PMID- 37595460
OWN - NLM
STAT- MEDLINE
DCOM- 20230925
LR  - 20230925
IS  - 1095-8630 (Electronic)
IS  - 0301-4797 (Linking)
VI  - 345
DP  - 2023 Nov 1
TI  - Novel integrated modelling based on multiplicative long short-term memory (mLSTM) 
      deep learning model and ensemble multi-criteria decision making (MCDM) models for 
      mapping flood risk.
PG  - 118838
LID - S0301-4797(23)01626-2 [pii]
LID - 10.1016/j.jenvman.2023.118838 [doi]
AB  - Flood risk assessment is a key step in flood management and mitigation, and flood 
      risk maps provide a quantitative measure of flood risk. Therefore, integration of 
      deep learning - an updated version of machine learning techniques - and 
      multi-criteria decision making (MCDM) models can generate high-resolution flood 
      risk maps. In this study, a novel integrated approach has been developed based on 
      multiplicative long short-term memory (mLSTM) deep learning models and an MCDM 
      ensemble model to map flood risk in the Minab-Shamil plain, southern Iran. A 
      flood hazard map generated by the mLSTM model is based on nine critical features 
      selected by GrootCV (distance to the river, vegetation cover, variables extracted 
      from DEM (digital elevation model) and river density) and a flood inventory map 
      (70% and 30% data were randomly selected as training and test datasets, 
      respectively). The values of all criteria used to assess model accuracy 
      performance (except Cohens kappa for train dataset = 86, and for test 
      dataset = 84) achieved values greater than 90, which indicates that the mLSTM 
      model performed very well for the generation of a spatial flood hazard map. 
      According to the spatial flood hazard map produced by mLSTM, the very low, low, 
      moderate, high and very high classes cover 26%, 35.3%, 20.5%, 11.2% and 7% of the 
      total area, respectively. Flood vulnerability maps were produced by the 
      combinative distance-based assessment (CODAS), the evaluation based on distance 
      from average solution (EDAS), and the multi-objective optimization on the basis 
      of simple ratio analysis (MOOSRA), and then validated by Spearman's rank 
      correlation coefficients (SRC). Based on the SRC, the three models CODAS, EDAS, 
      and MOOSRA showed high-ranking correlations with each other, and all three models 
      were then used in the ensemble process. According to the CODAS-EDAS-MOOSRA 
      ensemble model, 21.5%, 34.2%, 23.7%, 13%, and 7.6% of the total area were 
      classified as having a very low to very high flood vulnerability, respectively. 
      Finally, a flood risk map was generated by the combination of flood hazard and 
      vulnerability maps produced by the mLSTM and MCDM ensemble model. According to 
      the flood risk map, 27.4%, 34.3%, 14.8%, 15.7%, and 7.8% of the total area were 
      classified as having a very low, low, moderate, high, and very high flood risk, 
      respectively. Overall, the integration of mLSTM and the MCDM ensemble is a 
      promising tool for generating precise flood risk maps and provides a useful 
      reference for flood risk management.
CI  - Copyright (c) 2023 Elsevier Ltd. All rights reserved.
FAU - Mohammadifar, Aliakbar
AU  - Mohammadifar A
AD  - Department of Natural Resources Engineering, University of Hormozgan, 
      Bandar-Abbas, Hormozgan, Iran.
FAU - Gholami, Hamid
AU  - Gholami H
AD  - Department of Natural Resources Engineering, University of Hormozgan, 
      Bandar-Abbas, Hormozgan, Iran. Electronic address: hgholami@hormozgan.ac.ir.
FAU - Golzari, Shahram
AU  - Golzari S
AD  - Department of Electrical and Computer Engineering, University of Hormozgan, 
      Bandar-Abbas, Hormozgan, Iran; Deep Learning Research Group, University of 
      Hormozgan, Bandar-Abbas, Hormozgan, Iran.
LA  - eng
PT  - Journal Article
DEP - 20230816
PL  - England
TA  - J Environ Manage
JT  - Journal of environmental management
JID - 0401664
SB  - IM
MH  - *Floods
MH  - *Deep Learning
MH  - Memory, Short-Term
MH  - Risk Assessment
MH  - Decision Making
OTO - NOTNLM
OT  - CODAS-EDAS-MOOSRA ensemble
OT  - Deep learning
OT  - Feature selection
OT  - Flood risk map
OT  - Iran
COIS- Declaration of competing interest The authors declare no competing interests.
EDAT- 2023/08/19 11:41
MHDA- 2023/09/25 06:42
CRDT- 2023/08/18 18:07
PHST- 2023/06/23 00:00 [received]
PHST- 2023/07/30 00:00 [revised]
PHST- 2023/08/14 00:00 [accepted]
PHST- 2023/09/25 06:42 [medline]
PHST- 2023/08/19 11:41 [pubmed]
PHST- 2023/08/18 18:07 [entrez]
AID - S0301-4797(23)01626-2 [pii]
AID - 10.1016/j.jenvman.2023.118838 [doi]
PST - ppublish
SO  - J Environ Manage. 2023 Nov 1;345:118838. doi: 10.1016/j.jenvman.2023.118838. Epub 
      2023 Aug 16.