;
Pijush, Samui .
GEOLOGICAL JOURNAL, 2023, 58 (06) :2283-2301