Assessment of data mining, multi-criteria decision making and fuzzy-computing techniques for spatial flood susceptibility mapping: a comparative study

被引：15

作者：

Balogun, Abdul-Lateef ^{[1
,2
]}

Sheng, Tan Yong ^{[1
]}

Sallehuddin, Muhammad Helmy ^{[1
]}

Aina, Yusuf A. ^{[3
]}

Dano, Umar Lawal ^{[4
]}

Pradhan, Biswajeet ^{[5
,6
,7
,8
]}

Yekeen, Shamsudeen ^{[9
]}

Tella, Abdulwaheed ^{[10
]}

机构：

[1] Univ Teknol PETRONAS, Dept Civil & Environm Engn, Geospatial Anal & Modelling GAM Res Lab, Seri Iskandar, Perak, Malaysia

[2] Esri Australia, Profess Serv Dept Resources, West Melbourne, Vic, Australia

[3] Yanbu Ind Coll, Dept Geomat Engn Technol, Yanbu, Saudi Arabia

[4] Imam Abdulrahman Bin Faisal Univ, Dept Urban & Reg Planning, Dammam, Saudi Arabia

[5] Univ Technol Sydney, Fac Engn & IT, Ctr Adv Modeling & Geospatial Informat Syst CAMGI, Sydney, NSW, Australia

[6] Sejong Univ, Dept Energy & Mineral Resources Engn, Seoul, South Korea

[7] King Abdulaziz Univ, Ctr Excellence Climate Change Res, Dept Meteorol, Jeddah, Saudi Arabia

[8] Univ Kebangsaan Malaysia, Earth Observat Ctr, Inst Climate Change, Bangi, Selangor, Malaysia

[9] Univ Guelph, Dept Geog Environm & Geomat, Guelph, ON, Canada

[10] Foresight Inst Res & Translat, Earth Environm & Space Div, Ibadan, Nigeria

来源：

GEOCARTO INTERNATIONAL | 2022年 / 37卷 / 26期

关键词：

Multi-criteria decision making; GIS; flood hazard; remote sensing; flood susceptibility; fuzzy computing; machine learning; ANALYTICAL HIERARCHY PROCESS; SUPPORT VECTOR MACHINE; NEURAL-NETWORK MODEL; HAZARD AREAS; RIVER-BASIN; GIS; FRAMEWORK; KELANTAN; RISK; AHP;

D O I：

10.1080/10106049.2022.2076910

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

This study develops an Adaboost-GIS model for flood susceptibility mapping and evaluates its relative performance by undertaking a comparative assessment of the machine learning model with Multi-Criteria Decision Making (MCDM) and soft computing models integrated with GIS. An Analytic Hierarchy Process (AHP), Analytic Network Process (ANP), Fuzzy-AHP, Fuzzy-ANP and AdaBoost machine learning models were developed and integrated with GIS to classify the susceptibility of the study area. Out of 70 sample validation locations, Adaboost's performance was the best with a 95.72% similarity match with very high and high susceptibility locations followed by F-ANP, ANP, F-AHP and AHP with 95.65%, 92.75%, 81.42% and 77.14% similarity matches, respectively. It also had the highest AUC (0.864). Thus, the Adaboost machine learning, Fuzzy computing and conventional MCDM models can be adopted by stakeholders for accurately assessing flood susceptibility, thereby fostering safe and resilient cities.

引用

页码：12989 / 13015

页数：27

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