PMID- 38160971
OWN - NLM
STAT- MEDLINE
DCOM- 20240305
LR  - 20240305
IS  - 1096-0953 (Electronic)
IS  - 0013-9351 (Linking)
VI  - 245
DP  - 2024 Mar 15
TI  - Coastal Flood risk assessment using ensemble multi-criteria decision-making with 
      machine learning approaches.
PG  - 118042
LID - S0013-9351(23)02846-3 [pii]
LID - 10.1016/j.envres.2023.118042 [doi]
AB  - Coastal areas are at a higher risk of flooding, and novel changes in the climate 
      are induced to raise the sea level. Flood acceleration and frequency have 
      increased recently because of unplanned infrastructural conveniences and 
      anthropogenic activities. Therefore, the assessment of flood susceptibility 
      mapping is considered the most significant flood management model. In this paper, 
      flood susceptibility identification is performed by applying the innovative 
      Multi-criteria decision-making model (MCDM) called Analytical Hierarchy Process 
      (AHP) by ensembles with Support vector machine (AHP-SVM) and Decision Tree 
      (AHP-DT). This model combines two Representation concentration pathway (RCP) 
      scenarios such as RCP 2.6 & RCP 8.5. The factors influencing the coastal flooding 
      in Bandar Abbas, Iran, identified through Flood susceptibility mapping. 
      Multi-criteria decision-making (MCDM) has been applied to evaluate the Coastal 
      flood conditioning factors, and ensemble machine learning (ML) approaches are 
      employed for Coastal risk factor (CRF) prediction and classification. The 
      statistical variances are measured through Friedman and Wilcoxon signed rank 
      tests and statistical metrics such as Accuracy, sensitivity, and specificity. 
      Among the models, AHP-DT obtained an improved AUC value of ROC as 0.95. After 
      applying the ML models, the northern and western park of Raidak Basin River 
      recognises very low and low flood susceptibility because of their topographic 
      characteristics. The eastern part of the middle section fell very high and high 
      CFSM. Observed from this result analysis, the people living nearer to the 
      coastline are distributed by the low to medium exposure in the region of the west 
      and middle of the considered study area. The results of this study can help 
      decision-makers take necessary risk reduction approaches in the high-risk 
      flooding zones of the coastal system.
CI  - Copyright (c) 2023 Elsevier Inc. All rights reserved.
FAU - Asiri, Mashael M
AU  - Asiri MM
AD  - Department of Computer Science, College of Science & Art at Mahayil, King Khalid 
      University, Saudi Arabia.
FAU - Aldehim, Ghadah
AU  - Aldehim G
AD  - Department of Information Systems, College of Computer and Information Sciences, 
      Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi 
      Arabia.
FAU - Alruwais, Nuha
AU  - Alruwais N
AD  - Department of Computer Science and Engineering, College of Applied Studies and 
      Community Services, King Saud University, Saudi Arabia, P.O.Box 22459, Riyadh, 
      11495, Saudi Arabia.
FAU - Allafi, Randa
AU  - Allafi R
AD  - Department of Computers and Information Technology, College of Sciences and Arts, 
      Northern Border University, Arar, Saudi Arabia.
FAU - Alzahrani, Ibrahim
AU  - Alzahrani I
AD  - Department of Computer Science, College of Computer Science and Engineering, Hafr 
      Al Batin University, Saudi Arabia.
FAU - Nouri, Amal M
AU  - Nouri AM
AD  - Department of Computer Science, Applied College, Imam Abdulrahman Bin Faisal 
      University, Dammam, 34212, Saudi Arabia.
FAU - Assiri, Mohammed
AU  - Assiri M
AD  - Department of Computer Science, College of Sciences and Humanities- Aflaj, Prince 
      Sattam Bin Abdulaziz University, Aflaj, 16273, Saudi Arabia. Electronic address: 
      m.assiri@psau.edu.sa.
FAU - Ahmed, Noura Abdelaziz
AU  - Ahmed NA
AD  - Department of Computer and Self Development, Preparatory Year Deanship, Prince 
      Sattam Bin Abdulaziz University, AlKharj, Saudi Arabia.
LA  - eng
PT  - Journal Article
DEP - 20231229
PL  - Netherlands
TA  - Environ Res
JT  - Environmental research
JID - 0147621
SB  - IM
MH  - Humans
MH  - *Floods
MH  - Risk Assessment
MH  - *Machine Learning
MH  - Iran
MH  - Risk Factors
OTO - NOTNLM
OT  - Analytical hierarchy process (AHP)
OT  - Coastal flooding
OT  - Decision tree
OT  - Flood hazard
OT  - Flood risk prediction
OT  - Flood susceptibility map
OT  - MCDM
OT  - Machine learning
OT  - Support vector machine (SVM)
COIS- Declaration of competing interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2024/01/02 11:46
MHDA- 2024/03/05 06:43
CRDT- 2023/12/31 19:18
PHST- 2023/07/14 00:00 [received]
PHST- 2023/12/16 00:00 [revised]
PHST- 2023/12/23 00:00 [accepted]
PHST- 2024/03/05 06:43 [medline]
PHST- 2024/01/02 11:46 [pubmed]
PHST- 2023/12/31 19:18 [entrez]
AID - S0013-9351(23)02846-3 [pii]
AID - 10.1016/j.envres.2023.118042 [doi]
PST - ppublish
SO  - Environ Res. 2024 Mar 15;245:118042. doi: 10.1016/j.envres.2023.118042. Epub 2023 
      Dec 29.