An assessment of flash flood susceptibility in Golestan province, Iran, using multiple computational approaches

被引:2
|
作者
Sabzevari, Sayed Arash Hosseini [1 ]
Mehdipour, Haleh [2 ]
Aslani, Fereshteh [1 ]
机构
[1] Shahid Beheshti Univ, Dept Architecture & Urban Planning, Tehran, Iran
[2] Univ Florida, ME Rinker Sr Sch Construct Management, Gainesville, FL 32611 USA
关键词
Flash flood; Golestan province; Climate change; Spatial analysis; Hybrid computational approach; LAND-USE CHANGE; CLIMATE-CHANGE; PREDICTION; IMPACT; MODEL; RISK;
D O I
10.1108/IJDRBE-02-2023-0018
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
PurposeGolestan province in the northern part of Iran has been affected by devastating floods. There has been a significant change in the pattern of rainfall in Golestan province based on an analysis of the seven heaviest rainfall events in recent decades. Climate change appears to be a significant contributing factor to destructive floods. Thus, this paper aims to assess the susceptibility of this area to flash floods in case of heavy downpours.Design/methodology/approachThis paper uses a variety of computational approaches. Following the collection of data, spatial analyses have been conducted and validated. The layers of information are then weighted, and a final risk map is created. Fuzzy analytical hierarchy process, geographic information system and frequency ratio have been used for data analysis. In the final step, a flood risk map is prepared and discussed.FindingsDue to the complex interaction between thermal fluctuations and precipitation, the situation in the area is further complicated by climate change and the variations in its patterns and intensities. According to the study results, coastal areas of the Caspian Sea, the Gorganrood Basin and the southern regions of the province are predicted to experience flash floods in the future. The research criteria are generalizable and can be used for decision-making in areas exposed to flash flood risk.Originality/valueThe unique feature of this paper is that it evaluates flash flood risks and predicts flood-prone areas in the northern part of Iran. Furthermore, some interventions (e.g. remapping land use and urban zoning) are provided based on the socioeconomic characteristics of the region to reduce flood risk. Based on the generated risk map, a practical suggestion would be to install and operate an integrated rapid flood warning system in high-risk zones.
引用
收藏
页码:341 / 356
页数:16
相关论文
共 50 条
  • [41] Flood susceptibility mapping of Cheongju, South Korea based on the integration of environmental factors using various machine learning approaches
    Widya, Liadira Kusuma
    Rezaie, Fatemeh
    Lee, Woojin
    Lee, Chang-Wook
    Nurwatik, Nurwatik
    Lee, Saro
    JOURNAL OF ENVIRONMENTAL MANAGEMENT, 2024, 364
  • [42] Predicting the Distribution of Phlebotomus papatasi (Diptera: Psychodidae), the Primary Vector of Zoonotic Cutaneous Leishmaniasis, in Golestan Province of Iran Using Ecological Niche Modeling: Comparison of MaxEnt and GARP Models
    Sofizadeh, Aioub
    Rassi, Yavar
    Vatandoost, Hassan
    Hanafi-Bojd, Ahmad Ali
    Mollalo, Abolfazl
    Rafizadeh, Sayena
    Akhavan, Amir Ahmad
    JOURNAL OF MEDICAL ENTOMOLOGY, 2017, 54 (02) : 312 - 320
  • [43] A unified framework for the assessment of multiple source urban flash flood hazard: the case study of Monza, Italy
    Galuppini, Giacomo
    Quintilliani, Claudia
    Arosio, Marcello
    Barbero, Giuseppe
    Ghilardi, Paolo
    Manenti, Sauro
    Petaccia, Gabriella
    Todeschini, Sara
    Ciaponi, Carlo
    Martina, Mario L., V
    Creaco, Enrico
    URBAN WATER JOURNAL, 2020, 17 (01) : 65 - 77
  • [44] Assessment of social vulnerability in areas exposed to multiple hazards: A case study of the Khuzestan Province, Iran
    Hejazi, Seyed Jafar
    Sharifi, Ayyoob
    Arvin, Mahmoud
    INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION, 2022, 78
  • [45] Flood susceptibility modeling of the Karnali river basin of Nepal using different machine learning approaches
    Duwal, Sunil
    Liu, Dedi
    Pradhan, Prachand Man
    GEOMATICS NATURAL HAZARDS & RISK, 2023, 14 (01)
  • [46] Flash Flood Susceptibility Modeling and Magnitude Index Using Machine Learning and Geohydrological Models: A Modified Hybrid Approach
    Elmahdy, Samy
    Ali, Tarig
    Mohamed, Mohamed
    REMOTE SENSING, 2020, 12 (17)
  • [47] Integrating Harris Hawks optimization and TensorFlow deep learning for flash flood susceptibility mapping using geospatial data
    Tinh, Le Duc
    Thao, Do Thi Phuong
    Bui, Dieu Tien
    Trong, Nguyen Gia
    EARTH SCIENCE INFORMATICS, 2024, 17 (04) : 3397 - 3412
  • [48] FLASH FLOOD PRONE AREA ASSESSMENT USING GEOMORPHOLOGICAL AND HYDRAULIC MODEL
    Maftei, C.
    Papatheodorou, K.
    JOURNAL OF ENVIRONMENTAL PROTECTION AND ECOLOGY, 2015, 16 (01): : 63 - 73
  • [49] Flash Flood Risk Assessment of the Great Kwa River Basin Using Analytical Hierarchy Process
    Ogarekpe, Nkpa Mba
    Nnaji, Chidozie Charles
    Ekpenyong, Maurice George
    WATER CONSERVATION SCIENCE AND ENGINEERING, 2022, 7 (04) : 599 - 611
  • [50] Flash flood susceptibility modeling using optimized deep learning method in the Uttarakhand Himalayas
    Rihan, Mohd
    Mallick, Javed
    Ansari, Intejar
    Islam, Md Rejaul
    Hang, Hoang Thi
    Shahfahad, Atiqur
    Rahman, Atiqur
    EARTH SCIENCE INFORMATICS, 2025, 18 (01)