PMID- 35338986
OWN - NLM
STAT- MEDLINE
DCOM- 20220418
LR  - 20220531
IS  - 1095-8630 (Electronic)
IS  - 0301-4797 (Linking)
VI  - 312
DP  - 2022 Jun 15
TI  - A hybrid novel framework for flood disaster risk control in developing countries 
      based on smart prediction systems and prioritized scenarios.
PG  - 114939
LID - S0301-4797(22)00512-6 [pii]
LID - 10.1016/j.jenvman.2022.114939 [doi]
AB  - A Decision Support System (DSS) is a highly efficient concept for managing 
      complex objects in nature or human-made phenomena. The main purpose of the 
      present study is related to designing and implementation of real-time monitoring, 
      prediction, and control system for flood disaster management as a DSS. Likewise, 
      the problem of statement in the research is correlated to implementation of a 
      system for different climates of Iran as a unique flood control system. For the 
      first time, this study coupled hydrological data mining, Machine Learning (ML), 
      and Multi-Criteria Decision Making (MCDM) as smart alarm and prevention systems. 
      Likewise, it created the platform for conditional management of floods in Iran's 
      different clusters of climates. According to the KMeans clustering system, which 
      determines homogeneity of the hydrology of a specific region, Iran's rainfall is 
      heterogeneous with 0.61 score, which is approved high efficiency of clustering in 
      a vast country such as Iran with four seasons and different climates. In 
      contrast, the relation of rainfall and flood disaster is evaluated by Nearest 
      Neighbors Classification (NNC), Stochastic Gradient Descent (SGD), Gaussian 
      Process Classifier (GPC), and Neural Network (NN) algorithms which have an 
      acceptable correlation coefficient with a mean of 0.7. The machine learning 
      outputs demonstrated that based on valid data existence problems in developing 
      countries, just with verified precipitation records, the flood disaster can be 
      estimated with high efficiency. In the following, Technique for Order of 
      Preference by Similarity to Ideal Solution (TOPSIS) method as a Game Theory (GT) 
      technique ranked the preventive flood damages strategies through three social (Se 
      1), environmental (Se 2), and economic (Se 3) crises scenarios. The solutions of 
      flood disaster management are collected from literature review, and the opinion 
      approves them of 9 senior experts who are retired from a high level of water 
      resource management positions of Iran. The outcomes of the TOPSIS method proved 
      that National announcement for public-institutional participation for rapid 
      response and funding (G1-2), Establishment of delay structures to increase flood 
      focus time to give the animals in the ecosystem the opportunity to escape to the 
      upstream points and to preserve the habitat (G 2-8), and Granting free national 
      financial resources by government agencies in order to rebuild sensitive 
      infrastructure such as railways, hospitals, schools, etc. to the provincial 
      treasury (G3-10) are selected as the best solution of flood management in Social, 
      Environmental, and Economic crises, respectively. Finally, the collected data are 
      categorized in Social, Environmental, and Economic aspects as three dimensions of 
      Sustainable Development Goals (SDGs) and ranked based on the opinion of 32 
      experts in the five provinces of present case studies.
CI  - Copyright (c) 2022 Elsevier Ltd. All rights reserved.
FAU - Akbarian, Hadi
AU  - Akbarian H
AD  - Department of Civil Engineering, Ferdowsi University of Mashhad, Mashhad, Iran. 
      Electronic address: haghot@gmail.com.
FAU - Gheibi, Mohammad
AU  - Gheibi M
AD  - Departamento de Ingenieria Industrial, Tecnologico de Monterrey, Puebla, Mexico. 
      Electronic address: mohamadgheibi@ymail.com.
FAU - Hajiaghaei-Keshteli, Mostafa
AU  - Hajiaghaei-Keshteli M
AD  - Departamento de Ingenieria Industrial, Tecnologico de Monterrey, Puebla, Mexico. 
      Electronic address: mostafahaji@tec.mx.
FAU - Rahmani, Mojtaba
AU  - Rahmani M
AD  - Department of Marine Engineering, Amirkabir University of Technology, Tehran, 
      Iran. Electronic address: Mojtaba.rkl@gmail.com.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20220323
PL  - England
TA  - J Environ Manage
JT  - Journal of environmental management
JID - 0401664
SB  - IM
MH  - Developing Countries
MH  - *Disasters/prevention & control
MH  - Ecosystem
MH  - *Floods
MH  - Hydrology
OTO - NOTNLM
OT  - Clustering
OT  - Decision support system
OT  - Flood control
OT  - Machine learning
OT  - Sustainable smart management
EDAT- 2022/03/27 06:00
MHDA- 2022/04/19 06:00
CRDT- 2022/03/26 20:09
PHST- 2021/11/19 00:00 [received]
PHST- 2022/03/10 00:00 [revised]
PHST- 2022/03/17 00:00 [accepted]
PHST- 2022/03/27 06:00 [pubmed]
PHST- 2022/04/19 06:00 [medline]
PHST- 2022/03/26 20:09 [entrez]
AID - S0301-4797(22)00512-6 [pii]
AID - 10.1016/j.jenvman.2022.114939 [doi]
PST - ppublish
SO  - J Environ Manage. 2022 Jun 15;312:114939. doi: 10.1016/j.jenvman.2022.114939. 
      Epub 2022 Mar 23.