Using the fuzzy inference system and morphometric indices, in finding the vulnerable areas to the earthquake

被引:3
作者
Jami, Mohsen [1 ]
Gorgij, Alireza Docheshmeh [1 ]
机构
[1] Univ Sistan & Baluchestan, Fac Ind & Min Khash, Zahedan, Iran
关键词
Earthquake; Vulnerability; Fuzzy Inference System; Relative Tectonic Activity Index; Morphometric Indices; GEOMORPHIC EVIDENCE; LOGIC; IDENTIFICATION; SOUTHEAST; TECTONICS; MODEL;
D O I
10.1007/s12665-021-09751-y
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Every year many earthquakes occurs around the world and some of them are highly destructive. Therefore, the study of the vulnerable areas to the earthquakes seems crucial. Current paper presents a novel method for the assessment of areas, which are prone to the earthquakes, using relative tectonic activity index (IAT). IAT is calculated via six morphometric indices, including Asymmetry factor, Mountain Front Sinuosity, Valley Floor width, Hypsometric Integral, Average of Channel Mean Slope and Drainage Basin Shape. Every index has some limits to show the magnitude of tectonic activity and sometimes they are not clear enough and need a reliable and accurate scoring method to reduce the uncertainty of the calculation results. Morphometric Indices, as the key factors in determining the tectonics situation of an area, were analyzed via fuzzy inference system to improve the accuracy of decision making in the IAT calculation, as the suggested method in present paper for the earthquake vulnerability assessment. Torbat-e-Jam basin was selected as a case of paper to operate the proposed method. Results showed that about 0.87 percent of paper area has the high IAT and is high risk of earthquake, consequently. About 82.47 and 16.66 percent of paper are determined as the moderate and low IAT, respectively. The map of the earthquake intensity of the paper area was plotted, afterwards, to check whether the results of presented method are reliable, and it approved that. The above method, thus, is applicable in those areas, which do not have enough seismologic data.
引用
收藏
页数:15
相关论文
共 50 条
  • [21] Classification of Microarray Data using Fuzzy Inference System
    Kumar, Mukesh
    Rath, Santanu Kumar
    2014 INTERNATIONAL CONFERENCE ON RECENT TRENDS IN INFORMATION TECHNOLOGY (ICRTIT), 2014,
  • [22] Moire Fringe Segmentation Using Fuzzy Inference System
    Woo, Wing Hon
    Yen, Kin Sam
    9TH INTERNATIONAL CONFERENCE ON ROBOTIC, VISION, SIGNAL PROCESSING AND POWER APPLICATIONS: EMPOWERING RESEARCH AND INNOVATION, 2017, 398 : 247 - 255
  • [23] Edge Detection Using Fuzzy Logic (Fuzzy Sobel, Fuzzy Template, and Fuzzy Inference System)
    Katoch, Rachita
    Bhogal, Rosepreet Kaur
    INTELLIGENT COMMUNICATION, CONTROL AND DEVICES, ICICCD 2017, 2018, 624 : 741 - 752
  • [24] Clustering Stock Performance Considering Investor Preferences Using a Fuzzy Inference System
    Abidin, Siti Nazifah Zainol
    Jaaman, Saiful Hafizah
    Ismail, Munira
    Abu Bakar, Ahmad Syafadhli
    SYMMETRY-BASEL, 2020, 12 (07):
  • [25] A nonlinear partial least squares algorithm using quadratic fuzzy inference system
    Abdel-Rahman, Araby I.
    Lim, Gino J.
    JOURNAL OF CHEMOMETRICS, 2009, 23 (9-10) : 530 - 537
  • [26] Rainfall Prediction in the Northeast Region of Thailand using Modular Fuzzy Inference System
    Kajornrit, Jesada
    Wong, Kok Wai
    Fung, Chun Che
    2012 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE), 2012,
  • [27] A Fuzzy Inference System for Multiple Criteria Job Evaluation Using Fuzzy AHP
    Kutlu, Ahmet C.
    Behret, Hulya
    Kahraman, Cengtz
    JOURNAL OF MULTIPLE-VALUED LOGIC AND SOFT COMPUTING, 2014, 23 (1-2) : 113 - 133
  • [28] Modeling and control of an unstable system using probabilistic fuzzy inference system
    Sozhamadevi, N.
    Sathiyamoorthy, S.
    ARCHIVES OF CONTROL SCIENCES, 2015, 25 (03): : 377 - 396
  • [29] IMPLEMENTATION OF A FUZZY INFERENCE SYSTEM USING A NORMALIZED FUZZY NEURAL-NETWORK
    CHAO, CT
    TENG, CC
    FUZZY SETS AND SYSTEMS, 1995, 75 (01) : 17 - 31
  • [30] A Fuzzy Inference System for Credit Scoring using Boolean Consistent Fuzzy Logic
    Latinovic, Milica
    Dragovic, Ivana
    Arsic, Vesna Bogojevic
    Petrovic, Bratislav
    INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS, 2018, 11 (01) : 414 - 427